Identifying Black hole attack using Divide and Conquer Algorithm in Mobile Adhoc Network S.Hemalatha 1, P.C.Senthil Mahesh 2 1,2 Professor,Panimalar Institute of Technology Chennai, Tamil Nadu 2 Professor,Annamachariyar Instittue of Technology,Andra Pradesh, E-mail: 1 slaechemalatha@gmail.com, 2 senthilmagesh@gmail.com Correspondent author P.C.Senthil Mahesh Abstract Mobile Ad-hoc network is a collection of nodes which tries to communicate each other without any fixed infrastructure. In this network, nodes can move freely and dynamically from self-organized into arbitrary topologies. Due to self-organizing, the network is vulnerable to attack by an intruder who attempts to gain unauthorized access and damage data on communication medium. Transmitting of packet from source to destination is one of the greatest challenges because the packet should reach the destination without disturbances like delay, packet loss attacker and intruder etc. Black hole attack is kind of denial of service attack, where a node advertising itself as having shortest path to others nodes and commit other nodes to transfer packet through it, but black whole attacker do not forward the packet to the next hope. In this research article proposed an algorithm is used to identify a black hole attack in MANET. This algorithm is implemented using AODV protocol without changing the originality of Adhoc On-Demand Distance vector protocol. Proposed algorithm is implemented by using NS2, and results compared with AODV shows better performance in all aspects. 1.Introduction 924
1 Mobile ad hoc network is designed for a self configuring wireless network which is composed of woks in mobile equipment. Communication of These mobile nodes is done without any fixed infrastructure, and also all further transmission is established in wireless medium. Applications of MANET will be used in military purpose, disaster area, personal area network and so on [1].A Routing protocol in an Ad-hoc network (Wikipedia, 2004) is divided into two main categories of proactive and reactive protocol. In proactive protocol nodes maintain routing information for all other nodes in the network and it is stored in routing table. So this protocol is also named as a table driven protocol. In reactive protocol, route information is established when a packet is transferred between the nodes. In the table driven protocol is classified into different types Destination Sequenced Distance Vector Routing (DSDV), Cluster Gateway switch routing protocol (CGSR), Optimization link state routing protocol, Topology dissemination Based on reverse path (TBRPF), Fish Eye state Routing Protocol (FSR).In source initiated routing protocols are classified into different types of protocols such as Ad-hoc On- Demand Distance Vector (AODV) (Perkins, & Royer 1999), Dynamic source routing protocol (DSR). In Dynamic Source Routing Protocol each node maintains a route cache contains a route learned by the node. Source node only initiates route discovery process enters into a route cache continuously updated. AODV node creates a route on demand to maintain a complete a route using DSDV algorithm. TORA is another source initiated on Demand protocol, in a concept of link reversal of directed Acyclic Graph. TORA has the capacity of routing repair. ABR routing protocol (Giannoulis et al 2007) is on demand protocol route selection is based on the signal strength in the link. 2. LITERATURE SURVEY Intruder detection is a one of the challenges in MANET ( Tiranuch Anantvalee & Jie Wu 2006; Ioanna Stamouli, et al; Dorothy Denning, 1987 925
2 Yongguang Zhang & Wenke Lee 2000). Different methodologies were proposed for identifying an intruder in MANET from the year 2001to 2003, these methodologies were based on the technique of Knowledge-based intrusion detection (Farooq Anjum et al 2003) signature based intruder detection, sensor based intruder detection (Kachirski & Guha 2003), anomaly based intruder detection (Md. Safiqul Islam & Syed Ashiqur Rahman 2011) collaborative intruder detection (Ningrinla Marchang & Raja Datta 2008)and zone based intruder detections Sun, B, Wu, K & Pooch, U 2003.Identification of an intruder was done by defining architecture in MANET during the year 2005 to 2007, based on corporative based intruder detection architecture and RIDAN architecture was developed. Different types of MANET attacks were identified using attacks detection techniques (Ranjana & Rajaram 2007), warm hole attacks, critical node identification (Karygiannis et al 2006; Rajaram & Palaniswami 2010), fabrication attacks (Ranjana & Rajaram, 2007), consumption attacks, packet dropping attacks, black whole attacks were detected based on attack detection techniques. From the above literature survey, the routing protocol only performs routing the packets, none of the protocols have been proposed for identifying a black hole attacker in the Ad hoc network. There is need for an algorithm for efficient delivery of packet to the destination. In this research work AODV protocol is modified for efficient packet delivery. Performances of AODV modified algorithms are compared with AODV and simulation results depict that proposed algorithms out performs existing AODV. 3. RESEARCH METHODOLOGY A novel algorithm is designed and implemented that incorporates identifying black hole attack. This algorithm is implemented by modifying existing AODV protocol as AODV lacks ability to identify black hole attacker node. 926
3 3.1 Overview of Research In this research article new algorithms are designed by modifying Adhoc On-Demand Distance Vector protocol to identify a black hole attacker. Design and analysis of black hole detection algorithm is designed in following stages, and algorithm is named Black hole detection AODV protocol is explained in the following sections. 3.2.1 Black hole Detection Ad-hoc On-Demand Distance Vector (BHDAODV) algorithm Design Stages : Design Stages involved in Black hole detection Adhoc On-Demand Distance Vector algorithm(bhd-aodv) implementation for identifying a black hole node are as follows: A. Decide the path using AODV protocol. B. Transmit Packet. C. Apply Black hole detection algorithm on packets. D. Identify the Black hole attacker node. E. Redirect the new route. F. Sending alert message. 3.2.1.1 Decide the path using AODV protocol In this stage, existing AODV protocol is used to identify route between source node to the destination node. 927
4 3.2.1.2 Transmit Packet Once the path is identified between source to destination using AODV protocol, source node starts sending packet to the destination node through the identified path. 3.2.1.3 Apply Divide and Conquer on packets Divide and Conquer Algorithm Procedure(Source, Dest, G) - Divide and Conquer strategy Consider the ordered Set G={1..N}. Step 1: Initialize source = 1, dest = N. Step 2: Calculate middle = No of hops (source to dest)/2. Step 3: (i) Check whether the middle node forward the packet. If (yes) 928
5 Calculate the new middle node from the middle node to the destination then go to step 2. Else Calculate the middle node from source node to middle node then go to step 2. Repeat the process. If there is no forwarding of packet from any node, that node is marked as a Black hole attacker node. Process whether the middle node is black hole attacker node If True Set black hole attacker node = Middle and initiate route discovery process. Step 5: Process to retransmit the data through stage 1. Step 6: Send alert message about the black hole attacker node. Step 7: Stop. 3.2.1.4 Identify the black hole attacker node The black hole attacker node node is identified when the node is not forwarding the packet to the next node. 3.2.1.5 Route Re Direction The source node discovers new route to retransmit the packets when the node is identified as black hole attacker node. 3.2.1.6 Send Alert Message 929
6 This stage is focused for sending alert message to all nodes in the network about victim to avoid further packets transmission through black hole attacker node. Result : NS2 is used to simulate and compare the results with AODV. Three performance parameters such as throughput, packet delivery ratio, and End to End delay are taken for comparison. The simulation results depict that proposed BHDADOV algorithm better in all aspects.the result of simulations are shown in figure 1,2 and 3. Black hole detection AODV AODV Figure 1 Throughput 930
7 Black hole detection AODV AODV Figure 2 Packet Delivery Ratio Black hole detection AODV 931
8 AODV Figure 3 End To End Delay The AAODV algorithm is further modified for identifying an intruder node in MANET, by adding two more stages to the AAODV algorithm. 4. CONCLUSION An Adhoc network is a combination of different nodes, created for communicating each other without any infrastructure. Transmitting of packet from source to destination is one of the greatest challenges because the packet should reach the destination without disturbances like delay, packet loss and security breach. Adhoc On-Demand Distance vector protocol is designed for transmitting packet by finding a new route when it s needed. Even though this protocol is creating a path on demand, protocol functionality limits on route redirection, security and energy consumption. BHDAODV algorithm designed to identify black hole attacker node overcome it limitation of reliable packet delivery. Simulation results show that the algorithm performs better than exiting AODV. The Proposed research modifies an existing AODV protocol and gives solution to identify black hole attacker node in MANET. The proposed algorithms are simulated by ns2 and outperform the existing ADOV. REFERENCES 1. Dorothy Denning, E 1987, An Intrusion-detection Model IEEE Transaction on Software Engineering, vol.13, no.7, pp.222-232. 932
9 2. Farooq Anjum, Dhanant Subhadrabandhu & Saswati Sarkar, 2003, Signature based Intrusion Detection for Wireless Ad-Hoc Networks: A Comparative Study of Various Routing Protocols. 3 Giannoulis, S, Antonopoulos, C, Topalis, E & Koubias, S 2007, ZRP versus DSR and TORA: A comprehensive survey on ZRP performance, IEEE Transactions on Industrial Informatics vol.3, no.1, pp.63-72. 4. Ioanna Stamouli, Patroklos G. Argyroudis & Hitesh Tewari, 2005 Real-time Intrusion Detection for Ad hoc Networks Proceedings of the Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks WoWMoM 05), 0-7695-2342-0/05. 5. Karygiannis, A, Antonakakis, E & Apostolopoulos, A 2006, Detecting Critical Nodes for MANET Intrusion Detection Systems, pp.7-15. 6. Kachirski, O & Guha, R 2003, Effective Intrusion Detection using Multiple Sensors in Wireless Ad hoc Networks, In Proc. 36th Annual Hawaii Int l. Conf. on System Sciences (HICSS 03), pp.57.1. 7. Ningrinla Marchang & Raja Datta 2008, Collaborative techniques for intrusion detection in mobile ad-hoc networks, Ad Hoc Networks, vol.6(4), pp.508 523. 8. Perkins, CE & Royer, EM 1999, Ad-hoc On-Demand distance vector routing, in: Proceedings of 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp.90-100. 9. Rajaram, A & Palaniswami, S 2010, Malicious node detection system for mobile ad hoc networks. (IJCSIT) International Journal of Computer Science and Information Technologies, vol.1(2), pp.77 85. 10. Ranjana, R & Rajaram, M 2007, Detecting Intrusion Attacks in AdhocNetworks, Asian Journal in Information Technology, vol.6(7), pp.758-761, ISSN: 1682:3915. 11 Md. Safiqul Islam & Syed Ashiqur Rahman 2011, Anomaly Intrusion Detection System in Wireless Sensor Networks: Security Threats and Existing Approaches. International Journal of Advanced Science and Technology vol. 36. 12. Sun, B, Wu, K & Pooch, U 2003, Zone-based Intrusion Detection for Mobile Ad hoc Networks 933
10 13. Tiranuch Anantvalee & Jie Wu 2006, A Survey on Intrusion Detection in Mobile Ad Hoc Networks, pp.170-196. 14. Wikipedia, 2004, The free encyclopedia http://en.wikipedia.org/wiki/ Mobile_ad-hoc_ network. 15 Yongguang Zhang & Wenke Lee 2000, Intrusion detection in wireless ad-hoc networks. In MOBICOM, pp.275 283. 934