A Survey on VANET Intrusion Detection Systems

In recent years, the security issues on Vehicular ad hoc networks (VANETs) have become one of the primary concerns. The VANET is inherently very vulnerable to attacks than wired network because it is characterized by high mobility, shared wireless medium and the absence of centralized security services offered by dedicated equipment such as firewalls and authentication servers. Attack countermeasures such as digital signature and encryption, can be used as the first line of defense for reducing the possibilities of attacks. However, these techniques have limited prevention in general, and they are designed for a set of known attacks. They are unlikely to avoid most recent attacks that are designed to circumvent existing security measures. For this reason, there is a need of second technique to “detect and notify” these newer attacks, i.e. “intrusion detection”. This article aims to present and classify current techniques of Intrusion Detection System (IDS) aware VANETs.

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Enhancing vehicular ad-hoc networks security using intrusion detection system techniques

Proceedings of the 4th International Conference on Smart City Applications - SCA '19 ◽

10.1145/3368756.3369017 ◽

2019 ◽

Author(s):

Meriem Houmer ◽

Moulay Lahcen Hasnaoui

Keyword(s):

Intrusion Detection ◽

Ad Hoc Networks ◽

Intrusion Detection System ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Detection System ◽

Hoc Networks

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Intrusion Detection in Vehicular Ad-Hoc Networks on Lower Layers

Transportation Systems and Engineering ◽

10.4018/978-1-4666-8473-7.ch010 ◽

2015 ◽

pp. 192-220

Author(s):

Chong Han ◽

Sami Muhaidat ◽

Ibrahim Abualhaol ◽

Mehrdad Dianati ◽

Rahim Tafazolli

Keyword(s):

Intrusion Detection ◽

Ad Hoc Networks ◽

Intelligent Transportation Systems ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Transportation Systems ◽

Detection Systems ◽

Channel Errors ◽

Major Attention ◽

Hoc Networks

Vehicular Ad-Hoc Networks (VANETs) are a critical component of the Intelligent Transportation Systems (ITS), which involve the applications of advanced information processing, communications, sensing, and controlling technologies in an integrated manner to improve the functionality and the safety of transportation systems, providing drivers with timely information on road and traffic conditions, and achieving smooth traffic flow on the roads. Recently, the security of VANETs has attracted major attention for the possible presence of malicious elements, and the presence of altered messages due to channel errors in transmissions. In order to provide reliable and secure communications, Intrusion Detection Systems (IDSs) can serve as a second defense wall after prevention-based approaches, such as encryption. This chapter first presents the state-of-the-art literature on intrusion detection in VANETs. Next, the detection of illicit wireless transmissions from the physical layer perspective is investigated, assuming the presence of regular ongoing legitimate transmissions. Finally, a novel cooperative intrusion detection scheme from the MAC sub-layer perspective is discussed.

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Artificial Intelligence Based Intrusion Detection System to Detect Flooding Attack in VANETs

Handbook of Research on Network Forensics and Analysis Techniques - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-4100-4.ch006 ◽

2018 ◽

pp. 87-100 ◽

Cited By ~ 3

Author(s):

Mannat Jot Singh Aneja ◽

Tarunpreet Bhatia ◽

Gaurav Sharma ◽

Gulshan Shrivastava

Keyword(s):

Intrusion Detection ◽

Ad Hoc Networks ◽

Intrusion Detection System ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Performance Metrics ◽

Detection System ◽

Delivery Ratio ◽

Flooding Attack ◽

Hoc Networks

This chapter describes how Vehicular Ad hoc Networks (VANETs) are classes of ad hoc networks that provides communication among various vehicles and roadside units. VANETs being decentralized are susceptible to many security attacks. A flooding attack is one of the major security threats to the VANET environment. This chapter proposes a hybrid Intrusion Detection System which improves accuracy and other performance metrics using Artificial Neural Networks as a classification engine and a genetic algorithm as an optimization engine for feature subset selection. These performance metrics have been calculated in two scenarios, namely misuse and anomaly. Various performance metrics are calculated and compared with other researchers' work. The results obtained indicate a high accuracy and precision and negligible false alarm rate. These performance metrics are used to evaluate the intrusion system and compare with other existing algorithms. The classifier works well for multiple malicious nodes. Apart from machine learning techniques, the effect of the network parameters like throughput and packet delivery ratio is observed.

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