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State of the Art Intrusion Detection System for Cloud Computing.

1. Introduction

There are problems with the implementation of Cloud computing systems that will be as a basis in this paper. Cloud computing is the new concept of computing where people only need to pay for services and resources without needed to place any cost for physical hardware. With the implementation of Cloud computing in the application today, it emerges a new technique in software development and deployment. It also changes how people are using and managing resources. Cloud computing can be defined as internet-based computing, where shared resource, software and information are provided to the user on demand [1].

Cloud computing systems are distributed and nesting a lot of resources and private information, therefore because of their nature, cloud computing environments are easy targets for intruders looking for possible vulnerabilities to exploit [2]. When organizations and companies which are using Cloud computing services, they will move their resources from their own private infrastructure to the Cloud infrastructure. If the Cloud is compromised, the organization's resource will be at risk. Cloud Computing systems need protection mechanisms that will monitor the network activity and detect if any intrusion attempts happen within the Cloud Computing infrastructure whether it was from external or internal sources [3]. In fact, the cheap availability of significant amounts of computational resources can be regarded as a means for easily perpetrating distributed attacks, as it has recently been observed in several security incidents involving Amazon's EC2 cloud infrastructure [4].

Cloud implementation introduces a new term, "co-residency" which means that multiple independent customer share the same physical infrastructure [5]. Hence, it was possible for different Cloud host residing in a single machine. There are several methods for neighbour discovery between Virtual Machine (VM) in a Cloud infrastructure; therefore, it is crucial to provide countermeasures for this type of attack. Ristenpart et al simulates the "co-residency" attack on Amazon EC2, which is one of the largest Cloud service providers [6]. Their works make use on freely available open source software. Inter VM's communication also become a big concern when we are implementing Cloud Computing system where communication between Clouds are not monitored and controlled. When implementing VM in the system layer of the Cloud, each Guest Operating system (OS) exposed to the risk of being attacked by other Guest OS either intentionally or accidentally. In a way to protect each Cloud, a new method is required to monitor inter VM's activity and detect if any abnormalities occur and at the same time to block the events from occurring [7].

In addition, there are lack of specific IDS built to protect Cloud computing systems [8]. Current implementation of IDS in the Cloud computing systems are still using the traditional way which installing traditional open source or enterprise IDS in the Cloud Computing server to protect the Cloud Computing systems. These traditional IDS implementation, such as on virtual machines (VM), which is considered more vulnerable with diverse security requirements [9]. Implementing traditional IDS need a lot of self-maintenance and did not scale with the customer security requirements. In addition, maintenance of traditional IDS in Cloud Computing system requires expertise and consume more time where not each Cloud user have [10, 11]. An attack against a cloud computing system can be silent for a network-based IDS deployed in its environment, because node communication is usually encrypted. Attacks can also be invisible to host-based IDSs, because cloud-specific attacks don't necessarily leave traces in a node's operating system, where the host-based IDS reside. In this way, traditional IDSs can't appropriately identify suspicious activities in a cloud environment.

Worse come to worst, a decentralized traditional IDS approach where being implemented in current Cloud computing infrastructure will make the IDS management become more complicated [12]. Cloud subscribers that concern about their data protection will install their own IDS and this action will isolate the Cloud Service Provider (CSP) from the IDS management realm and CSP will have no authority in managing each Cloud subscribers' local IDS. This approach also affects the bill that Cloud subscribers will pay if the IDS were installed on their own host because monitoring intrusions may consume excessive amount of hidden data transfer [13]. Furthermore, each of the subscriber' IDS will not be the same in term of type and configurations and each user may have the risk of outdated signatures. If any attack happens and the IDS signature was outdated, the IDS will not treat the event the same way as IDS with updated signature and some of the IDS may cause a false negative detection. This will bring risk not only to the Cloud subscriber itself but also to the other Cloud users and in worst case scenario, this attack also may affect CSP and the whole system.

Cloud Computing is a new implementation of computer technology and open a new research area and create a lot of opportunity of exploration. One of the new implementation in Cloud is Intrusion Detection System (IDS). Cloud Computing is a Model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing is based on five attributes: multi-tenancy (shared resources), massive scalability, elasticity, pay as you go and self-provisioning of resources.

The objective of this study is to characterise and provide a comprehensive review on the state of the art research in Cloud IDS. To the author's knowledge very few comprehensive studies on Cloud IDS have been conducted. For this purpose, a comprehensive review is conducted within this paper to discuss the state of the art research on Cloud IDS.

2. Motivation: the need for Cloud Intrusion Detection System

With respect to Cloud computing, to the best of our knowledge, we are the first to identify the intrusion detection problem for such systems based on artificial immune system (AIS). However, there have been efforts in traditional systems to address this problem. An obvious example of such systems is network intrusion detection system (NIDS) where an intrusion detection system is usually deployed at a border node to look after a whole network of computers. As part of the network, different sub-domains or clusters can have varying security requirements [14]. This NIDS implementation introduce a new Virtual Machine (VM) level threats in cloud computing because the existence of numerous VMs owned by different Cloud users significantly increases attack surface for cross-VM (or VM-to-VM) attacks [15].

Traditional IDS need a lot of self-maintenance and did not scale with the customer security requirements. In addition, maintenance of traditional IDS requires expertise and consume more time that normal company did not have [10, 11]. The cost of maintaining and installing the traditional IDS is also a big consideration in implementing IDS in an organization. In addition, a decentralized traditional IDS approach where being implemented in traditional IDS can increase the network vulnerabilities in the protected system when the IDS system is deployed and implemented together in the same network and made visible to others. The IDS itself are exposed to the internal attacks where attacker from the same network will have access to the IDS and launching attack directly towards the IDS. The IDS must be isolated and invisible from the same network where the host and servers reside [11].

To protect computing infrastructures which contains valuable assets from cyber-attacks, most enterprises set their strategy to deploy their IDS on dedicated hardware. However, such strategy is no longer effective today when small and medium enterprises (SMEs) are conveniently tapping into the Cloud environment which provides them the platform, infrastructure and software as services on a pay-per-use basis [9]. Moreover, IDS is commonly deployed in the traditional way, such as on virtual machines (VM), which is considered more vulnerable with diverse security requirements. In the traditional deployment, the benefits of customization and on-demand operations offered by Cloud are contradicted by the lengthy intrusion response time and thus affecting the overall security of the system [10].

2.1 Research on Cloud Computing Intrusion Detection System

This paper reviews the research related to the detection of intrusions for Cloud Computing in particular. Therefore, the existing literature in these domains has been explored to draw a comparative analysis of the proposed approach with the related works in Cloud Intrusion Detection. Fig.1 shows the scattered plot of the research that being investigated within this review. The plot was marked with numbers which indicated the reference number at the end of this paper.

2.1.1 Virtual Machine Introspection (VMI-based) and Grid Cloud IDS

The research related to Cloud IDS started since 2003 where Garfinkel.T and Rosenblum.M proposed the virtual machine introspection based architecture for intrusion detection [16]. At that time, normal implementation of cloud infrastructures includes the deployment of virtual machine monitors (VMM). Hence, this research also related to the research based on VMM. Virtual machine introspection (VMI) is the process of monitoring and inspecting a virtual machine from the outside for the purpose of analysing the software running inside it [17]. As explained in their paper, VMI IDS will monitor cloud client directly by using command namely Inspection Command to monitor VM state, Monitor Commands to monitor VM event and Administrative Commands to control the execution of a VM. This host-based IDS (HIDS) make a decision based on the Operating System (OS) Interface Library and Policy Engine. The prototype, Livewire will suspend the execution of the VM until the administrator responds to that event. This procedure may bring a concern when the VM operation suspended and user is unable to access the VM. This may affect the availability of the Cloud guest system. The experiment conducted using custom attacks that launch at the monitored host. The results show that Livewire are able to detect all the attacks. From the author point of view, this implementation is successful but may need some improvement in term of resource management especially if this system is implemented in cloud environment.

A few years later Roschke et al proposed a VM compatible IDS architecture in their paper Intrusion Detection in the Cloud [18]. This research provides a solution to protect Cloud environment by placing the IDS at each VM nodes. The NIDS rely on the detection of the signature-based IDS that analysed and detect any intrusions targeting the Cloud. At the same time, AV. Dastjerdi et al proposed mobile agent implementations in Cloud computing environment where the IDS located at every VM host [19]. The proposed system based on anomaly detection engine where it provides IDS for Cloud application regardless by their location. The issue with their proposed system is the implementation will produce network load with the increase of VMs attached to mobile agent.

K. Viera et al also proposed a solution in protecting Cloud in general but more focused on Grid computing implementation [3]. The proposed hybrid system integrates knowledge and behaviour analysis together to detect specific intrusions and placed on each node of the grid system. Each IDS node cooperatively takes part in intrusion detections and once any attacks were detected, they will broadcast the alert among them using middleware communication mechanism. The experiments were conducted with a prototype that they developed to evaluate the proposed architecture using Grid-M [20]. Grid-M is a middleware for simulating mobile device, sensor and grid computing developed by a research group at the Federal University of Santa Catarina. The datasets were generated from audit elements coming from both the log system and from data captured during node communications. The results show that the proposed system generates low number of false positives, but after several repetitions, the quantity of false positives varied and uncertain. However, this system provides low processing cost while still providing a satisfactory performance for real-time implementation.

Work by S Gupta et al provided a solution if detecting insider attacks or inter-VM attacks by detecting malicious system call sequences of well-known program [21]. Their proposed system will grab the fingerprint of the well-known program and monitor the system call activities during execution. The detections are based on the stored system call array and if the execution did not execute the next expected system call, the event will be considered malicious. From our point of view, developing the system calls fingerprint is a time-consuming task and the author still using manual technique which is not suitable in Cloud environment.

The recent work from P. Mishra et al proposed Cloud IDS based on VMI which having two major detection components; Traffic Monitoring for performing traffic analysis in detecting network based attack and Process Monitoring which does the process-monitoring at VMM-level without affecting any operations running on VMs [22]. They use three different datasets for experiment and the results shows a great performance result where 98.8% detection accuracy for UOC dataset [23], 98.9% accuracy for CAIDA [24] and 95.43% accuracy for UNSW-NB dataset [25]. Table 1 summarizes cloud IDS research on VMM technology that reviewed in this sub-section.

2.1.2 Distributed, Collaborative and Agent based Cloud IDS

Distributed Cloud IDS introduced by Irfan Gul and M. Hussain make use of multi-threading agent in monitoring and handling large amount of information in Cloud environment [26]. The agents then send the monitoring information to the third-party monitoring service for analysis and reporting any incidents. Their proposed system was tested with custom Denial of Service (DoS) attack targeting the Cloud network and the results show that the multi-threading system provides nearly 50% improvement of processing time compared to single-threading system. The same year, Cloud IDS described by ST. Zargar et al covers protection for two service level; Infrastructure level which provides a comprehensive hybrid Intrusion Detection and Prevention (IDP) system and virtual level where users are provided with software level IDP [27]. Within infrastructure level, the system cooperates with each other to create a comprehensive global version of all the clusters' databases on global infrastructure layer that is shared and used for detection and prevention among different cloud providers.

Some of the solutions focused on developing adaptive and distributed Cloud IDS. D Krishnan and M Chatterjee suggest that Cloud IDS can perform very well if the Cloud IDS can adapt to the environment and at the same time communicate with other IDS in different Cloud locations [28]. Their hybrid IDS which contains both anomaly and behaviour based detection will monitor Cloud environment and if any intrusions event occur, the IDS will distribute the alert to other IDS nodes so that each nodes update with the situations. IDS detection using behaviour based will monitor the current behaviour of the Cloud environment like number of users connected, log in time of users, bandwidth utilization, incoming and outgoing traffic flow, various connected ports, protocols and IP addresses used in incoming received requests and the detection engine will report if any deviation occurs compared to the recorded normal behaviour profile.

Nguyen Doan Man and Eui-Nam Huh proposed a collaborative intrusion detection system framework for protecting cloud computing environments. Their work consists of three main components; IDS Manager, which resides at the management region of a Collaborative Cloud, IDS Dispatcher, which is built inside each Cloud region, and Elementary Detector, which is distributed to monitor each VM and generates alarms for any detected intrusions [30]. The author did not provide any experiments and results from this research.

A similar work from P. Ghosh et al improves the model by generating Virtualised "Mini-IDS" for each Cloud host besides provides multi-threading monitoring and third party analysis service [29]. A mini IDS will be assigned for a specific Cloud host each time a new host is generated. Each instance supervises on each user activities and sends a report of all the activities to the IDS Controller via cloud NIDS after the end of each session. The proposed system will be able to handle large network traffic from Cloud users and provides security at the same time. The work of K.M.M. Vieira et al proposed a new approach to combine autonomous system and machine learning [30]. In their work, the autonomous system is following Monitor-Analyse-Plan-Knowledge (MAPE-K) cycle composed by the monitoring, analysis, planning and executing modules. They used sensors for collecting data from IDS logs, network traffic, system logs, and data communication. They also make use of Apache Hadoop for storage and analytics.

I. Jeganathan and A. Prakasam proposed agent-based Cloud intrusion detection system that monitoring and analysing clients' network packet at the same time filter the illegal user from accessing the cloud environment [31]. The agents send intrusion alert to a monitoring service for further action. Their proposed system will be placed outside the cloud environment where there are heavy network traffics such as switch, router or gateway for network traffic monitoring to have a global view of the system.

Araujo et al. proposed an elastic and internal Cloud-based detection system (EICIDS). This type of IDS is based on protection of virtual machines against internal users who can use some VMs to perform malicious activities which is part of our research [32]. Monitoring of virtual machines is done by IDS sensors dispersed in the cloud environment, and the instantiation of these sensors is made in each VM, where the packets passing in VMs are captured and subsequently analysed for the identification of threats. Thus, the entire virtual environment is monitored, while the remaining components of EICIDS reside outside the virtual environment are thus protected from possible attacks by compromised VMs. However, the architecture of EICIDS is centralised IDS admin and there is no signature generation system. Even this proposed system did not provide protection for outsider attacks.

In their research, Al Haddad et al proposed a collaborative network intrusion detection system for cloud computing environment. Their work proposed that the NIDS should be placed at the front end as well as at the back end on Virtual Machine Monitor (VMM) of a cloud network environment [33]. This hybrid-based NIDS consists of packet sniffing module, signature-based detection, anomaly detection, alert system and central log database. From author point of view, placing multiple NIDS inside a cloud network environment will result a complex configuration of a set of NIDS and the monitoring process will consume more resources than what we can imagine. The researcher still in the experiment phase and so far, no experimental results were provided.

Singh et al proposed a novel Collaborative IDS Framework for Cloud computing environment [34]. This framework integrates Snort to detect the known attacks using signature matching. To detect unknown attacks, anomaly detection system (ADS) is built using Decision Tree Classifier and Support Vector Machine (SVM). Alert Correlation and automatic signature generation reduce the impact of DoS and Distributed Denial of Service (DDoS) attacks and increase the performance and accuracy of IDS. However, it requires a high training time. Table 2 lists and analyses the distributive, collaborative and agent-based cloud IDS research based on their type, method, positioning, dataset, advantages and also their limitations.

2.1.3 IDS as a Service

There are some researchers provide different perspectives of Cloud IDS such as providing IDS as a Service. The work of Wang et al distributes IDS into local and remote components which local components collect and analyse logs in the client location while remote component focused on detecting intrusions based on the analysis from the local components [35]. W. Yassin et al suggests an alternative in intrusion detection where a framework of IDS based on Cloud was proposed [11]. The cloud-based IDS make use the Cloud technology for managing and detecting intrusions targeting a normal network environment. In detail, the IDS components such as User Database, Signature Database and Analysis Engine located in a Cloud environment, where the users' traffic are directed to the IDS through a secured Virtual Private Network (VPN) connection. The concerns about this implementation are internal attack may become invisible from the view of IDS and the VPN network is a single point of failure where any disruption of the connection will bring down the whole system. In addition, the increasing number of clients will increase the workload of the IDS Cloud. The same idea also shared in Hamad H. and Al-Hoby work on managing IDS as a service [36]. Their work tries to replace the traditional local server-based network environments with a cloud-based network infrastructure that monitor the network traffic and analyse for any intrusion attempt. This mechanism is offered to the end user as a service. In the proposed work, they utilise the huge performance of Cloud infrastructure for fast and efficient search algorithms. The proposed system able to receive the subscriptions requests from the cloud users and translates these requests into a standardised signature that can be deployed and utilised as the Cloud Intrusion Detection Service (CIDS). An experiment was conducted by using 500 signature definitions and the detection rate form the proposed system less than the traditional implementations. However, this system shows an improvement in term of detection overhead, compared to the traditional IDS implementation.

Still in the context of Cloud-based IDS perspective, one of the concerns in this type of implementation is the IDS became the victim of the attack itself. By considering this issue, HA Kholidy and F Baiardi proposed Cloud-based Intrusion Detection system (CIDS) which applied scalable and elastic architecture with point-to-point (P2P) solution and no central coordinator, which means they try to eliminate single point of failure [37]. Their proposed system distributes the workload of a single IDS into several cloud locations and isolates the task using monitored VM. This hybrid IDS claim that it can detect the masqueraders that access from several nodes and both host-based and network-based attacks because they are monitoring any intrusion in the middleware layer of a cloud. Based on CIDS, the same author proposed the enhanced version of their works named Hierarchical and Autonomous IDS for Cloud Systems (HA-CIDS) [38]. In this model, their IDS not only detect masquerades attack, but the detection also covers DDoS, host based and network-based attack with the inclusion of OSSEC HIDS, Snort NIDS and integrity checker within their detection mechanism. The detection mechanism is not distributed as previous model but located in a single cloud node.

The recent work by Azuan et al provides a Danger Theory Model Cloud-based IDS. This is the only work that using Artificial Immune System (AIS) for Cloud-based IDS, namely SaaSIDS [39]. SaaSIDS is a Cloud host that receive network traffic and analysing them for intrusion detections via hybrid engine which provides signature-based and anomaly detections. The system consists of three major components; SaaSIDS sensor, a sensor that installed on users' network and forward the packet through VPN to the SaaSIDS cloud. The other component is SaaSIDS Service Component which filter network traffic and pre-processing them before analyse it is using Hybrid Detection Engine for intrusion detections.

The problem of traffic bottlenecking is the main issue for IDS as a Service model. The work of S Garg and K Kaur provides a solution for this problem by maintaining a group of IDS as a Service and load balancing them to support a heavy traffic of clients' network data [26]. The signature-based IDS use Snort NIDS for intrusion detection. From the experiment, their proposed model able to eliminate single point of failure by introducing concept of multiple IDS in cloud environment and increase availability of IDS service for cloud users by balancing load between IDSs.

Recent work from SM Alqahtani et al proposed a Cloud-based IDS for detecting DDoS attack [36]. They proposed that the IDS placed in a separate VM and dedicated for analysing intrusion data. Another VM was created for monitoring and sending suspicious packet to the IDS. From the experiment, the processing required for detecting ICMP Flood attack is 39-40%. Their system is able to provide promising performance improvement compared to traditional method.

Single point of failure is the main concern of IDS as a Service architecture. The work of M. Mbaye and C. Ba proposed distributed Cloud-based IDS with the Publish and Subscribe paradigm [41]. They considered client as a Publisher which sent intrusion data to the IDS while the IDS as a Subscriber which receive the intrusion data together with the taxonomy of the intrusion. The distributed IDS will receive the data based on the taxonomy which each IDS will support different kind of taxonomy. In a simple view, table 3 describes Cloud IDS as a Service research in tabular form.

2.1.4 Open Source Cloud IDS

Open source software is the best cost-effective solutions in solving real world problems and at the same time did not sacrifice the performance. Mazzariello et al in their research provides a solution for detecting intrusions by using network intrusion detection system (NIDS) for Open Source Cloud Computing environment [42]. In the paper, they provide a review on eucalyptus, an open source cloud management system and deploying a signature-based IDS inside the eucalyptus environment. They try to prove that a carefully deployed traditional solution could mitigate a severe problem in cloud computing environment. In the implementation, they install IDS at the frontend of the cloud environment. Such implementation has a flaw where it tends to make inter-cloud attacks invisible from detection. The author did not provide any IDS performance result based on detection rate.

B Borisaniya et al proposed a solution in Cloud IDS by implementing open source technology in developing their prototype [43]. They choose to use Eucalyptus for the Cloud framework and Snort as the NIDS that located in each Cluster Controller (CC) to monitor network traffic. From their perspective, this approach allows the Cloud administrator to monitor the type and source of the attack, which in turn can be used to prevent the similar future attacks. They employ Honeypot in detecting unknown attacks since honeypot will monitor for suspicious behaviour. The experiment shows that the proposed system able to detect medium and low level of security threats and still need some improvement in detecting high level of security threat.

HM Alsafi and WM Abduallah proposed a Cloud IDS that focused on Infrastructure as a Service (IaaS) layer of Cloud environment and implementing hybrid detection mechanism which combine anomaly and signature based detection [44]. Further than just detect and reporting intrusion, their proposed system includes the protection mechanism which terminates the user session that is being used for the attack, block access to the target from the offending source and block all access to the targeted host, service, application, or other resource.

More recent work that take the benefits of honeypot in protecting Cloud environment also been done by AA Thu which [45]. The proposed system placed honeypot within the Cloud environment to monitor any internal attacks that targeted the other Cloud host. The honeypot will work closely with IDPS in protecting the Cloud infrastructure. From the testing, the result shows that IDPS system with honeypot works better than traditional implementation of IDPS.

The work of T Xing et al also implemented open source technology in their proposed system, SnortFlow [46]. SnortFlow is the combination of Snort, an open source NIDS and OpenFlow, communications protocol that gives access to the forwarding plane of a network switch or router over the network. The proposed system has multiple module including SnortFlow Server which evaluates the network security status and generates actions to be pushed into the controller, SnortFlow daemon for collecting alert data from Snort agent in Dom 0 and Alert Interpreter, takes care of parsing the alert and targets the suspect traffic. The experiments are conducted to find the best place for implementing SnortFlow. From the result, the SnortFlow perform more efficient when implemented in the Domain 0. The work from T Kuldeep et al also proposed the usage of Snort NIDS in their implementations. They proposed the location of Snort NIDS is behind the gateway which monitors all the traffic going through the network gateway [47]. JK Khatri and G Khilari on the other hand proposed the Cloud IDS by using Kernel-based Virtual Machine (KVM) and Suricata NIDS [48]. Both open source applications were installed on Ubuntu Linux. Recent work from Priyanka Sharma et al provides Cloud environment using VirtualBox open source virtualisation software and OpenStack Cloud environment framework [49]. They launched DoS attack targeting their Cloud and the result shows that OpenStack take longer time to load an image compared during normal situation.

There is an implementation where multiple open source applications are combine and work closely in protecting Cloud from threats. The work from C Ambikavathi and SK Srivatsa make use OpenNebula as the Cloud computing platform, Snort as the signature -based NIDS, Bro also as signature-based NIDS and OSSEC open source signature-based HIDS [50]. The aim of the author is to provide total protection mechanisms by implementing all the available open source applications. The same work also proposed by W. Muche where they integrate the use of Snort for NIDS, OSSEC for HIDS and expand the capability of anomaly detection by using Naive Bayes Tree (NBTree) classifier [51]. By using KDD'99 dataset, their work was able to achieve 95.5% detection. Table 4 listed the Cloud IDS research based on open source approaches.

2.1.5 Machine Learning Cloud IDS

Make use data mining and machine learning algorithm for classifying intrusions is also being adapted by several researchers. G. Sathya and K Vasanthraj proposed a work based on data mining technique [37]. In their work make use of C4.5 algorithm in detecting anomaly attacks. In their multi-level IDS for Cloud computing, they monitored network traffic and identified the attack at the first level. The second level classifies the attack into four categories and the third level identifies the type of the attack. More recent work by M Moorthy and M Rajeswari focused on using Genetic Algorithm (GA) in generating new rules in their proposed cloud Intrusion detection system [53]. Their work generate the network profile based on CIDD [37] by analysing real-traces in the dataset. Their proposed system can achieve 80% of True Positive Rate (TPR) during the experiment.

Machine learning algorithm also implemented in the work of MN Ismail et al. They proposed an IDS for detecting flooding based DoS attack targeting Cloud network by using covariance matrix approach [54]. The experiment is done in two phases; Training phase where the IDS were run with the normal network traffic and Testing phase where the IDS were run with the abnormal network traffic which contains flooding attacks. From the experiment they prove that there is a huge different between normal network traffic and network traffic under flooding-based DoS attack by using covariance matrix.

The latest work from HA. Kholidy et al implemented finite state Hidden Markov Model (HMM) in predicting multi stage attack in Cloud computing environments [55]. They include HMM as an improvement from their previous work in [38] for predicting and provides early warning for any future attacks. Their proposed system works by tracking the evolution of an attack while it is in progress, the state changes and the system can trigger appropriate responses based on a confidence level threshold, which would result in a lower false positive rate. The proposed prediction model has successfully fired the early warning alerts before the launching of the LLDDoS1.0 attack by 39 minutes plus 37 seconds and 64 minutes plus 42 seconds before the detection phase starts.

J. Arshad et al. proposed an intrusion severity analysis for cloud computing where in their research, they used the hybrid approach where the attacks were detected based on the attack database that they provide and from the Profile Engine (PE) which is based on the behaviour of the monitored virtual machines (VM). This machine learning based IDS using classification technique for intrusion severity analysis from the monitored system calls. The dataset used in this research is the artificial data generated from the computer program. This dataset did not provide the real cloud environment and did not represent the actual response of a cloud environment towards any attack [14]. The results obtained from the research successfully demonstrate the effectiveness of the intrusion severity analysis method for Clouds but the dataset and may be questionable because the research used the self-generated dataset and they did not provide details about the methodology in building their datasets.

In the same year, work from U Hameed et al using Genetic Algorithm (GA) in detecting intrusions in Cloud computing environment especially in SaaS model [56]. Their proposed genetic algorithm engine analysed data and matched the data with the signature database in the detection engine. This system checks the intrusions by matching the signature in knowledge base. If the location is not matched with any existing details in the knowledge base it provides an exit from the system. If existence check is passed it analyse and matches the data for the proper recognition based on the fitness function X=[delta]X0. If the fitness is proven, then different authentication checks are implemented to reach the result at each step it applies genetic algorithm. If the desired record is not matched it reproduces the new record by mutation, performs fitness test X=[delta]X0 and rechecks from the data in knowledge base. Same method will be repeated for three generations as per termination criteria.

The same algorithm also sparks the inspiration for P Singh and B Hazela to propose a Cloud IDS model based on GA [57]. GAs aim is to produce the best solutions by the evolution of a sample set of potential solutions [58]. Inside the sample set, solutions that are poor tend to die out while the better solutions mate and propagate their advantageous traits, thus introducing more solutions into the set that boast better potential. Their proposed work is the mix of Cloud Intrusion Detection System and two types of chromosomes based on different criteria which is based on the job length and bandwidth utilisation of the resources. Based on the selected criteria, the system performs the crossover operation with the aid of neural network. The result will be the best chromosome of the first generation. From the experiment, the proposed model able to detect 57% of random sets of cloud attacks.

JK Seth and S Chandra come with a proposal that provides Cloud protection based on the user risk level [59]. They believe that every cloud user did not require the same security level of protection. In their proposed work, they classify the user risk level into four categories; normal, low, medium and high, based upon stored rules and pattern and this process will use Artificial Neural Network (ANN) classification algorithm. The repository will update the rules on occurrence of new pattern and train the classifier at the time of minimal load on cloud. The proposed model increases resource availability and optimise IDS resource utilisation.

Deshpande et al in their research proposed a HIDS for cloud computing environment based on system call trace analysis where only the failed system call were used to predict the intrusion [60]. The proposed system makes use of k-nearest neighbour (kNN) algorithm for comparing the current information with the available database. The prototype developed based on four main modules; Data Logging Module, Pre-processing Module, Analysis and decision engine and Management Module. Based on the paper, the experiment was conducted using a list of self-generated system calls based on Linux API. The result has been estimated using three different real-time datasets, with a time window of 30 and 60 s. From the paper the prototype can achieve the accuracy with a high sensitivity of 96%.

As discussed in the previous section, "co-residency attack" is a current threat in Cloud computing environment. N Pitropakis and C Lambrinoudakis support this statement by providing a solution for detecting this type of attack by using Smith-Waterman algorithm [61]. In their latest work, they make use of Graphical Processing Unit (GPU) processing in supporting the huge and complex calculation related to Cloud. GPU processing may improve the overhead processing of Cloud IDS in analysing and detecting intrusion from a large amount of data. From the experiment, they found that using GPU parallel processing achieved six times faster performance compared to normal Central Processing Unit (CPU) processing.

In computer networking, port mirroring is the method to duplicate all port in the network device towards a single port. NS Aljurayban and A Emam implement port mirroring method in collecting network traffic in the Cloud environment and analyse the traffic using Artificial Neural Network (ANN) algorithm [62]. From the experiment they got great result of sensitivity with a good result in accuracy. Extending the work of previous research, GK Chaturvedi et al combine Fuzzy-Clustering with Artificial Neural Network in providing a better intrusion detection model for Cloud Computing [63]. The dataset is divided into two groups which are training and testing. Their proposed work divides the training data into several subsets using fuzzy clustering technique while at the same time trains the different ANN using different subsets. Then their model will determine membership grades of these subsets and combines them via a new ANN to get final results.

In their paper, Kwon.H et al proposed a self-similarity based lightweight intrusion detection method as a solution for protecting cloud environment. They used Cosine Similarity Based Self-similarity as a detection algorithm and during the experiment, they monitored Windows event log from the DARPA 1999 datasets for any intrusion based on Security ID (SID) and EventID [64]. In this experiment, the overall false-positive ratio was 4.17%. They claim that their IDS can work robustly even though the Windows event log does not include enough information regarding security rather than the other operating system's audit log.

Recently, game theory has been used extensively to model and analyse network security issues. Game theory is a machine learning approach that provides a set analytical and mathematical framework to model rational decision-making or strategies of multiple players having different objectives interacting with each other [65]. The proposed work from Z. Li et al provides a protection for Cloud from intrusions by using differential game model [66]. The simulation results show the changes in the probability under the optimal strategies condition. Using the game theoretic framework, dynamic optimal strategies can improve the security of cloud computing system and energy consumption in the proposed optimal strategy is significantly lower than the static strategy. Artificial Immune System (AIS) is one of the solutions that try to mimic our body protection against pathogen. This is because our body protection system always accurate and less prone to fails and this is become the main reason why AIS will provide the same result. One of AIS method, namely Dendritic Cell Algorithm (DCA) was inspired by one of our body cell type that monitored the body tissue for any pathogen [67]. This algorithm activated by multiple signals for calculating the maturity of the monitored environment where the matured Dendritic Cell (DC) indicates the malicious level of the environment and on the other hand, semi-mature DC indicates the normal environment. The work from Azuan et al, using DCA for detecting intrusions attempt that targeting Cloud environment [68]. Their integrated system monitored Cloud infrastructure from the agents that installed on each Cloud VM and from virtual switch level where the "co-residency" attack could be detected. Each agent and virtual switch collect numbers of specific data recognised as signals value in DCA. The signals value then being normalised and analysed for the results between mature or semi-mature conditions. Table 5 provides a summary of Cloud IDS research in machine learning approaches.

3. Challenges

Based on the related literature, we found that the following issues have not been sufficiently solved. These are gaps in the reviewed work that would prove to be directions for future works. Table 6 shows a clear gap analysis that Cloud IDS research should fill especially in the following issues:

* Providing real Cloud IDS implementation: In identifying the performance and workability of a proposed Cloud IDS, prototypes need to be developed and tested in a physical Cloud computing environment. Most of the research provides solution and results based on simulation and static data analysis. Even some research that claim to be real implementation still provide analysis results based on static dataset and this is not really represents real Cloud IDS implementation. The real Cloud IDS must be developed based on a working prototype that monitoring Cloud network traffic or system calls or both which is termed as hybrid. Other research especially that implemented open source technology in developing Cloud IDS, more focused on making use of traditional methods in protecting Cloud environment. This framework did not really represent the state of the art Cloud IDS technology because implementing traditional method in Cloud computing environment will introduce another risk to the environment.

* Cloud subscribers' data privacy: Within the process of detecting intrusions involve monitoring enormous data flow and most of the data belong to subscribers and may contains sensitive or private information. As discussed in [69], protecting data privacy is the major challenges in Cloud computing and pooling user's data in Cloud introduces a new risk in data privacy breach.

* Detecting co-residency attack: [5] and [70] describe co-residency attack as an internal attack conducted by a tenant targeting another tenant residing within the same Cloud infrastructure. This type of attack is unique to Cloud and exist because of the nature of Cloud computing. Although this issue is important, little research has been carried out covering this challenge. Although multiple reviewed frameworks stressed on the importance for protecting Cloud from co-residency attack, very few of them implemented it. Other frameworks more focused on entry point security and endpoint security while neglecting the importance of monitoring the risk of co-residency attack.

4. Conclusions

Cloud IDS aims to provides protection for Cloud computing environment from attacks while maintaining its functionality. Protecting Cloud is importance since its nature that pool user data in centralised source [71]. We have given an extensive survey of current Cloud IDS research in this review which focused on different approaches in developing Cloud IDS frameworks. We also highlight the motivations for Cloud IDS and the basic definitions and principle of Cloud IDS in the literature.

Research in Cloud IDS is still expanding and there are areas of research to be explored especially with the current advancement of detection algorithm. As previously discussed where Cloud IDS research evolved from the Grid computing protection mechanism and current technologies make use of machine learning algorithm including game theory and Artificial Immune System (AIS). The future will explore the specific data set that represents Cloud computing environment which the analysis will describe true performance of Cloud IDS.

The future also may explore the detection of intrusions for big data that become the continuity of the Cloud computing research. [72] discussed that big data will become the future of Cloud research where data collected by Cloud become larger than any typical database may capture, stored and analyse. Future Cloud IDS framework need to be prepared for threat analysis related to big data.


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Azuan Ahmad (1), Wan Shafiuddin Zainudin (2), Mohd Nazri Kama (3), Norbik Bashah Idris (4), Madihah Mohd Saudi (1), Nur Hafiza Zakaria (1)

(1) Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia

(2) CyberSecurity Malaysia, Malaysia

(3) Advanced Informatics School, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

(4) Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Malaysia
Table 1. Cloud Intrusion Detection System on Virtual Machine Monitor

Title             IDS Type  Detection   Positioning   Dataset

Livewire [16]     HIDS      Anomaly     On the        Custom Dataset
Intrusion         NIDS      Signature   On each VM    Not stated
Detection in the            based
Cloud [18]                  detection
IDS               Hybrid    Anomaly     On each Grid  log system and
for Grid and                based       node          from data
Cloud                                                 captured during
Computing [3]                                         node
Fingerprinting    HIDS      Signature-  Domain 0      Custom Dataset
system calls                based
approach for
IDS in
NvCloudIDS        Hybrid    Hybrid      VMI based     UOC, UNSW-NB
[22]                                    IDS           and CAIDA

Title             Advantages        Limitations

Livewire [16]     Livewire are      Livewire will
                  able to detect    suspend the
                  all the attacks   execution of
                  in their          the VM until
                  experiment        the admin
                                    responds to
                                    that event
Intrusion         Secures VM        Multiple
Detection in the  based on          instances of
Cloud [18]        user              IDS are
                  configuration.    required which
IDS               Low               Varied and
for Grid and      processing cost   uncertainty
Cloud             and satisfactory  false positive
Computing [3]     performance       results.
Fingerprinting    Detects           Time
system calls      malicious         consuming
approach for      software by       tasks and the
IDS in            system call       author still
Cloud[21]         sequence          using manual
NvCloudIDS        Provides high     Attack on the
[22]              detection result  hypervisor will
                                    bring down the
                                    whole system.

Table 2. Distributive, Collaborative and Agent-based Cloud IDS Research

Title           IDS Type  Detection        Positioning

Distributed     NIDS      Signature-based  On Domain 0
Cloud IDS [26]
Adaptive        NIDS      Hybrid           Any nodes
for Cloud
Collaborative   Hybrid    Real Time        On each node
detection [15]
Mini-IDS [29]   NIDS      Signature-based  On each cloud
IRAS [30]       NIDS      Anomaly-based    Agent on each
                                           Cloud host
Agent based     NIDS      Signature-based  Outside of
Cloud IDS [31]                             cloud network
EICIDS [32]     NIDS      Real Time        On each node
Collaborative   NIDS      Hybrid           On each cloud
NIDS [33]                                  host and at the
                                           front end
Collaborative   NIDS      Signature-based  On each cluster
Framework for
Cloud [34]

Title           Dataset         Advantages

Distributed     Custom DOS      Improvement in
Cloud IDS [26]  attack          term of
                                processing time
Adaptive        No explanation  May detects
Distributed                     both Network
IDS                             attacks and host
for Cloud                       attacks.
Collaborative   No explanation  May detects
intrusion                       both Network
detection [15]                  attacks and host
Mini-IDS [29]   No              Able to handle
                Implementation  large network
IRAS [30]       Not yet         provide self-
                implemented     awareness, self-configuration
                                and self-healing
                                in the cloud.
Agent based     No              Get the overall
Cloud IDS [31]  implementation  view of
                                network traffic
EICIDS [32]     Custom Attacks  EICIDS may
                                detects inter-VM
Collaborative   Not provided    Can monitor
NIDS [33]                       both external
                                and internal
Collaborative   KDDCup 99       Protect against
IDS                             DDoS attack
Framework for
Cloud [34]

Title           Limitations

Distributed     May unable to
Cloud IDS [26]  detect inter-VM
Adaptive        Communication
Distributed     among IDS
IDS             nodes may create
for Cloud       additional
Computing       unwanted traffic
[28]            for the Cloud
Collaborative   Need more
intrusion       explanations
detection [15]  about the
                experiments and
Mini-IDS [29]   Did not detect
                unknown attacks
IRAS [30]       Single point of
Agent based     Unable to detect
Cloud IDS [31]  new unknown
                attacks and inter-
                VM attack
EICIDS [32]     Not fault tolerant
                since the
                architecture is
Collaborative   Will result a
NIDS [33]       complex
                configuration of
                a set of NIDS
                and the
                process will
                consume more
Collaborative   Requires more
IDS             training samples
Framework for
Cloud [34]

Table 3. Cloud IDS as a Service Research

Title              IDS Type  Detection

Cloud-based IDS    NIDS      Signature-based  IDS as a
[11]                                          service
SIDSCC [35]        NIDS      Signature-based  IDS as a
IDS as a service   NIDS      Signature-based  Snort is
[36]                                          provided as a
                                              web service
CIDS [37]          Hybrid    Hybrid           IDS as a
A hierarchical,    Hybrid    Hybrid           Located in a
autonomous, and                               cloud node
forecasting cloud
IDS [38]
SaaSIDS [39]       NIDS      Hybrid           IDS as a
Rules based        NIDS      Signature-based  IDS as a
Enhancement in                                service
Cloud IDS Service
Pub-Sub IDS [41]   NIDS      Signature-based  IDS as a

Title              Dataset         Advantages        Limitations

Cloud-based IDS    Experiment are  Reduce cost of    Single point of
[11]               not being done  managing IDS      failure and
                                   and cost of       internal attack
                                   resource          will be
SIDSCC [35]        ICMP Flood      Provide           Did not detect
                   attack          promising         unknown
                                   performance.      attacks
IDS as a service   Custom dataset  Able to detect    Invisible to
[36]                               known attack.     unknown or
                                                     new attack.
CIDS [37]          CIDD            Able to detect    The analysis
                                   both known        may have delay
                                   and unknown       because of the
                                   masquerade        location of
                                   attack.           distributed
                                   Eliminate         analysis cloud.
                                   single point of
A hierarchical,    CIDD            Detect more       Single point of
autonomous, and                    attacks than      failure.
forecasting cloud                  previous
IDS [38]                           version
SaaSIDS [39]       Custom Dataset  Reduce cost of    The IDS can be
                                   implementing      flooded by the
                                   IDS and human     traffic of the
                                   resource          client.
Rules based        Custom dataset  Able to           Did not detect
Enhancement in                     eliminate         unknown
Cloud IDS Service                  single point of   attacks
[40]                               failure and
                                   availability of
                                   IDS service
Pub-Sub IDS [41]   No              Prevent single    Unable to
                   Implementation  point of failure  detect

Table 4. Cloud IDS Research based on Open Source Approaches

Title             IDS Type   Detection         Positioning

NIDS for Open     NIDS       No                On Virtual
Source Cloud                 explanation in    Machine
[42]                         the paper         Manager
IDPS [44]         NIDS       Hybrid and        Traditional
                             prevention        Network
Incorporating     NIDS       Hybrid            Cluster
Honeypot for                                   Controller
IDS                                            (Eucalyptus
in Cloud [43]                                  framework)
Integrated        NIDS       Hybrid            Within Cloud
IDPS with                                      Network
honeypot on
SnortFlow [46]    NIDS       Signature-        Domain 0
Snort for         NIDS       Signature-based   Behind
Cloud IDS [47]                                 gateway
Suricata and      NIDS       Signature-based   On multiple
KVM Cloud                                      places
IDS [48]
Integrated        Hybrid     Signature-based   Hypervisor
Open Source
Cloud IDS [50]
IDS for private   Hybrid     Hybrid            In VM host
Cloud [51]

Title             Dataset         Advantages        Limitations

NIDS for Open     No explanation  The IDS has a     No IDS
Source Cloud                      good              performance
[42]                              performance       result based on
                                  result based on   detection rate
                                  the resource
IDPS [44]         No explanation  Able to detect    Inter-VM attack
                                  both known        will be invisible
                                  and unknown       from IDS
Incorporating     Custom dataset  Detect known      Need some
Honeypot for                      and unknown       improvement in
IDS                               attacks.          detecting high
in Cloud [43]                     Controlled use    level of security
                                  of honeypot       threat
                                  generates less
                                  number of false
                                  alarms for
Integrated        Own dataset     Detect and        The honeypot is
IDPS with                         prevent internal  useless if no
honeypot on                       attack            attack targeting
cloud                                               them
SnortFlow [46]    Custom dataset  Performance is    Unable to detect
                                  good in           inter-VM attacks
Snort for         No              Provide overall   Unable to detect
Cloud IDS [47]    implementation  perimeter         inter-VM or
                                  protection for    internal attack
Suricata and      No information  Reduce cost       Unable to detect
KVM Cloud                                           unknown attacks
IDS [48]
Integrated        Not describe    Cost effective    Redundancy of
Open Source                       solutions         NIDS may
Cloud IDS [50]                                      sacrifice
IDS for private   KDD99           95.5%             Can be expand
Cloud [51]                        detection rate.   with real

Table 5. Cloud IDS Research in Machine Learning approaches

Title            IDS Type     Detection        Positioning

Self-similarity  HIDS         Anomaly          On each cloud
IDS [64]                      based            host
Network          NIDS         Anomaly          Behind Router
activity                      based
and log audit
for cloud
Virtual HIDS     NIDS (even   Anomaly          Behind router
for Cloud [53]   the title    based
Detecting DoS    NIDS         Anomaly-based    Domain 0
attack in cloud
matrix [54]
HMM              NIDS         Hybrid           On VMM
model [55]
GA Cloud IDS     NIDS         Signature        On domain 0
[56]                          based
ANN Cloud        HIDS         Anomaly-based    VMM
IDS [59]
HIDS for         HIDS         Signature        On each cloud
Cloud [60]                    based            host
Smith-           HIDS         Signature-based  On Domain 0
GPU Cloud
IDS [61]
ANN Cloud        NIDS         Anomaly-based    On the switch.
IDS [62]
GA Cloud         NIDS         Anomaly-based    Simulation
FC-ANN           NIDS         Anomaly-based    On the switch
Cloud IDS
Differential     Numerical    Anomaly-based    MATLAB
Game Model       Analysis                      Simulation
DCA              Hybrid       Hybrid           Virtual Switch
CloudIDS [68]                                  and Each
                                               Cloud VM

Title            Dataset           Advantages

Self-similarity  DARPA 1999        May work
IDS [64]                           robustly even
                                   though the
                                   Windows event
                                   log does not
                                   include enough
                                   Short learning
Network          Not describe      Detecting
activity                           unknown
classification                     attack based on
and log audit                      traffic analysis
for cloud
Virtual HIDS     CIDD              Up to 80%
for Cloud [53]                     TPR
Detecting DoS    Custom attack     Able to detect
attack in cloud                    the DoS
matrix [54]
HMM              LLDDoS1.0         Successfully
prediction                         fired the early
model [55]                         warning alerts
                                   before the
                                   launching of
                                   the attack
GA Cloud IDS     No                GA with fixed
[56]             implementation    generational
                                   iteration helps
                                   in enhancing
                                   the security
ANN Cloud        No information    Increases
IDS [59]                           resource
                                   availability and
                                   optimise IDS
HIDS for         Custom System     Up to 96% of
Cloud [60]       Call dataset      accuracy
Smith-           Custom co-        Six times faster
Waterman         residency attack  performance
GPU Cloud
IDS [61]
ANN Cloud        ISOT dataset      Provide great
IDS [62]                           accuracy result
GA Cloud         Custom dataset    57% detections
IDS[57]                            of random sets
                                   of cloud
FC-ANN           Not implemented   Improve the
Cloud IDS                          result of ANN
[63]                               Cloud IDS
Differential     No explanations   Improve cloud
Game Model                         security and
[66]                               reduce energy
DCA              DARPA 1999        Total
CloudIDS [68]                      protection for
                                   every part of
                                   Cloud IDS

Title            Limitations

Self-similarity  Did not detect co-
IDS [64]         residency attack
Network          Unable to detect
activity         inter-VM or
classification   internal attack.
and log audit
for cloud
Virtual HIDS     Unable to detect
for Cloud [53]   inter-VM or
                 internal attack.
Detecting DoS
attack in cloud
matrix [54]
HMM              Require further
prediction       testing with other
model [55]       attacks.
GA Cloud IDS     Did not detect
[56]             unknown attack.
ANN Cloud        Unable to detect
IDS [59]         network-based
HIDS for         Unable to detect
Cloud [60]       novel attack and
                 the system did
                 not provide real
Smith-           Unable to detect
Waterman         unknown attack.
GPU Cloud
IDS [61]
ANN Cloud        Unable to detect
IDS [62]         internal attacks
GA Cloud         The detection rate
IDS[57]          can be improved
                 in the future
FC-ANN           Single point of
Cloud IDS        failure when
[63]             implemented IDS
                 on switch.
Differential     Requires more
Game Model       explanation on
[66]             the experiment in
                 real environment.
DCA              Did not handle
CloudIDS [68]    very well in term
                 of high traffic

Table 6. Summary of Cloud IDS Research

Title                               IDS Type   Co-residency

Livewire [16]                       HIDS       No
Intrusion Detection in the Cloud    NIDS       No
Distributed intrusion detection in  Hybrid     No
clouds using mobile agents [34]
Intrusion Detection                 Hybrid     No
for Grid and Cloud
Computing [3]
Distributed Cloud IDS [26]          NIDS       No
NIDS for Open Source Cloud          NIDS       No
SIDSCC [35]                         NIDS       No
Self-similarity IDS [64]            HIDS       No
Cloud-based IDS [11]                NIDS       No
IDS as a service [36]               NIDS       No
CIDS [37]                           Hybrid     No
IDPS [44]                           NIDS       No
Collaborative intrusion detection   Hybrid     Yes
Adaptive Distributed IDS            NIDS       Yes
for Cloud Computing [28]
Incorporating Honeypot for IDS      NIDS       No
in Cloud [43]
Fingerprinting system calls         HIDS       No
approach for IDS in Cloud [21]
A hierarchical, autonomous, and     Hybrid     No
forecasting cloud IDS [38]
Network activity classification     NIDS       No
schema in IDS and log audit for
cloud computing [52]
Virtual HIDS for Cloud [53]         NIDS       No
Integrated IDPS with honeypot on    NIDS       No
cloud computing environment
SnortFlow [46]                      NIDS       No
Detecting DoS attack in cloud       NIDS       No
computing using covariance
matrix [54]
SaaSIDS [39]                        NIDS       No
Rules based Enhancement in          NIDS       No
Cloud IDS Service [40]
Mini-IDS [29]                       NIDS       No
Pub-Sub IDS [41]                    NIDS       No
IRAS [30]                           NIDS       No
HIDS for Cloud [60]                 HIDS       No
HMM prediction model [55]           NIDS       No
GA Cloud IDS [56]                   NIDS       No
Snort for Cloud IDS [47]            NIDS       No
Agent based Cloud IDS [31]          NIDS       No
ANN Cloud IDS [59]                  HIDS       Yes
EICIDS [32]                         NIDS       Yes
Smith-Waterman GPU Cloud IDS        HIDS       Yes
Suricata and KVM Cloud IDS          NIDS       Yes
ANN Cloud IDS [62]                  NIDS       No
Integrated Open Source Cloud        Hybrid     Yes
IDS [50]
FC-ANN Cloud IDS [63]               NIDS       No
Collaborative NIDS [33]             NIDS       Yes
Collaborative IDS Framework for     NIDS       Yes
Cloud [34]
GA Cloud IDS[57]                    NIDS       No
IDS for private Cloud [51]          Hybrid     Yes
NvCloudIDS [22]                     Hybrid     Yes
Differential Game Model [66]        Numerical  No
DCA CloudIDS [68]                   Hybrid     Yes

Title                               Data Privacy  Real-time
                                    Concern       Detection

Livewire [16]                       Yes           Yes
Intrusion Detection in the Cloud    Yes           Yes
Distributed intrusion detection in  No            Yes
clouds using mobile agents [34]
Intrusion Detection                 No            No
for Grid and Cloud
Computing [3]
Distributed Cloud IDS [26]          No            No
NIDS for Open Source Cloud          No            Yes
SIDSCC [35]                         No            Yes
Self-similarity IDS [64]            No            No
Cloud-based IDS [11]                No            No
IDS as a service [36]               No            Yes
CIDS [37]                           No            Yes
IDPS [44]                           Yes           No
Collaborative intrusion detection   No            Yes
Adaptive Distributed IDS            No            No
for Cloud Computing [28]
Incorporating Honeypot for IDS      No            Yes
in Cloud [43]
Fingerprinting system calls         No            No
approach for IDS in Cloud [21]
A hierarchical, autonomous, and     No            Yes
forecasting cloud IDS [38]
Network activity classification     No            No
schema in IDS and log audit for
cloud computing [52]
Virtual HIDS for Cloud [53]         No            No
Integrated IDPS with honeypot on    No            No
cloud computing environment
SnortFlow [46]                      No            Yes
Detecting DoS attack in cloud       No            Yes
computing using covariance
matrix [54]
SaaSIDS [39]                        No            Yes
Rules based Enhancement in          No            No
Cloud IDS Service [40]
Mini-IDS [29]                       No            No
Pub-Sub IDS [41]                    No            No
IRAS [30]                           No            No
HIDS for Cloud [60]                 No            Yes
HMM prediction model [55]           No            Yes
GA Cloud IDS [56]                   No            No
Snort for Cloud IDS [47]            No            No
Agent based Cloud IDS [31]          No            No
ANN Cloud IDS [59]                  No            No
EICIDS [32]                         No            Yes
Smith-Waterman GPU Cloud IDS        No            Yes
Suricata and KVM Cloud IDS          No            Yes
ANN Cloud IDS [62]                  Yes           No
Integrated Open Source Cloud        No            Yes
IDS [50]
FC-ANN Cloud IDS [63]               No            No
Collaborative NIDS [33]             No            Yes
Collaborative IDS Framework for     Yes           No
Cloud [34]
GA Cloud IDS[57]                    No            No
IDS for private Cloud [51]          No            No
NvCloudIDS [22]                     No            No
Differential Game Model [66]        No            No
DCA CloudIDS [68]                   Yes           Yes
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Author:Ahmad, Azuan; Zainudin, Wan Shafiuddin; Kama, Mohd Nazri; Idris, Norbik Bashah; Saudi, Madihah Mohd;
Publication:International Journal of Communication Networks and Information Security (IJCNIS)
Article Type:Report
Date:Dec 1, 2018
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