Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic

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Mr. Roshan Helonde, IJRIT 46

International Journal of Research in Information Technology

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Black Hole Attack Detection on AODV in MANET Black Hole Attack Detection on AODV in MANET Black Hole Attack Detection on AODV in MANET Black Hole Attack Detection on AODV in MANET

Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic

Mr. Roshan Helonde1, Prof. M.R. Joshi2

1M.E. Student, Department of Computer Science and Information Technology, H.V.P.M’s College of Engineering & Technology, Amravati (M.H), India

roshanphelonde@rediffmail.com

2Assistant Professor, Department of Information Technology, H.V.P.M’s College of Engineering & Technology, Amravati (M.H), India

mukundjoshi98@yahoo.co.in

Abstract

There are several standard protocols for Mobile Ad hoc Networks (MANET) that have been developed for devices with higher computing features. The Efficient routing protocols can provide significant benefits to mobile ad hoc networks, in terms of both performance and reliability. Many routing protocols for such networks have been proposed so far. Amongst the most popular ones are Ad hoc On-demand Distance Vector (AODV). In this paper we discuss that how the Black Hole Attack is occurred and how to remove that attack by using Fuzzy System. In this paper an intrusion detection system is introduced to detect Black Hole Attack on AODV in MANET using fuzzy logic. This Fuzzy System uses two factors that is forward packet ratio and destination sequence number. These factors are implemented using fuzzy logic in which fidelity level is checked and compared against threshold value and detected whether there is black hole attack or not. The proposed IDS have been tested using NS2 (Network Simulator 2).

Keywords:AODV, Black Hole Attack, Fuzzy Logic, MANET.

1. Introduction

MANET is a mobile ad-hoc[1] network which dynamically set up temporary paths between mobile nodes which acts both as router and hosts to send and receive packets. As MANET [4] do not have a fixed topology, no base-station support and no fixed routers so at each step nodes have to adjust their transmission and reception parameters. All these characteristics of MANET make it more susceptible to the attacks. One of these attacks is the Black Hole attack. In the Black Hole attack, a malicious node intercepts all data packets across itself by making use of the vulnerabilities of the route discovery packets of the on demand protocols, such as AODV.

2. Aodv and Its Route Discovery Process

AODV: Ad-hoc On Demand Distance Vector routing Protocol is designed to address routing problems in ad- hoc networks and provides communication between mobile nodes. AODV[4] initiates its route discovery process by sending a RREQ (Route Request) packet. After creating the RREQ packet the node sets timer and waits for RREP (Route Reply) message. An intermediate node upon the reception of a RREQ packet checks whether it has seen it before by examining the originator’s IP address and the RREQ broadcast ID pair. Each node maintains a list of the originator IP and RREQ broadcast ID pair for each route request that it receives. This information remains in this list for a finite period of time and it is used to avoid flooding attacks or anomalous node behavior [1,7,10].

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Mr. Roshan Helonde, IJRIT 47 Fig 1: Route Discovery Process

3. Description of Black Hole Attack

Attacks in the network layer have generally two purposes: not forwarding the packets or adding and changing some parameters of routing messages; such as sequence number and hop count. A basic attack that an adversary can execute is to stop forwarding the data packets. As a result, when the adversary is selected as a route, it denies the communication to take place. In black hole attack, the malicious node waits for the neighbors to initiate a RREQ packet. As the node receives the RREQ packet, it will immediately send a false RREP packet with a modified higher sequence number. So, that the source node assumes that node is having the fresh route towards the destination. The source node ignores the RREP packet received from other nodes and begins to send the data packets over malicious node. A malicious node takes all the routes towards itself. It does not allow forwarding any packet anywhere. This attack is called a black hole as it swallows all objects, data packets.

Fig 2: Black hole Attack in MANET

In figure 2, source node S wants to send data packets to a destination node D in the network. Node M is a malicious node which acts as a black hole[8]. The attacker replies with false reply RREP having higher modified sequence number. So, data communication initiates from S towards M instead of D.

4. Black Hole Attack on AODV

In AODV[1], Dst Seq is used to determine the freshness of routing information contained in the message from originating node. To succeed in the black hole attack[1] the attacker must generate its RREP with Dst Seq greater than the Dst Seq of the destination node.

Fig 3: Black Hole Attack

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Mr. Roshan Helonde, IJRIT 48 Here, we assume that the destination node D has no connections with other nodes. The source node S constructs a route in order to communicate with destination node D.Upon receiving RREQ (a1), node A forwards RREQ (b1) since it is not the destination node. To impersonate the destination node, the attacker M sends spoofed RREP(e1) shown in Table 1 with IPSec, AODV.Dst [11] the same with D and increased Dst Seq (in this case 65 as) to source node S.

Table 1: Values of RREQ and RREP

5. Problems In Black Hole Attack

In Designing of intrusion detection system[10] to detect the black hole attack on AODV[1] in MANETs. This detection system is based on FUZZY LOGIC. We proposed a system in which the improvement is by making use of two factors i.e. destination sequence number and forward packet ratio for the detection system. Fuzzy Logic[10]

provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, impressive, noisy or missing input information.

Fig 4: Heavy traffic load on Bad node

The proposed algorithm to detect the black hole attack is given as under:

Black Hole Detect(S,D)

/* S is the source node and D represents the Destination Node over the network*/

{

1. As transmission begins it will search for all the intermediate nodes and send data on to it.

2. Tithe intermediate node failed forwarding the probe message to the next node.

3. Fuzzify the Communication Rate on each Neighbor Node it will check the RESPONSE time for the intermediate node

If (Fuzzy Rule(Response Time)> HIGH) {

The Attacker Node is detected. Update Neighbor Node Table & Routing Table for The Intermediate Nodes

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Mr. Roshan Helonde, IJRIT 49 }

4. The unresponsive node is incapable of responding to the probe message.

5. The diagnosis algorithm will then be called to decide which one is the case.

}

6. Solution Against Black Hole Attack

The proposed system[9] is based upon fuzzy logic[6]. Fuzzy logic is a form of multi valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. The fuzzy model[9] is integrated with AODV routing protocol[13] as shown in fig 5. It consists of following four components i.e.,

6.1 Fuzzy Parameter Extraction 6.2 Fuzzy Computation 6.3 Fuzzy Verification Module 6.4 Alarm Packet Generation Module.

Fig 5: The Purposed System

6.1 Fuzzy Parameter Extraction

The input to the fuzzy system [9] in node “i “is extracted by listening to the traffic received and generated by its immediate neighbors and creates a fuzzy parameter list in new neighbor table for its every neighbor. Each node in the network works in the promiscuous mode (i.e. it can listen to the traffic of its neighbors) and listens to the routing and network traffic of their neighbors and collects the information for fuzzy system. The neighbor table of node “ i “ has the following fields for its neighbor node “ j “ : Forward Packet Ratio, Average Destination Sequence Number and Fidelity Level. Forward packet ratio: data packets forwarded / data packets received. The sequence number of a particular node depends upon the number of connections of respective node in the network. A node having high value of destination sequence number is assumed to be a reliable node in AODV.

6.2 Fuzzy Computation

The proposed system[9] receives forward packet ratio and average destination sequence number as input from routing and network traffic and has one output, Fidelity Level. If forward packet ratio is LOW and sequence number ratio is LOW, then fidelity level is LOW”. The fidelity level lies between 0 and 10. The minimum value for fidelity can occur as a result of more malicious behavior than legitimate behavior of a neighboring node. Hence, a fidelity level of 0 represents complete malicious behavior and 10 represents legitimate behavior of a particular node.

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Mr. Roshan Helonde, IJRIT 50 6.3 Fuzzy Verification Module

In the verification module, the calculated fidelity level is compared with the threshold fidelity level. If the computed fidelity level is less than threshold level, the node is black hole node, otherwise node is legitimate node.

Table 2: Fuzzy Rule Base

6.4 Alarm Packet Generation Module

On the basis of information passed by fuzzy verification module [9], if the fidelity level is less than the threshold fidelity level, this model generates an alarm packet with IP address of the node that is declared as black hole node.

7. Conclusion And Future Work

This work proposes fuzzy logic based a very simple and effective solution to detect and isolate the black hole node from AODV enabled MANET using proposed system. Major improvement of the system can be done in terms of detection rate. The packet delivery ratio of system can be improved up to required level. The Proposed system can be further extended to provide security from more active attacks that a malicious node can perform against the routing protocol. Fuzzy logic incorporates a simple, rule based approach to solving a problem. This system will not only detects the black hole attack in early stage of communication, but isolates it from the network.

Thus improve the performance to great level.

References

[1] Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto, “Detecting Black hole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method”, International Journal of Network Security, Vol.5, issue 3, Nov 2007, pp 338–346.

[2] William Stallings “Wireless Communication & Networks” (Pearson Education). Rajkamal “Mobile Computing”Oxford Press.

[3] Agrawal D.P., Zeng Q, A, “Introduction to Wireless & Mobile Systems” (2/e) CENGAGE Learning.

[4] J.M.L. Manickam and S. Shanmugavel, "Fuzzy Based Trusted Ad Hoc On-demand Distance Vector Routing Protocol for MANET", IEEE in Proc. WiMob, 2007.

[5] Fuzzy Logic with Engineering Applications by Timothy J. Ross Mcgraw Hill, Inc.

[6] Timothy J. Ross,(I2000) “Fuzzy Logic with Engineering Applications”,McGraw Hill International Editions.

[7] Payal N. Raj and Prashant B. Swadas, “DPRAODV: A dynamic learning system against black hole attack in AODV based MANET”, International Journal of Computer Science Issues (IJCSI), Volume 2, Numbe 3, 2009, pp 54-59.

[8] Kulbhushan and Jagpreet Singh , “Fuzzy Logic based Intrusion Detection Syste against Blackhole Attack on AODV in MANET”, IJCA Special Issue on “Network Security and Cryptography” NSC, 2011.

[9] Poonam Yadav, Rakesh Kumar Gill and Naveen Kumar, “A Fuzzy Based Approach to Detect Black hole Attack”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-2, Issue -3, July 2012.

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Mr. Roshan Helonde, IJRIT 51

[10] Miss. Shweta Modi and Mr. Jitendra Prithviraj, “Performance Comparison of IACO, AODV Networking Routing Protocols”, International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN), 2011.

[11] Zaiba Ishrat, “Security issues, challenges & Solution in MANET”, International Journal of Computer Sci ence & Technology (IJCST), Vol- 2, Iss ue- 4, Oct - Dec 2011.

[12] Arash Dana and Mohamad Hadi Babaei, “A Fuzzy Based Stable Routing Algorithm for MANET”, International Journal of Computer Science Issues (IJCSI), ISSN: 1694-0814, Vol 8, Issue-1, January 2011.

[13] Prof.S.P. Setti, Vijay Kumar D V, Nagendra Prasad G S M and Narasimha Raju K, “Implementation of Fuzzy Priority Scheduler for MANET and Performance Analysis with Reactive Protocols”, International Journal of Engineering Science and Technology, Vol. 2(8), 2010,pp 3635-3640.

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