دانلود رایگان مقاله نظریه بازی مبتنی بر چارچوب تشخیص نفوذ چند لایه برای VANET

Abstract

Vehicular Ad-hoc Networks (VANETs) are vulnerable to various type of network attacks like Blackhole attack, Denial of Service (DoS), Sybil attack etc. Intrusion Detection Systems (IDSs) have been proposed in the literature to address these security threats. However, high vehicular mobility makes the process of formulating an IDS framework for VANET a difficult task. Moreover, VANETs operate in bandwidth constrained wireless radio spectrum. Therefore, IDS frameworks that introduce significant volume of IDS traffic are not suitable for VANETs. In addition, dynamic network topology, communication overhead and scalability to higher vehicular density are some other issues that needs to be addressed while developing an IDS framework for VANETs. This paper aims to address these issues by proposing a multi-layered game theory based intrusion detection framework and a novel clustering algorithm for VANET. The communication overhead of the IDS is reduced by using a set of specification rules and a lightweight neural network based classifier module for detecting malicious vehicles. The volume of IDS traffic is minimized by modeling the interaction between the IDS and the malicious vehicle as a two player non-cooperative game and adopting a probabilistic IDS monitoring strategy based on the Nash Equilibrium of the game. Finally, the proposed clustering algorithm maintains the stability of the IDS framework, which ensures that the framework scales up well to networks with higher vehicular densities. Simulation results show that the proposed framework achieves high accuracy and detection rate across wide range of attacks, while at the same time minimizes the overall volume of intrusion detection related traffic introduced into the vehicular network.

5. Conclusion and future work

In this paper, a novel clustering algorithm, a CH election algorithm and a game theory based IDS framework for VANETs have been proposed. The proposed clustering algorithm ensures the stability of the IDS framework by generating stable vehicular clusters with enhanced connectivity among member vehicles. CH and agent nodes election algorithms are then executed to elect the CH and a set of agent nodes for each cluster. The proposed IDS framework uses the the agent nodes, the CHs and the RSUs operating at three different levels of the vehicular network to carry out the intrusion detection operation in a distributed manner. The framework uses a set of specification rules based on the Packet Drop Rate (PDR), Packet Forwarding Rate (PFR), Receive Signal Strength Indicator (RSSI) and Duplicate Packet Rate (DPR) values of the vehicles, along with a lightweight neural network based classifier module for detecting malicious vehicles. In addition, the proposed framework minimizes the volume of IDS traffic introduced into the vehicular network by modeling the interaction between the IDS and the malicious vehicle as a two player non-cooperative game, and by adopting a probabilistic IDS monitoring strategy based on the Nash Equilibrium of the game.

The clustering algorithm proposed in the paper only considers the vehicles that stopped at the traffic signal and vehicles approaching the road intersection point with relatively low speed. For our future work, we envisage to formulate a dynamic clustering algorithm, which takes into account the vehicles in motion for generating stable vehicular cluster. We also aim to improve the DR and minimize the FAR rate of the proposed IDS framework by analyzing the performances of various other classifiers using Support Vector Machine (SVM), Decision Tree, Logistic Regression, Multilayer Perceptron (MLP) etc. Additionally, we also aim to extend and implement the proposed IDS framework to various other networks like Software Defined Network (SDN), Delay Tolerant Network etc., in future.