Security Improvements for Connected Vehicles Position-Based Routing

The constant growing on the number of vehicles is increasing the complexity of traffic in urban and highway environments. It is paramount to improve traffic management to guarantee better road usage and people’s safety. Through efficient communications, Vehicular Ad hoc Networks (VANETs) can provide enough information for traffic safety initiatives, daily traffic data processing, and entertainment information. However, VANETs are vulnerable to malicious nodes applying different types of net-work attacks, where an attacker can, for instance, forge its position to receive the data packet and drop the message. This can lead vehicles and authorities to make incorrect assumptions and decisions, which can result in dangerous situations. Therefore, any data dissemination protocol designed for VANET should consider security issues when selecting the next-hop forwarding node. In this paper, we propose a security scheme designed for position-based routing algorithms, which analyzes nodes position, transmission range, and hello packet interval. The scheme deals with malicious nodes performing network attacks, faking their positions forcing packets to be dropped. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) to compare our proposed scheme integrated with two well-known position-based algorithms. The results were collected in an urban Manhattan grid environment varying the number of nodes, the number of malicious nodes, as well as the number of source-destination pairs. The results show that the proposed security scheme can successfully improve the packet delivery ratio while maintaining low average end-to-end delay of the algorithms.

Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles

Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput

An Efficient Black-hole and Worm-hole Attacks Resilient Scheme for Cloud and Fog-Assisted Internet of Vehicles

The Internet of Vehicles IoV is a distributed network that supports the use of data created by connected cars and vehicular ad-hoc networks VANETs for real-time communication among the vehicles and other infrastructures in the network Although IoV increases safety and efficient information exchange in transportation its inter-connectivity exposes the vehicles and the people to different cyber-attacks such as black-hole and worm-hole which are capable of disrupting the network In this paper we identify the black-hole and worm-hole attacks as the major security threats to the IoV technology We then propose periodic-time slicing and trust factor approaches to detect and prevent a black-hole attack and a cryptography procedure to prevent other IoV related cyber-attack

A hierarchical detection method in external communication for self-driving vehicles based on TDMA

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms

Secure Position-Based Routing for VANETs

Vehicular communication (VC) systems have the potential to improve road safety and driving comfort. Nevertheless, securing the operation is a prerequisite for deployment. So far, the security of VC applications has mostly drawn the attention of research efforts, while comprehensive solutions to protect the network operation have not been developed. In this paper, we address this problem: we provide a scheme that secures geographic position-based routing, which has been widely accepted as the appropriate one for VC. Moreover, we focus on the scheme currently chosen and evaluated in the Car2Car Communication Consortium (C2C-CC). We integrate security mechanisms to protect the position-based routing functionality and services (beaconing, multi-hop forwarding, and geo-location discovery), and enhance the network robustness. We propose defense mechanisms, relying both on cryptographic primitives, and plausibility checks mitigating false position injection. Our implementation and initial measurements show that the security overhead is low and the proposed scheme deployable

Analysis of cyber risk and associated concentration of research (ACR)² in the security of vehicular edge clouds

Intelligent Transportation Systems (ITS) is a rapidly growing research space with many issues and challenges. One of the major concerns is to successfully integrate connected technologies, such as cloud infrastructure and edge cloud, into ITS. Security has been identified as one of the greatest challenges for the ITS, and security measures require consideration from design to implementation. This work focuses on providing an analysis of cyber risk and associated concentration of research (ACR2). The introduction of ACR2 approach can be used to consider research challenges in VEC and open up further investigation into those threats that are important but under-researched. That is, the approach can identify very high or high risk areas that have a low research concentration. In this way, this research can lay the foundations for the development of further work in securing the future of ITS

Hybrid intrusion detection in connected self-driving vehicles

Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems render self-driving vehicles vulnerable to many types of malicious attacks, such as Sybil attacks, Denial of Service (DoS), black hole, grey hole and wormhole attacks. In this paper, we propose an intelligent security system designed to secure external communications for self-driving and semi self-driving cars. The proposed scheme is based on Proportional Overlapping Score (POS) to decrease the number of features found in the Kyoto benchmark dataset. The hybrid detection system relies on the Back Propagation neural networks (BP), to detect a common type of attack in VANETs: Denial-of-Service (DoS). The experimental results show that the proposed BP-IDS is capable of identifying malicious vehicles in self-driving and semi self-driving vehicles