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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Scheme of security in Mobile Ad Hoc Networks using Route Blacklist Limit Mechanism Hemant Kamle, Geetika Dubey Computer Science And Engg. ,SATI , Vidisha,RGPV kamle_hemant@yahoo.co.in M.Tech (Student) Abstract. Routing protocols play an bandwidth conversation are the two key important role for communications in Mobile elements presenting re- search challenges. Ad Hoc Network Mobile Ad hoc Networks Limited bandwidth makes a network easily (MANET) has various types of Denial of congested by control signals of the routing Service Attacks (DoS) are possible because of protocol. Routing schemes developed for the inherent limitations of its routing wired networks seldom consider restrictions of protocols. Denial-of-Service (DoS) and this type. Instead, they assume that the Distributed Denial-of Service (DDoS) are network is mostly stable and the overhead for widely known security attacks which attempt routing messages is negli- gible. Considering to make computer resources unavailable to its these differences between wired and wireless intended users. Considering the Ad hoc On network, it is necessary to develop a wireless Demand Vector routing protocol as the base routing protocol that restricts congestion in the protocol it is possible to find a suitable network [1][2][3]. solution to over- come the attack of initiating / This paper proposes minor modifications to forwarding fake Route Requests (RREQs) that the existing AODV routing protocol (RFC lead to hogging of network resources and 3561) in order to restrict congestion[4][5] in hence denial of service to genuine nodes. In the network during a particular type of DoS this paper, a proactive scheme is proposed that attack[6]. In addition to this it incurs could prevent a specific kind of DoS attack absolutely no extra overhead [7]. The rest of and identify the misbehaving node. Since the this paper is organized as follows. In section 2, proposed scheme is distributed in nature it has we describe the DoS attack caused due to the capability to prevent Distributed DoS as RREQ flooding and its implications on the well. DoS attacks consume the resources of a existing AODV driven MANET [8][9]. To remote host or network that would otherwise combat this DoS attack a pro- active [10] be used for serving legitimate users. The scheme is proposed in section 3. Section 4 performance of the proposed algorithm in a presents an illustration to describe the series of simulations reveal that the proposed implications of RREQ flooding on pure scheme provides a better solution than existing AODV and the modified AODV. To quantify approaches with no extra overhead. the effectiveness of the proposed scheme, a According to our proposal we apply RREQ DoS [11] attack was simulated in the mobile limit and check through that limit and prevent environment and its performance results are our network. reported in section 5. Keyword :- 2. Related Work RREQ_Accept_limit, AODV, Radio range, The security difference between wired MANET, DOS. infrastructure networks and mobile ad hoc networks [12] motivated researchers to model 1. Introduction an IDS that can handle the new security challenges such as securing routing protocols. In an ad hoc wireless network where wired A cooperative intrusion detection model has infrastructures are not feasible, energy and been proposed in [13], where every node 557 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 participates in running its IDS in order to interaction between intruders and IDS in wired collect and identify possible intrusions. If an infrastructure networks. In [14], the authors anomaly is detected with weak evidence, a aimed at demonstrating the suitability of game global detection process is initiated for further theory for development of various decision, investigation about the intrusion through a analysis, and control algorithms in intrusion secure channel. This model suffers from detection. They accomplished this by performance penalties and false alarm rates. addressing some of the basic network security An extension of this model was proposed in tradeoffs, and giving illustrative examples in [14], where a set of intrusions can be different platforms. They proposed two identified with their corresponding source. different schemes, based on game theoretic Moreover, authors addressed the problem of techniques. They considered a generic model run-time resource constraint by IDS through of a distributed IDS with a network of sensors. modeling a repeatable and random head Finally, one can conclude that game theory is cluster election framework. Watchdog and a strong candidate to provide the much-needed pathrater were proposed in [15] to improve the mathematical framework for analyzing the throughput in MANET in the presence of interaction between the IDSs in MANET to misbehaving nodes. Watchdog goal is to reduce false positives. identify the compromised nodes in the network while pathrater goal is to notify the In AODV, a malicious node can override the routing protocols from using misbehaving restriction put by RREQ_RATELIMIT [12] nodes. This model did not propose any (limit of initiating / forwarding RREQs) by punishment procedure that could motivate increas- ing it or disabling it. A node can do so nodes to behave normally. Hence, because of its self-control over its parameters. misbehaving nodes can continue operating in The default value for the RREQ_RATELIMIT the network and benefiting from others’ is 10 as proposed by RFC 3561. A services. CORE [16] is a cooperative compromised node may choose to set the enforcement mechanism based on a value of parameter RREQ_RATELIMIT to a monitoring and reputation systems. The goal very high number. This allows it to flood the of this model is to detect selfish nodes and net- work with fake RREQs [12] and lead to a enforce them to cooperate. Each node keeps kind of DoS attack. In this type of DoS attack track of other nodes’ cooperation using a non-malicious node cannot fairly serve other reputation as the cooperation metric. CORE nodes due to the network-load imposed by the ensures that misbehaving nodes are punished fake RREQs. This leads to the following by gradually stopping communication services problems: and provides incentives for nodes, in the form ••Wastage of nodes’ processing time (more of reputation, to cooperate. Note that, overhead) reputation is calculated based on data Wastage of bandwidth monitored by local node and information .Exhaustion of the network resources like provided by other nodes involved in each memory (routing table en- tries) operation. •Exhaustion of the node’s battery power Game theory [20] was successfully used in This further results in degraded throughput. many disciplines including economics, Most of the network resources are wasted in political science and computer science. trying to generate routes to destinations that do Currently, it has been used to address not exist or routes that are not going to be used problems where multiple players with for any communication. This implies that the different objectives can compete and interact existing version of AODV is vulnerable to with each other. To predicate the optimal such type of malicious behav- ior from an strategy used by intruders to attack a network, internal node (which is then termed as a the authors of [17] modeled a non-cooperative compromised node). game theoretic model to analyze the 558 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 3. Proposed Scheme: RREQs originated/forwarded by a neighboring node per unit time is tracked. According to malicious node criteria, If this count exceeds the value of malicious node increase RREQ (Route RREQ_BLACKLIST_LIMIT, one can safely request) limit from 10 to greater value per assume second and flooding it to neighbor nodes , if neighbor node work without checking this parameter so that neighbor node not fairly 3.3 Algorithm Route Deployment serve another packets, that means node work Figure 1 illustrates the working procedure in without checking RREQ_Limit and congested the proposed AODV scheme. itself and to others. As shown in the figure, malicious node So in our proposal we create a module for (depicted by the black node) floods RREQs in checking RREQ packet limit and forward to the network and two genuine nodes (depicted actual destination nodes, here we divide our by purple nodes) want to communicate with proposal into two parts. each other. In this scheme, the no. of RREQs that can be accepted from a neighbor is 1) RREQ Accept Limit limited. Hence, the neighbors of the malicious node, will only accept and forward three 2) RREQ Blacklist Limit RREQ packets received from it within a time interval of one sec. This rate limit of three The proposed scheme shifts the responsibility packets is to ensure fair share of a node’s to monitor this parameter on the node’s resources to all the neighbors. Moreover, neighbor, thus ensuring the compliance of this whenever the malicious node crosses the restriction. This solves all of the problems RREQ_BLACKLIST_LIMIT of 10 RREQ (mentioned in section 2) caused due to packets within a time interval of one sec, its flooding of RREQs from a compromised node. neighbors will blacklist it. Thus, in addition to Thus instead of self-control, the control limiting the clogging up of resources in the exercised by a node’s neighbor results in network, the proposed scheme also, isolates preventing the flooding of RREQs. the malicious node. The route established in 3.1 RREQ_ACCEPT_LIMIT this scheme is expected to be the optimum RREQ_ACCEPT_LIMIT denotes the number route, which consists of minimum number of of RREQs that can be accepted and processed intermediate nodes. Thus, no DoS attack is per unit time by a node. The purpose of this experienced in the developed scheme. parameter is to specify a value that ensures Figure:1 Route Request Flooding with Blacklisted uniform usage of a node's resources by its Node neighbors. RREQs exceeding this limit are dropped, but their timestamps are recorded. This information will aid in monitoring the neighbor's activities. In the simulations carried out, the value of this parameter was kept as three (i.e. three RREQs can be accepted per unit time). This value can be made adaptive, depending upon node metrics such as it memory, processing power, battery, etc 3.2 RREQ_BLACKLIST_LIMIT The RREQ_BLACKLIST_LIMIT parameter is used to specify a value that aids in determining whether a node is acting malicious or not. To do so, the number of 559 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 4. Simulation Details Simulation time 50 (seconds) The simulation described in this paper was Traffic type CBR Packet size (bytes) 1000 tested using the ns-2 test-bed that allows users Number of traffic 10 to create arbitrary network topologies [22]. By connections changing the logical topology of the network, Maximum Speed 30 ns-2 users can conduct tests in an ad hoc (m/s) network without having to physically move the nodes. ns-2 controls the test scenarios through a wired interface, while the ad hoc We get Simulator Parameter like Number of nodes communicate through a wireless nodes, Dimension, Routing protocol, traffic interface. etc. The AODV protocol incorporated in NS-2 by According to below table 1 we simulate our Uppsala University, Sweden, was used as the base protocol. Modifications were made to this version network. of AODV protocol that confirms to RFC 3561. 4.2 UDP Comparison of 25 nodes with TCP was used as the transport protocol Radio random movement transmission range is set as 250 meters. Traffic sources used are Constant-Bit-Rate (CBR) and the Figure 3 show UDP packet transmissions field configuration is 800 x 800m with 40 nodes. which includes packet receive, packet lost and total packet transmission and comparison normal case. Graph shows that the packet received is much more than the packet loss. Figure: 2 A sample topology generated by ns-2 4.1 Simulation Parameter Here we create the simulation parameter table, according to that given table we simulate the Figure 3 UDP packets Normal Case our result and analyze them. we use routing 4.3 UDP Comparison after Blacklist Node protocol as AODV with uni-casting Figure 4 show UDP packet transmissions mechanism and take the simulation time as 50 which includes packet receive, packet lost and sec. Table-1 total packet transmission and comparison after blacklist node enter in our network. Graph Metrics Parameter Number of nodes 40 shows that the packet loss is much more than Dimension of 800×800 the packet received. simulated area Routing Protocol AODV 560 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 network through various routing attack effect like worm hole , selfish node and etc. and prevention mechanism for that type of attack so that no any intrusive process comes to our network, and network safe through unwanted activity, we also apply IDS mechanism with DSR , DSDV and other type of mobile ad-hoc routing and simulate our result. References [1] Peng Yang, Biao Huang,Multi-path Routing Protocols for Mobile Ad Hoc Networks, International conference on Figure 4 UDP packets after Blacklisted Node computer science & software engineering 4.4 TCP Comparison 2008. Figure show TCP packet transmission which [2] Dan Galatchi, Roxana Zoican, On- includes packet receives and total packet Demand Multicaste Routing Protocols in transmission and comparison before and after Dynamic Ad Hoc Networks,TELSIKS blacklisted node in network. Graph shows that Serbia,Nis, October 7-9,2009. the packet received is much large before [3] Broch J., Maltz D.A., Johnson D.B., Hu blacklist node comes to our network than the Y.C., and Jetcheva J., A performance entering blacklist node in our network. comparison of multi-hop wireless ad hoc network routing protocols. In 4th International Conference on Mobile Computing and Networking (ACM MOBICOM’ 98), pages 85–97, Oct 1998 [4] Rahman A., Security issues in mobile systems, http://www.cs.ucl.ac.uk/staff/F.AbdulRahman/ docs /sec-in-ob.html, 1995 (accessed on May 03, 2004). 5. Conclusion and Future Work: [5] Royer E.M. and Toh C.K., A review of current routing protocols for ad hoc mobile wireless networks. IEEE Personal According to our result we find out if blacklist Communications, vol. 6:46–55, Apr 1999. node comes to our network so network has [6] Perkins C.E. and Royer E., Ad-hoc on- been heavily congested and actual data packet demand distance vector routing. In 2nd IEEE sends percentage decreases that conclude Workshop on Mobile Computing Systems and blacklist node sense less bandwidth consume Applications, pages 90– 100, 1999. through mobile nodes, also drop rate has been [7] Perkins C.E., Das S.R. and Royer E.. Ad- increases. The malicious nodes identified are hoc on-demand distance vector (aodv) routing. blacklisted and none of the genuine nodes in http:// www.ietf.org/internet-drafts/draft-ietf- the network are wrongly accused of manet-aodv- misbehaving. In the proposed scheme, there is 05.txt, 2000 (accessed on May 03, 2004). an enhancement in the per- formance of the [8] Karpijoki V., Signaling and routing network in presence of compromised nodes. security in mobile and ad-hoc networks. http://www.hut.fi/vkarpijo/iwork00/, 2000 Here we simulate our result through AODV (accessed on May 03, 2004). routing protocol with a Blacklist node and analyze our results, in future we simulate our 561 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 [9] Jacquetand P., Viennot L., Overhead in Networks”, IEEEWireless Communications, Mobile Ad-hoc network Protocols, INRIA IEEE press, pp:48-60, 2004. Research Project RR-3965, 2000. [20] S. Marti, T. Giuli, K. Lai, and M. Baker, [10] Y. Zhang andW. Lee, “Intrusion “Mitigating Routing Misbehavior in Mobile Detection inWireless Ad-Hoc Networks”, Ad Hoc Networks”, Proc. 6th annual Proc. MOBICOM 2000, Boston, ACM press, international conference on Mobile computing pp:275-283, 2000. and networking, Boston, USA, ACM press, pp:255 - 265, 2005. [21] G. Owen, “Game Theory”, 3rd ed. New [11]http//dolphin.eng.uc.edu/networks/thesis/l York, NY: Academic Press, 2001. aksmi%20thesis.pdf (accessed on May 03, [22] Nidhi S Kulkarni, Balasubramanian 2004) Raman and Indra Gupta, On Demand Routing Protocols for mobile Ad Hoc Networks; A [12] Routing Security in Ad Hoc Networks, Review,2009 IEEE International Advance Janne Lundberg, Helsinki University of Computing conference,Patiala,India,6-7 Technology. March 2009. [13] Perkins C.E., Terminology for Ad-Hoc Networking, Draft-IETF-MANET terms- 00.txt, November 1997. [14] A. Agah, S. Das, and K. Basu, “Intrusion Detection in Sensor Networks: A Non- cooperative Game Approach”, Proc. 3rd IEEE International Symposium on Network Computing and Applications, IEEE press, 2004. [15] T. Alpcan and T. Basar, “A Game Theoretic Approach to Decision and Analysis in Network Intrusion Detection”, Proc. of 43rd IEEE Conference on Decision and Control (CDC), Paradise Island, Bahamas, 2004. [16] Y. Huang and W. Lee, “A Cooperative Intrusion Detection System for Ad Hoc Networks”, Proc. 1st ACM Workshop Security of Ad Hoc and Sensor Networks, Virginia, ACM press, pp:135-147, 2003. [17] M. Mehrandish., H. Otrok, M. Debbabi, C. Assi and P. Bhattacharya, “A Game Theoretic Approach to Detect Network Intrusions: The Cooperative Intruders Scenario”, Proc. 49th annual of IEEE GLOBECOM, San Francisco, IEEE press, 2006. [18] P. Michiardi and R. Molva, “Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks”, Journal of Ad hoc Networks, Elsevier, pp:193- 219, 2005. [19] A. Mishra, K. Nadkarni, and A. Patcha, “Intrusion Detection inWireless Ad Hoc 562 All Rights Reserved © 2012 IJARCET
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