Analysis of Black hole Attack in Ad hoc On-Demand Distance Vector (AODV) Routing Protocol : Vehicular Ad-hoc Networks (VANET) Context

In past years, popularity of Mobile Ad hoc Networks has led to the conception of Vehicular Ad hoc Networks. These networks must be highly secure before their implementation in real world. One of the vital aspects of these networks is routing protocol. Most of the protocols in VANET acknowledge all nodes in a network to be genuine by default. But there might be malicious nodes which can make the network vulnerable to various attacks. One such attacks is a black hole attack on AODV routing protocol. Because of its popularity, AODV and black hole attack are taken into consideration for this thesis. The aim of the thesis is to analyze effects of black hole attack on AODV and understand security need of routing protocols in VANET. The experimentation for this thesis was performed with 40, 60 and 80 nodes in network simulator (NS). The performance metrics such as average throughput, end to end delay and packet delivery ratio of each assumed scenarios under blackhole attack and with prevention method are calculated. The obtained calculations are compared to analyze the network performance of AODV. The results from the simulator demonstrate that overall network performance of AODV increased with black hole prevention algorithm in comparison to AODV under black hole attack only. Out of all the performance metrics that are used to analyze the network performance, the average throughput of AODV is significantly increased by 21 percent (approximately) when the mitigation algorithm is applied. The prevention approach used for the thesis can make AODV perform better against black hole attack. However, this approach is limited to a small to medium sized networks only

A Prey-Predator Defence Mechanism For Ad Hoc On-Demand Distance Vector Routing Protocol

This study proposes a nature-based system survivability model. The model was simulated, and its performance was evaluated for the mobile ad hoc wireless networks. The survivability model was used to enable mobile wireless distributed systems to keep on delivering packets during their stated missions in a timely manner in the presence of attacks. A prey-predator communal defence algorithm was developed and fused with the Ad hoc On-demand Distance Vector (AODV) protocol. The mathematical equations for the proposed model were formulated using the Lotka-Volterra theory of ecology. The model deployed a security mechanism for intrusion detection in three vulnerable sections of the AODV protocol. The model simulation was performed using MATLAB for the mathematical model evaluation and using OMNET++ for protocol performance testing. The MATLAB simulation results, which used empirical and field data, have established that the adapted Lotka-Volterra-based equations adequately represent network defense using the communal algorithm. Using the number of active nodes as a measure of throughput after attack (with a maximum throughput of 250 units), the proposed model had a throughput of 230 units while under attack and the intrusion was nullified within 2 seconds. The OMNET++ results for protocol simulation that use throughput, delivery ratio, network delay, and load as performance metrics with the OMNET++ embedded datasets showed good performance of the model, which was better than the existing conventional survivability systems. The comparison of the proposed model with the existing model is also presented. The study concludes that the proposed communal defence model was effective in protecting the entire routing layer (layer 2) of the AODV protocol when exposed to diverse forms of intrusion attacks

A survey on mitigation methods to Black hole Attack on AODV routing protocol

AODV is a routing protocol that is designed for MANETs and it is using the on-demand routing method to establish the routes between nodes. The main benefit of this protocol is establishment of desired routes to destination when the source node requires and it keeps the routes as long as they are needed. The black hole attack is a common attack that can be accrued in AODV protocols. In this kind of attack, the attacker uses of one or more malicious nodes which advertise themselves in the network by setting a zero metric to all the destinations that causes all the nodes toward the data packets to these malicious nodes. The AODV is vulnerable against black hole attacks due to having network centric property, where all the nodes have to share their routing tables for each other. In this paper, we present the survey of existing mitigation methods that have been proposed to secure AODV. Keywords: Mobile Ad hoc Network (MANET); Black hole attack; Cooperative Black hole attack; Ad-hoc On-demand Distance Vector (AODV)

Comparative and Analytical Study towards Mitigation of Gray hole Attacks in VANET

Vehicular Adhoc Network is a type of (MANET) Mobile Adhoc Network that enables vehicles on the road to intelligently interact and communicate with other vehicle and road side infrastructure unit. It is prone to several type of attacks and one such attack is Grayhole attack. Gray hole attack is one of the attack on routing specification in which malicious node selectively drops packets coming from the source. Due to lack of security in Adhoc on Demand Distance Vector (AODV) routing protocol, Grayhole attack disrupts the performance of network and render communication impossible. This paper reviews various attacks in VANET including Grayhole attack on AODV routing protocol and provides a survey of existing defence approaches to mitigate them

Innovative Technique to Detect and Prevent Malicious Nodes in AOMDV against Blackhole Attacks in MANET for Increase the Network Efficiency

The Ad hoc on-demand multipath distance vector (AOMDV) routing protocol is one type of reactive routing protocol used in MANET. It is designed on top of the AODV routing protocol, so it utilizes the features of the AODV protocol. The MANET is a wireless ad hoc network without any physical infrastructure; all nodes can be moved across the network, and connections are made between them as needed simply with the help of RREQ, RREP, and RERR packets. Because the network is dynamic, nodes can quickly join and depart anytime. So far, no security threats have been caused by this feature. The blackhole attack is one type of active and dangerous attack in MANET. In this attack, the attackers use the AOMDV flaw to demonstrate their bad intent, causing data loss and decreasing network performance. Many studies have been done on various detection and prevention methods to prevent blackhole attacks. But it still goes on. To improve network performance against black hole attacks, this study offers a dynamic threshold value with multiple paths technique approach on AOMDV; it will be demonstrated in Network Simulator 2

Protocol for Multiple Black Hole Attack Avoidance in Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) form a new wireless networking paradigm with unique characteristics that give them appreciated interest in a vast range of applications. However, many challenges are facing MANETs including security, routing, transmission range, and dynamically changing topology with high node mobility. Security is considered as the main obstacle for the widespread adoption of MANET applications. Black hole attack is a type of DoS attack that can disrupt the services of the network layer. It has the worst malicious impact on network performance as the number of malicious nodes increases. Several mechanisms and protocols have been proposed to detect and mitigate its effects using different strategies. However, many of these solutions impose more overhead and increase the average end-to-end delay. This chapter proposes an enhanced and modified protocol called “Enhanced RID-AODV,” based on a preceding mechanism: RID-AODV. The proposed enhancement is based on creating dynamic blacklists for each node in the network. Each node, according to criteria, depends on the number of mismatches of hash values of received packets as compared with some threshold values, and the sudden change in the round-trip time (RTT) can decide to add or remove other nodes to or from its blacklist. The threshold is a function of mobility (variable threshold) to cancel the effect of normal link failure. Enhanced RID-AODV was implemented in ns-2 simulator and compared with three previous solutions for mitigating multiple black hole attacks in terms of performance metrics. The results show an increase in throughput and packet delivery ratio and a decrease in end-to-end delay and overhead ratio

Utilizing the protected learning calculation method to forestall the Black Hole Attacks in Mobile ad-hoc networks

Mobile Ad-hoc Networks (MANETs) are a gathering of portable hosts which speak with each other with no focal system power or altered foundation. Because of its attributes like portability furthermore, heterogeneity ad-hoc networks are more defenseless to assaults. Black hole is an assault where every one of the bundles sent to assailant hub, by neighboring hubs, are dropped purposefully. In this thesis, we propose a secure learning calculation method which intends to identify and securing the black hole by considering the bundle drop reasons in needless mode. Presented AODV direction convention is adjusted to distinguish and securing the black hole assault. The investigation results demonstrate that our proposed calculation secure the AODV against black hole assault in MANETs