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Improved credit-based geographic routing protocol against Blackhole attack in Manet.

INTRODUCTION

A mobile ad hoc network (MANET) is an infrastructure less network which can establish the network through collection of mobile nodes. Each node in the network act as a router to forward the packet to the destination. The basic MANET structure is shown in figure 1. The network topology changes randomly due to mobility of nodes; therefore security is a major challenge in MANETs. Malicious nodes attack the network's availability through common techniques such as flooding, black hole and denial of service (DoS). The high dynamic nature of the network makes the need to operate limited resources with high efficiency that includes CPU processing availability, energy capacity and bandwidth of the network. The packet forwarding is done through links for topology based routing protocols. The position based routing protocols makes the route decision based on nodes geographical position. Because of the difficulties in MANET such as dynamic network topology and constraint battery resources, security solutions that has been deployed for wired networks are not directly portable to ad hoc networks. Many secure routing protocols are developed to protect routing protocols from malicious behaviors. Intrusion Detection System employed for this purpose are inefficient in terms of resource consumption and also it fails when any IDS node in the network does not work properly. Hence a credit based system is proposed to overcome the above problem. MANET is affected by various routing attacks such as black hole, gray hole, Sybil and wormhole attacks. We mainly concentrate on black hole attack which falsely acquires the fresh route to the destination and drops the packet without forwarding them as shown in Figure 1.

A black hole is a node that has the characteristics that it always responds with a RREP message to every RREQ, even though it does not really have a legitimate route to the target node.

MATERIALS AND METHODS

Geographic Routing Protocols:

The Geographic Routing Protocols (GRPs) are used in mobile ad hoc networks for their effective usage based on either positioning devices or other localization schemes. An advantage of GRP is that they prevent network wide searches for destinations. The Control and data packets can be sent in the general direction of the destination if the recent geographical coordinates are known. This reduces control overhead in the network. The principle used in this routing protocol is selecting the next route hop from the node's neighbors, which close to the destination. Since the forwarding route decision is based on local knowledge, there is a need to create and maintain routes for each destination. So, position-based routing protocols are highly scalable and mainly focus frequent changes in the network topology to improve routing. Each node always selects the optimal next hop based on the most current topology which is updated.

Dynamic Source Routing Protocol:

DSR use to update its route caches by finding new routes. It updates its cache with new route discovered or when there exist a direct route between source and destination node. When a source node wants to start data transmission with another node in the network, it checks its routing cache. When there is no route available to the destination in its cache or a route is expired, it broadcast RREQ. When the destination is located or any intermediate node that has fresh enough route to the destination node, RREP is generated. When the source node receives RREP it updates its route cache and the traffic is routed through the route. The node generates a route error message, if it does not receive any confirmation to the originator node. The originator node again performs new route discovery process. It is uncomplicated and efficient protocol. It allows multiple routes to destination node and routing is loop-free here. By combining the topology and position update schemes we maintain the route finding in the network.

Related work:

Ming-Yang Su, proposed a system with several fixed IDS (intrusion detection system) nodes that are deployed in MANETs in order to detect and prevent selective black hole attacks by MAODV. The ABM (Anti-Black hole Mechanism) function is mainly used to estimate the suspicious value of a node according to the amount of abnormal difference between RREQs and RREPs transmitted from the node and if the suspicious value exceeds the predefined threshold, message is broadcast to the nearby IDS, to cooperatively isolate the malicious node as shown in the Figure 2.

Latha Tamilselvan et al. proposed a black hole attack where a malicious node can absorb all data packets by fallaciously claiming a new and fresh route to the destination and then drops them without delivering them to the destination. Co-operative Black hole means the malicious nodes act in a group.

S. Marti, et al in proposed the concept of trust based on watchdog. Watch dog listens to neighboring nodes transmission, it calculates the trust value. Based on the values from this watchdog, trust value on the neighbor is being increased or decreased dynamically. The method is implemented only on DSR protocol.

Heissenbuttel et al, proposed that periodic beaconing make the inaccurate topologies in highly mobile networks, which will cause performance reduction due to outdated entries in the neighbor list. The method describes the beacon interval for nodes motion based on distance, speed, reactive beaconing.

The problem with the existing methods is that IDS nodes are fixed for a particular set of transmission range to detect black hole attack. Depending on the size of the area, usage of IDS increases. If anyone IDS node fails, then the nodes in that particular area is not considered in black hole attack detection process. For example in Figure 2, if IDS_A fails, then the nodes in the coverage area of IDS_A(node 0, 1, 2, 3 & M) cannot be detected. Also in the existing models the mobility characteristics are also not considered.

Proposed algorithm:

The proposed algorithm, Improved Credit-based Geographic Routing Protocol (ICGRP) overcomes the problems in the existing Intrusion detection systems against black hole attack and provides a secure and efficient routing in the network. In this paper, the mobility constraint is also considered. Through 1-hop neighborhood the route path is selected and the nodes position are monitored by setting predefined threshold values and if it exceeds the value beacon packets are sent to the neighboring nodes to update their neighbor list to maintain their local topology. It works on the two basic concepts:

i) Position updates of each node by On-Demand Learning(ODL) rule

ii) Accepting the RREP from a node in the network based on its credit value.

Upon initialization, source node will receive the destination node location through GPS, the current location and velocity through mobility prediction for location identification. Following this, in most geographic routing protocols node selected for route transmission is updated by GPS to verify its current location if there is any change. The position information received from neighboring node is stored at each node. Based on the position updates received from its neighbors, nodes will continuously updates its local topology, which is considered as a neighbor list. Those nodes from the neighbor list will be considered as possible candidates for data forwarding.

Position Update strategy for geographic routing dynamically adjusts the frequency of position updates based on the mobility dynamics of the nodes and the forwarding patterns in the network.

Mobility Prediction Rule:

This rule adapts the node generation rate to the frequency with which the nodes change the characteristics that govern their motion. The motion characteristics are included in the node's neighbors and then track the node's motion using simple linear motion schemes. Nodes that frequently change their motion need to update their neighbors, since there is a dynamic location change.

On-Demand Learning Rule:

The node movements which are harder to predict and the nodes closer to forwarding paths are monitored by GPS and this information is updated by the neighbor nodes which is maintaining the neighbor list for the route path. So, we can able to send data efficiently. One-hop neighborhood is used for forwarding path which is closest neighbor position of source node for effective routing performance to improve the accuracy of the topology along the routing paths between the communicating nodes. If a new node broadcasts it response when it overhears the transmission of a data packet from other vicinity and this path is considered, if it has possible route path to destination.

Credit Value:

Based on trust based communication, initial credit value of every node will be zero. After identifying the location of destination nodes through required path, a source node forwards an RREQ to its neighbor after accepting RREQ, its credit value will be incremented. If any node other than the actual destination node route replies then its credit value is checked. The RREP of a node is accepted based on the following criteria:

i) If credit value of the node is not equal to zero then that RREP is accepted

ii) If it is zero then that RREP will be rejected.

After receiving the RREP from secure neighbors, source sends data by comparing with trust values already defined. Thus this method detects and prevents the malicious black hole attack. Nodes move according to the Random Direction Mobility (RDM) model, a popular model used in the analysis and simulations of wireless adhoc networks. This mobility model maintains a uniform distribution of nodes in the target region over the entire time interval under consideration. Each node has the same radio range, and the radio coverage of each node is a circular area of radius R. The network is sufficiently dense such that the greedy routing always succeeds in finding a next hop node. In other words, we assume that a forwarding node can always find a one-hop neighbor that is closer to the destination than itself.

Performance metrics:

a) packet delivery fraction (pdf):

The packet delivery fraction ([P.sub.f]) is defined as the ratio of number of data packets received ([P.sub.r]) at the destinations over the number of data packets sent ([P.sub.s]) by the sources as given in equation (1). This performance metric is used to determine the efficiency and accuracy of routing protocols.

[P.sub.F] = [[[P.sub.r]]/[P.sub.s]] x 100 (1)

b) Average End-to-End Delay:

It is the average time involved in delivery of data packets from the source node to the destination node. To compute the average end-to-end delay, add every delay for each successful data packet delivery and divide that sum by the number of successfully received data packets as given in equation (2). This metric is important in delay sensitive applications such as video and voice transmission.

[E.sub.a] = [[[summation]([T.sub.r] - [T.sub.s])]/[P.sub.r]] (2)

c) Network Throughput:

A network throughput is the average rate at which message is successfully delivered between a destination node (receiver) and source node (sender). It is also referred to as the ratio of the amount of data received from its sender to the time the last packet reaches its destination. Throughput can be measured as bits per second (bps), packets per second or packet per time slot. For a network, it is required that the throughput is at high-level.

d) Packet Loss (PL):

It is defined as the number of packets dropped by the routers during transmission. It can be shown by equations (3) & (4)

Packet Loss = Total Data Packets Dropped ([P.sub.dr]) (3)

[P.sub.L] = [P.sub.s] - [P.sub.r] (4)

Where, [P.sub.L] = Packet Loss

[P.sub.s] = Total Data Packets Sent

[P.sub.r] = Total Data Packets Received

Thus the percentage of packet loss is given by the equation (5)

[P.sub.L] % = [[[P.sub.dr]]/[P.sub.s]] x 100 (5)

e) Average Residual Energy:

Let [P.sub.ij] and [E.sub.p] be the energy required to route data packet from node i to node j and the energy required to calculate position by the node respectively then the energy consumed by the node in the network Ec is

[E.sub.c] = ([[summation].sup.n.sub.i=1,j=2]) Pij + Ep (6)

Therefore the average residual energy of each node Er may be calculated as Er = Ee - Ec. (7)

where, Ee - total initial energy of a node

Ec--energy consumed by the node

Total energy consumed by the node in the network is equal to the sum of energy required to route packet from one node to another node and the energy required to calculate the position of the node. The average residual energy level of nodes and hence of the network is given by equation(8).

Ea = ([[summation].sup.n.sub.r=1])[[Er]/n] (8)

where n is the total number of nodes.

RESULTS AND DISCUSSIONS

The NS-2 simulator is used to evaluate the MANET network performance. We compare the position based routing protocol, ICGRP, with the conventional DSR and Intrusion Detection based routing protocol to analyze the results obtained for various simulation parameters. In each simulation, the network size is varied from 20 nodes to 50 nodes and the nodes are randomly placed in a region of size 1000m*1000m. The radio range for each node is assumed to be 250 meters. We use constant bit rate (CBR) traffic sources with each source generating 8 packets per second and the packet length are all 1024 bytes. The mobility model used is the Random Waypoint Model. This is shown in Table 1.

The pause time of the node mobility is defined as independent variable that reflects the degree of the node mobility. The simulations are performed by varying the speed from 5m/s to 50 m/s at its successive stages with an interval of 2m/s for a maximum of 50 nodes. The graphical tool Network Animator is used to observe the visual representation of NAM files created during simulation of 50 nodes. The snapshots of visual representations taken at two different time t1 = 138.942153 Sec. and t2 = 138.974673 Sec. are given in Figure 3 and 4.

Impact of network size and mobility of nodes on network parameters:

Figure 5 to 8 presents the PDR, Average End-to-End Delay, Average Residual Energy, and Packet Loss respectively by varying number of nodes. Figure 5 shows that the Packet Delivery Ratio (PDR) is high for the proposed method (ICGRP) than IDS and DSR protocol. Initially with 20 nodes the PDR is almost equal for all the three methods. When the number of nodes are increased to 30, 40 and 50 the PDR decreases due to the packet drop by increasing number of malicious nodes.

Figure 6 shows that End-to-End delay decreases if exact locations of the destination are obtained by avoiding the attacker nodes. Hence ICGPR shows less delay for the packet to reach the destination compared to IDS and DSR. Figure 7 shows the Average Residual energy in the network. In DSR the remaining energy is very low due to unwanted retransmissions due to the malicious nodes. In IDS energy consumption is due to the broadcasting of detected malicious node list to other IDS nodes in the network whenever it is detected. ICGRP uses minimum energy, as it does not have any such process since it detects the malicious nodes just by checking the credit value.

In Figure 8, ICGRP shows low packet Loss as it discards the RREP from malicious nodes and hence there is no way to send the packets to any node that performs attack. In IDS method there is a possibility for drop if any IDS node fails. ICGRP outperforms IDS and DSR as it always choose routes that are more reliable by avoiding the malicious nodes. Figure 9-13 shows that shows that ICGRP performs better than IDS and DSR when the mobility of the nodes is increased. The results show that using credit value based system increases the overall performance of the network. Increasing malicious nodes does not affect the data packets sent but it badly affects the packets received drastically decreases the packet delivery due the drop by malicious nodes.

Conclusion:

Thus, mobility constraints are identified by GPS enabled position update scheme to predict the position of destination nodes through 1-hop neighborhood and the neighbor list is updated accordingly. After finding the route path, source node send its route request to neighbor nodes to verify the path to the destination node using credit value in order to detect black hole attack. The simulation results indicate that location based schemes along with DSR achieve better Packet Delivery Ratio, End-to -End delay, Average Residual Energy and Packet Loss than the existing schemes.

ARTICLE INFO

Article history:

Received 3 September 2014

Received in revised form 30 October 2014

Accepted 4 November 2014

REFERENCES

A Trusted AODV Routing Protocol for Mobile Ad Hoc Networks, 2003. PhD thesis, Department of Computer Science and Engineering, The Chinese University of Hong Kong.

Blazevic, L., S. Giordano, JY. Le Boudec, 2004. "A Location Based Routing Method for Mobile Ad Hoc Networks", in IEEE Transaction on Mobile Computing, 3(4).

Chen, Q. and S.S. Kanhere and M. Hassan, 2010. "Mobility and Traffic Adaptive Position Update for Geographic Routing". CSE, UNSW, Sydney, Australia. UNSW-CSE-TR-1002. Available: ftp://ftp.cse.unsw.edu.au/pub/doc/papers/UNSW/1002.pdf

Heissenbuttel, M., T. Braun, M. Walchli and T. Bernoulli, 2007. "Evaluating of the limitations and alternatives in beaconing", in Ad Hoc Networks, 5(5): 558-578.

Hightower, J. and G. Bordello, 2001. "Location Systems for Ubiquitous Computing", in IEEE Computer, 34(8): 57-66.

Johnson, D., Y. Hu and D. Maltz, 2007. "The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4", RFC4728, February.

Karp, B. and H. T. Kung, 2000. "GP.R: Greedy Perimeter Stateless Routing for Wireless Networks", in Proceedings of ACM Modicum 2000, pp: 243-254, Boston, MA, USA.

Ko, Y. and N.H. Vaidya, 2002. "Location-Aided Routing (LAR) in Mobile Ad Hoc Networks", in ACM/Baltzer Wireless Networks, 6(4): 307-321.

Latha Tamilselvan, Dr. V. Sankaranarayanan, 2008. "Prevention of co-operative black hole attack in MANET", Journal of Networks, 3(5): 13-20.

Marti, S., TJ. Giuli, K. Lai, M. Baker, 2000. Mitigating routing misbehavior in mobile ad hoc networks. In: Proceedings of international conference on mobile computing and networking (MOBICOM'OO), pp: 255-265.

Ming-Yang su, 2011. "Prevention of selective black hole attacks on mobile ad hoc networks through Intrusion detection system", computer communications, Elsevier, 34: 107-117.

Semih Dokurer, Y.M. Erten, Can Erkin Acar, 2007. "Performance Analysis of Ad-hoc Networks under Black Hole Attacks", in: Proc. of the IEEE Southeast Con, pp: 148-153.

Soufine Djahel, Farid Nait-Abdesselam, Ashfaq Khokhar, 2008. "An Acknowledgment- Based Scheme to Defend Against Cooperative Black Hole Attacks in Optimized Link State Routing Protocol", in: Proc. of the IEEE International Conference on Communications (ICC), pp: 2780-2785.

(1) K. Mahalakshmi, (2) Dr. D. Sharmila, (3) R. Shenbagavalli

(1) Vivekanandha Institute of Engineering & Technology for women, Electronics and communication Engineering, Assistant Professor, Namakkal-637 205.India

(2) Bannari Amman Institute. of Technology, Electronics & Instrumentation Engineering, Professor & Head, Sathyamangalam-638 401.India.

(3) Vivekanandha Institute of Engineering & Technology for women, Electronics and communication Engineering, M.E. Embedded System Technologies, Namakkal-637 205. India

Corresponding Author: K. Mahalakshmi, Assistant Professor, Vivekanandha Institute of Engineering & Technology for women, Electronics and communication Engineering, Namakkal-637 205.India

Table 1: Simulation parameters.

        Parameter                Value

        Simulator             NS-2 (V2.35)
      Topology Size          1000m x 1000m
     Number of nodes               50
   Transmission range            250 m
        Bandwidth                2 Mbps
Interference Queue Length         500
      Traffic Type                CBR
       Packet Size             1024 bytes
       Packet Rate            8 packets/s
      Minimum speed              5 m/s
      Maximum speed              10 m/s
     Mobility model         Random Way point
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Author:Mahalakshmi, K.; Sharmila, D.; Shenbagavalli, R.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Geographic Code:9INDI
Date:Nov 1, 2014
Words:3257
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