Energy Efficient Way to Detect Black Hole Attack in MANETs

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 10 | March 2017 ISSN (online): 2349-6010

Energy Efficient Way to Detect Black Hole Attack in MANETs Harsimran Kaur Student Department of Computer Science Punjabi university, Punjab, India

Kamaljeet Mangat Assistant Professor Department of Computer Science Punjabi university, Punjab, India

Abstract Mobile Ad hoc Networks(MANETs) consists of mobile nodes that are interconnected by wireless network interfaces to create a temporary network, each node among the MANET works as a host as well as a router while receiving data nodes help other nodes to forward packets. The wireless nature of the communication makes these networks susceptible to various kinds of security threats. Since these networks lack any central authority, so any intruder can steal the information from the network.

Black hole is one such attack which intends to claim shortest path to the destination and then drops the data packets received by it. This paper presents the technique to detect the black hole attack but also in an energy efficient way. The proposed scheme has been compared on the basis of the remaining energy, throughput as well as packet delivery ratio. These parameters have showed an improvement over the existing scheme.

Keywords: MANETs, Black Hole, Throughput, Packet delivery ratio _______________________________________________________________________________________________________ I.

INTRODUCTION

A Mobile Ad hoc network (MANET) is wireless self-configuring network composed of different nodes communicate with each other in ad hoc manner without having to create any fixed infrastructure for deploying as well as for centralized administration.

The communication among these mobile nodes depends on the kind of routing mechanism used called multi hop routing protocols and connection is maintained in decentralized manner, routing protocols are designed to guarantee efficient packet delivery in network. Mobile Ad hoc networks(MANETs) are the self-organizing and Dynamic network having the capabilities for random motion of nodes to move in any direction to join or leave the network frequently in any direction or change their links by self-configuration of real time network. MANET can easily set up in situation where it is not possible to set up any infrastructure. In MANETs terminals nodes are not able to communicate directly with each other and they have to rely upon some other nodes, so that message should be delivered to destination such networks are often referred to as multi hop network.

Black hole attack is dangerous active attacks in denial of service attack category present in MANETs. MANET usually uses reactive routing protocols such as DSR OR AODV for the routing of data packets, the routing protocol is used to discover the routes this is performed by two types of packets as Route Request (RREQ) packet and Route Reply (RREP) packet. When the source node wants to exchange information or sends data to destination node then it checks its routing table if there is no route available in routing table then source node starts its initiation process by sending RREQ message to its neighboring nodes in network to find the shortest route between the source and destination in the network. If Black hole node is present in network, it will send False route reply(RREP) in exchange to this route request message claiming to have the shortest optimum path with minimum hop count to the destination or itself is destination node. Upon sending the fake reply packet, the malicious node can able to place itself in the communicating environment and drops or consumes packets when received it means that the transmitting packets must be passed by this malicious node only. After sending the RREP packet, the malicious node receives the data packets from source node and does not forward these packets to neighboring nodes and these packets are either dropped or consumed by this node.

This paper presents related work done by the various authors in past regarding detection or prevention of the black hole attack in the network, section 3 present existing problem, finally the proposed scheme has been presented in section with the results shown at the end.

II. LITERATURE SURVEY Shivani Uyyala et al. (2014), In this paper, the authors have proposed mechanism to detect and isolate these attacks in network.

MANETs are more susceptible to security attacks due to their unique characteristics such as dynamic topology, no fixed infrastructure, resource limitations and multi-hop scenario[1].

Golok et al. (2012) presents an approach which leads to prevent the black hole node [2]. Nobody will pay attention to malicious node’s Hello message packet. The various authors have given various proposals for detection and prevention of black hole attack in MANET but every suggestion has some limitations and their respected solutions. It is clear that malicious node is the main security threat that affects the performance of the AODV routing protocol. Every parameter has shown incredible

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improvement except avg. jitter and avg. end-to-end delay due to the overhead of key mechanism. It will be extensive such that the value of these parameters can be enhanced Khemariya et al. (2013) proposed an proficient approach for the detection and removal of the Black hole attack in the Mobile Ad Hoc Networks (MANET) is described [3]. The algorithm is implemented on AODV (Ad hoc on demand Distance Vector) Routing protocol. It can detect both the single Black hole attack and the Cooperative Black hole attack. The quality of the algorithm described in this paper is that it not only detects the black hole nodes in case when the node is not idle but it can also detect the Black hole nodes in case when a node is idle as well.

Firoz et al. (2012) In this paper the authors proposed EVM method which can pin down multiple black hole nodes efficiently by employing an encryption mechanism [4]. The verification process is initiated conditionally and it verifies the sequence number that was not faked by any malicious node. It shows that the EVM not only reduces the control overhead but also successfully identifies the malicious node.

Modi et al. [5] In this paper an algorithm is proposed that used the trust value which is used to identify the malicious node, after identifying the malicious node it will be removed from the neighbouring table and the source node would select the another path. This proposed algorithm offers a secure way transmission between any nodes in network topology. Researcher propose modification to the AODV protocol and justify the solution with implementation and simulation using NS-2.33. This simulation analysis shows the important improvement in end-to-end delay, throughput, and packet delivery ratio of AODV in presence of Black hole attack.

Nabarun Chatterjeea, Jyotsna Kumar Mandalb et al. (2013) In this research work a technique to avoid Blackhole attack in AODV routing protocol using Triangular Encryption Method in NS2 Simulator is proposed [6]. Triangular Encryption has been selected because of its low computation overhead. The limitation of the proposed work was that it only keep away from the Blackhole behaviour but could not detect and eliminate Blackhole nodes. The implementation becomes very time-consuming as the number of nodes increases. The number of packet sent is much more than the novel AODV protocol and thus it increases network load.

Amol Bhosle, Yogadhar Pandey et al. (2013) In this paper the authors proposed a method to provide the data security using node authentication and digital signature [7]. This new protocol design provides the integrity, confidentiality, non repudiation and verification with the help of AES, and digital signature. The node authentication achieved by the IP address of the nodes.

Digital signature formed with the help of RSA and hash function MD-5. This security mechanism called SMDNA (Securing MANET Data using Node Authentication) improves the performance of the routing protocol AODV. Rashmi, Ameeta Seehra et al. [8] present a clustering approach in Ad-hoc On-demand Distance Vector routing protocol for the detection and prevention of black-hole attack in MANETs. In this approach every member of the cluster will ping once to the cluster head, to detect the peculiar difference between the number of data packets arrived and forwarded by the node. If anomalousness is perceived, all the nodes will obscure the malicious nodes from the network. The results show that clustering approach is responsible for full delivery of packets even in presence of multiple black-hole nodes. As well as the detection rate and throughput are improved by four times and 1.5 times.

T. Prasanna Venkatesan, P. Raja kumar, A. Pitchaik kannu et al. [9] In this paper, existing intrusion detection techniques in the context of MANETs are explained. Since Intrusion prevention alone is not enough to achieve security in a network, it is presented a way to manage MANET security, by enhancing the existing secure protocols adding the component of Malicious nodes, not only in determining the route for sending packets, but also avoiding attempts of Denial of Service from Malicious Nodes. The accuracy of IDS can suffer from the high false positive or low false negative rates. If the majority of the mobile nodes are compromised then the intrusion detection becomes fail. An intrusion detection system aims to detect attacks on mobile nodes or intrusions into the networks. However, attackers may try to attack the IDS system itself, which may be addressed in future.

Payal N. Raj and Prashant B. Swadas et al. [11] proposed an approach DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver-2.33) proves that our protocol not only prevents Blackhole attack but on the other hand improves the overall performance of (normal) AODV in presence of black hole attack.

Djamel Djenouri1, Nadjib Badache2 et al. [12] propose a novel monitoring approach that overcomes some watchdog’s shortcomings, and improves the efficiency in detection. To overcome false detections due to nodes mobility and channel conditions the authors have proposed a Bayesian technique for the judgment, allowing node redemption before judgment.

Finally, the authors have suggested a social-based approach for the detection approval and isolation of guilty nodes. They have analysed the solution and asses its performance by simulation. The results illustrate a large improvement of our monitoring solution in detection vs. the watchdog, and an efficiency through our judgment and isolation techniques as well.

Djamel Djenouri, Nadjib Badache et al. [13] this paper deals with the misbehaviour nodes in mobile ad hoc networks (MANETs) that drop packets supposed to be relayed, whose objective may be either saving their resources or launching a DoS attack. It proposed a new solution to monitor, detect, and safely isolate such misbehaving nodes, structured around five modules: The monitor, responsible for controlling the forwarding of packets, (ii) the detector, which is in charge of detecting the misbehaving of monitored nodes, (iii) the isolator, basically responsible for isolating misbehaving nodes detected by the detector, (iv) the investigator, which investigates accusations before testifying when the node has not enough experience with the accused, and (v) finally the witness module that responds to witness requests of the isolator. These modules are based on new approaches, aiming at improving the efficiency in detecting and isolating misbehaving nodes with a minimum overhead.

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Satyendra Tiwari, Anurag Jain and Gajendra Singh Chowhan et al. [14] proposed a modified ack-based scheme for decision ambiguity for requested node on the basis of finite state machine. Finite state machine is an automata of theory of computation here the authors have used deterministic finite automate for the decision making of node and improved node authentication and minimize packet dropping in adhoc network.

K. Selvavinayaki, DR. E. Karthikeyan et al. [15] To reduce the effect of blackhole attack, a New Enhanced Proactive Secret Sharing Scheme (NEPSSS) to detect the black hole nodes and to ensure the data confidentiality, data integrity and authenticity has been proposed. In first phase of the proposed algorithm, the detection of black hole attack is achieved using trust active and recommendation of the nodes. In second phase of the work, Enhanced Proactive secret sharing scheme is used to provide the data authentication and integrity. The simulation results shows the proposed algorithm achieves the better packet delivery ratio, misbehaviour detection efficiency, fewer packets overhead and low end to end delay than the existing schemes.

Akshat Jain, Shekher singh Sengar & Vikas Goel et al. [16] This paper proposed a method to detect colluding black hole nodes in Ad hoc On Demand Distance Vector (AODV) routing protocol. A Light Weight Packet (LWP) routing mechanism is devised. LWP is digitally signed up by sender. Also the concept of authentic table for neighbours is used to detect whether neighbour is authentic or not. The further work can be done to evaluate the simulation result of proposed algorithm and compare it with existing solutions for optimality.

Bobby Sharma Kakoty et al. [17] In this work it is observed that presence of Blackhole node in MANETs, drastically changes the network performance in terms of higher loss in packets as well as generating lower throughput. It is very difficult to apply traditional attack prevention scheme such as cryptographic technique or general authentication technique due to dynamically changing topology with decentralized node distribution.

Hicham Zougagh, Ahmed Toumanari, Rachid Latif, Y. Noureddine .Idboufker et al. [18]This paper proposed a cooperative black hole attack against MANETs exploiting vulnerabilities of OLSR. In this attack, two attacking nodes cooperate in order to disrupt the topology discovery and prevent routes to a target node from being established in the network.

Abderrahmane Baadache, Ali Belmehdi et al. [19] In this paper, after having specified the black hole attack, a secure mechanism, which consists in checking the good forwarding of packets by an intermediate node, was proposed. The proposed solution avoids the black hole and the cooperative black hole attacks. Evaluation metrics were considered in simulation to show the effectiveness of the suggested solution.

Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto et al. [20] This research paper proposed an anomaly detection scheme using dynamic training method in which the training data is updated at regular time intervals. In conventional schemes, anomaly detection is achieved by defining the normal state from static training data.

However, in mobile ad hoc networks where the network topology dynamically changes, such static training method could not be used efficiently. The simulation results show the effectiveness of our scheme compared with conventional scheme.

Wei Wang, Huiran Wang, Beizhan Wang, Yaping Wang, JiajunWang [21] Anomaly detection is indispensable for satisfying security services in mobile ad hoc network(MANET) applications. Often, however, a highly secure mechanism consumes a large amount of network resources, resulting in network performance degradation. To shift intrusion detection from existing securitycentric design approaches to network performance centric design schemes, this paper presents a framework for designing an energy aware and self-adaptive anomaly detection scheme for resource constrained MANETs. The scheme uses network tomography, a new technique for studying internal link performance based solely on end-to-end measurements. With the support of a module comprising a novel spatial-time model to identify the MANET topology, an energy-aware algorithm to sponsor system service, a method based on the expectation maximum to infer delay distribution, and a Self-organizing Map (SOM) neural network solution to profile link activity, the proposed system is capable of detecting link anomalies and localizing malicious nodes. Consequently, the proposed scheme offers a trade-off between overall network security and network performance, without causing any heavy network overload. Moreover, it provides an additional approach to monitor the spatialtime behaviour of MANETs, including network topology, link performance and network security. The effectiveness of the proposed schemes is verified through extensive experiments.

Mehdi Medadian et.al [22] A mobile ad hoc network (MANET) is an autonomous network that consists of mobile nodes that communicate with each other over wireless links. In the absence of a fixed infrastructure, nodes have to cooperate in order to provide the necessary network functionality. One of the principal routing protocols used in Ad hoc networks is AODV (Ad hoc on demand Distance Vector) protocol. The security of the AODV protocol is threaded by a particular type of attack called ‘Black Hole’ attack. In this attack a malicious node advertises itself as having the shortest path to the destination node. To combat with black hole attack, it is proposed to wait and check the replies from all the neighbouring nodes to find a safe route but this approach suffers from high delay. In this paper, an approach is proposed to combat the Black hole attack by using negotiation with neighbours who claim to have a route to destination. The Simulation’s results show that the proposed protocol provides better security and also better performance in terms of packet delivery than the conventional AODV in the presence of Black holes with minimal additional delay and Overhead.

Shahid Shehzad Bajwa et.al [23] In current research self-security management is of high interest. Wireless Ad hoc networks use mobile nodes to enable communication outside wireless communication zone. Attacks on wireless ad hoc network routing protocols disrupt network performance and reliability. In active black hole attacks on wireless networks; malicious nodes advertise the shortest path through the, between source and destination, which leads to modifications in routing table and packet loss. The proposed Grouped Black Hole Attack Security Model (GBHASM) stops grouped malicious nodes to advertise the shortest path through them to source and destination hence eliminating routing table modifications and packet loss.

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Ming-Yang Su, Kun-Lin Chiang et.al [24] In this paper, IDS (Intrusion Detection System) nodes are deployed in MANETs in order to mitigate black hole attacks. The IDS nodes must be set in sniff mode in order to perform the so-called ABM (AntiBlackhole Mechanism) function, which is mainly used to estimate a suspicious value of a node according to the abnormal difference between the routing messages transmitted from the node. When a suspicious value exceeds a threshold, an IDS nearby will broadcast a block message, informing all nodes on the network, asking them to cooperatively isolate the malicious node.

Saurabh Gupta et.al. [25] The lack of centralised infrastructure in adhoc network makes it vulnerable to various attacks.

MANET routing disrupts if the participating node start performing malicious activity instead of the intended function. One such specific attack is a blackhole attack in which malicious node falsely claiming itself as having the fresh and shortest path to the destination. In this paper, the authors have proposed a protocol for avoiding blackhole attack without the constraint of special hardware and dependency on physical medium of wireless network. BAAP forms link disjoint multi-path during path discovery to provide greater path selection in order to avoid malicious nodes in the path using legitimacy table maintained by each node in the network. Non-malicious nodes gradually isolate the blackhole nodes based on the values collected in their legitimacy table and avoid them while making path between source and destination.

Prachee N. Patil et.al. [26] The Dynamic Source Routing (DSR) algorithm makes use of caching concepts to store all newly constructed routing paths in mobile ad hoc networks. Route caching is aggressively used by DSR. By virtue of source routing, it is possible to cache every overhead route without causing loops. Basically the forwarding nodes are caching source route from the packet and forwards it for future use. Also, the destination replies to all requests. Thus the source learns many alternate routes to the destination that are cached. Here authors of this proposed a new approach for blackhole prevention in DSR based on route caching. In this approach, once the blackhole node is detected in MANET during the path construction, they pass the blackhole node id to path function of DSR. In this function, paths are ready to be added in route cache, however priory to add each path in route cache is decided by parsing these paths for presence of blackhole node id. This process makes use of normal time of caching process only. In this paper, we propose the cache based blackhole prevention algorithm for DSR routing protocols in MANETs III. EXISTING PROBLEM A malicious node is the one which have intention of getting access to the useful information in the network and not letting the packets reaching the desired user. The malicious node causes packet drop in the network. For packet dropping attack to occur malicious node comes in the route from source to destination. Whenever the useful data comes to it, the malicious node simply drops the packets without forwarding them to the destination, sometimes it selectively drops some of the packets. In this way the malicious node can easily misroute lot of network traffic to itself and could cause an attack to the network with very little effort on it. These malicious nodes may work as a group. Malicious nodes can drop the control packets also. Control packets are those which are broadcasted in the network in the initial stage whenever source wants to send data to the destination and broadcasts the route request packets to form the route. In the case of control packets dropping, the malicious nodes make use of route error messages, which are sent to the source in case of link breakage. Whenever, malicious node will receive the route error message to be forwarded to the source it does not forward the packets. So the information about link breakage is not received by the source, as a result source keeps on sending the data packets using same route. Those packets are received by the malicious node which simply drops the packets.

A black hole is a node that always responds positively with a RREP message to every RREQ, even though it does not really have a valid route to the destination node. Since a black hole node does not have to check its routing table, it is the first to respond to the RREQ in most cases. Then the source routes data through the black hole node, which will drop all the data packets it received rather than forwarding them to the destination. In this way the malicious node can easily misroute lot of network traffic to itself and could cause an attack to the network with very little effort on it.

Most of the schemes has detected the black hole attacks efficiently but energy consumption is another issue that mobile ad hoc networks suffer from. This can be seen in cooperative bait detection scheme [10] in which network is flooded with the fake route requests to detect the malicious nodes. The existing scheme [10] would not take into energy efficiency of the network to detect the attacks. So the proposed scheme takes into account issues of security and energy efficiency at the same time.

IV. PROPOSED WORK The proposed work aims at detection of the black hole nodes in the network to reduce the loss of data and at the same time to reduce the energy consumption in the network. This is based on the two things that black hole node modifies the sequence number to very huge value and secondly it always replies positively to every incoming route request packet.

In order to detect the malicious nodes in the network, the source node would store all the route replies received from the node in the cache memory. Then the neighborhood of nodes which has replied to the source node is analyzed. If any node claims that it has path to the destination and same claim is not made by any one of the nodes in its neighborhood, then it can be inferred that the reply of the node is false. In such a scenario the node would be put in the malicious list.

On the other hand, those nodes whose neighbors have also replied to the source node, for them the sequence number would be compared against the average received sequence number which would detect them as malicious when their sequence number is greater than average value.

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Fig. 1: Working of the Proposed Scheme

V. RESULTS The performance of the network was analyzed on the basis of three parameters namely remaining energy of the network, packet delivery ratio and throughput.

Remaining energy = Initial Energy – Energy Consumed Packet Delivery Ratio = Number of packets received / Number of packets sent Throughput = Number of packets received * Size of each packet * 0.008

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Fig. 2: Comparison of PDR

The existing scheme involved detection of the malicious nodes using the reverse tracing scheme[10]. In this few test packets were sent over the path through which route replies came at the source node. The black hole nodes would drop those packets.

The packet delivery ratio which would be defined as ratio of number of packets received to the number of packets sent in the network, was high in the proposed scheme as shown in Fig 2. The values for the proposed scheme was approximately 87 percent and the values for the existing scheme were 82.

Fig. 3: Comparison of Remaining Energy

Although the existing scheme was able to detect the malicious black hole nodes in the network but it had included the initial flooding of bait route request packets in the network. This would lead to more energy consumption of the nodes. This was

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proved by the output of the remaining energy graph shown in Fig 3. The remaining energy of the proposed scheme was approximately 17 Joules while the same value for the bait detection scheme was 14 Joules.

Third parameter, throughput which is defined as amount of data received at the destination node, showed the value of 870 kbps for the proposed scheme and values of 470 kbps for the existing bait detection scheme as shown in Fig 4.

Fig. 4: Comparison of Throughput

VI. CONCLUSION The proposed scheme as well as the existing scheme was implemented in network simulator 2.35. The remaining energy of the proposed scheme was approximately 17 Joules while the same value for the bait detection scheme was 14 Joules. The packet delivery ratio which would be defined as ratio of number of packets received to the number of packets sent in the network, was high in the proposed scheme. The values for the proposed scheme was approximately 87 percent and the values for the existing scheme were 82 percent for the existing scheme. Third parameter, throughput which is defined as amount of data received at the destination node, showed the value of 870 kbps for the proposed scheme and values of 470 kbps for the existing bait detection scheme. Thus it can be concluded that the proposed scheme has detected the black hole attack in energy efficient way and also it was able to increase the packet delivery ratio and throughput of the network.

As a future work, the proposed scheme can be analysed against various other schemes related to detection of such attacks to check the efficiency of the proposed scheme. The bait detection scheme can also be used or modified to detect many other attacks that mobile ad hoc networks suffer from.

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