Self-driving vehicles are known to be vulnerable to different types of attacks due to the type of communication systems which are utilized in these vehicles. These vehicles are becoming more reliant on external communication through vehicular ad hoc networks. However, these networks contribute new threats to self-driving vehicles which lead to potentially significant problems in autonomous systems. These communication systems potentially open self-driving vehicles to malicious attacks like the common Sybil attacks, black hole, Denial of Service, wormhole attacks and grey hole attacks. In this paper, an intelligent protection mechanism is proposed, which was created to secure external communications for self-driving and semi-autonomous cars. The protection mechanism is based on the Proportional Overlapping Scores method, which allows to decrease the number of features found in the Kyoto benchmark dataset. This hybrid detection system uses Back Propagation neural networks to detect Denial of Service (DoS), a common type of attack in vehicular ad hoc networks. The results from our experiment revealed that the proposed intrusion detection has the ability to identify malicious vehicles in self-driving and even in semi-autonomous vehicles.

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.

Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

The emergence of self-driving and semi self-driving vehicles which form vehicular ad hoc networks (VANETs) has attracted much interest in recent years. However, VANETs have some characteristics that make them more vulnerable to potential attacks when compared to other networks such as wired networks. The characteristics of VANETs are: an open medium, no traditional security infrastructure, high mobility and dynamic topology. In this paper, we build an intelligent intrusion detection system (IDS) for VANETs that uses a Proportional Overlapping Scores (POS) method to reduce the number of features that are extracted from the trace file of VANET behavior and used for classification. These are relevant features that describe the normal or abnormal behavior of vehicles. The IDS uses Artificial Neural Networks (ANNs) and fuzzified data to detect black hole attacks. The IDSs use the features extracted from the trace file as auditable data to detect the attack. In this paper, we propose hybrid detection (misuse and anomaly) to detect black holes.

© 2017 Chongqing University of Posts and Telecommuniocations Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS) to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS) and black hole attacks on vehicular ad hoc networks (VANETs). The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection

Vehicular ad hoc networks play an important role in the success of a new class of vehicles, i.e. self-driving and semi self-driving vehicles. These networks provide safety and comfort to passengers, drivers and vehicles themselves. These vehicles depend heavily on external communication to predicate the surrounding environment through the exchange of cooperative awareness messages (CAMs) and control data. VANETs are exposed to many types of attacks such as black hole, grey hole and rushing attacks. In this paper, we present an intelligent Intrusion Detection System (IDS) which relies on anomaly detection to protect external communications from grey hole and rushing attacks. Many researchers agree that grey hole attacks in VANETs are a substantial challenge due to them having their distinct types of behaviour: normal and abnormal. These attacks try to prevent transmission between vehicles and roadside units and have a direct and negative impact on the wide acceptance of this new class of vehicles. The proposed IDS is based on features that have been extracted from a trace file generated in a network simulator. In our paper, we used a feed-forward neural network and a support vector machine for the design of the intelligent IDS. The proposed system uses only significant features extracted from the trace file. Our research, concludes that a reduction in the number of features leads to a higher detection rate and a decrease in false alarms.