Towards a secure ITS: Overview, challenges and solutions

Abstract

Intelligent Transportation Systems (ITS), the heart of the new revolution of smart transport, has evolved from the well-known Vehicular Ad-hoc Networks (VANETs) to become the Internet of Vehicles (IoV). In fact, the increase in the number of vehicles and the newly born technologies have stimulated the new Internet of Vehicles (IoV) or the Internet of Cars. In general, ITS aims at ensuring better traffic efficiency and reducing road accidents. However, due to different limitations and issues, these systems suffer from different security and privacy vulnerabilities. In fact, they are both vulnerable to various types of security and privacy attacks that may result in life-endangering situations. As a result, several solutions were presented to achieve the required levels of security and confidentiality. In this paper, an overview of ITS is presented stating the reasons behind the evolution from Vanet to IoV. Then, the main threats/attacks that threaten ITS are classified according to their (1) security impact and according to the (2) network layer(s) they affect. Solutions for each attack are also well presented. In addition, a security and performance evaluation and summary tables are presented to provide an overview of these surveyed solutions.

An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs

“…The highly dynamic, open-access medium, distributed infrastructure, and protocol designing issues have made VANETs vulnerable to many security attacks, such as a denial of service (DoS) attack, Sybil attack, wormhole attack, flooding attack, impersonation attack, jellyfish attack, GHA, and BHA [2,16,24,25]. Due to the presence of these attacks, the applications and services of VANETs can be compromised.…”

“…The accuracy metric measures how accurate the model is in detecting malicious or normal node behavior. It is defined as the percentage of all those correctly predicted instances to the overall instances calculated using Equation (25). In order to maximize the performance of a model, FPR and FNR must be minimized, while TPR and TNR must be maximized.…”

“…In order to maximize the performance of a model, FPR and FNR must be minimized, while TPR and TNR must be maximized. Accuracy = TP + TN TP + TN + FP + FN (25) Table 8 demonstrates one of the experiments of the proposed DPBHA performed with a total number of 75 nodes comprising 69 normal and 6 malicious nodes. After performing the simulation, the model successfully detected five out of six targeted malicious nodes, giving an 83.3% sensitivity.…”

“…For instance, the source node is always unaware of the intended destination. Such features of AODV make VANETs more vulnerable to various security attacks, such as a wormhole attack, black hole attack (BHA), and gray hole attack (GHA) [2,16,24,25].…”

Security Architecture for Swarms of Autonomous Vehicles in Smart Farming

“…How can we avoid alterations in the content of the messages? It is critical to analyse the vulnerabilities of the system and to provide a security design architecture that considers confidentiality and integrity of the data and messages, authentication and authorisation of the users or systems, and non-repudiation of data [8].…”