Speed Offset Attack Detection in Vehicular Ad-Hoc Networks (VANETs) Using Machine Learning

An integral component of the Intelligent Transportation System (ITS) is the emerging technology called Vehicular ad-hoc network (VANET). VANET allows Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication wirelessly to improve road safety, traffic congestion, and information dissemination. Communication of vehicles in a VANET network is vulnerable to various attacks. Commonly used cryptographic techniques alone are insufficient to ensure and protect vehicle message integrity and authentication from insider attacks. In such cases, additional measures are necessary to ensure the correctness of the transmitted data. Each vehicle in the network periodically broadcasts a basic safety message (BSM) that contains essential status information about a vehicle, such as its position, speed, and heading to other vehicles and Road Side Units (RSU) to report its status. A speed offset attack is where an attacker (misbehaving vehicle) misleads the network by adding an offset value to its actual speed data in each BSM. Such attacks can result in traffic congestion and road accidents; therefore, it is essential to accurately detect and identify such attackers to ensure safety in the network. This research proposes a novel data-centric approach for detecting speed offset attacks using Machine Learning (ML) and Deep Learning (DL) algorithms. Vehicular Reference Misbehavior (VeReMi) Extension Dataset is used for this research. Preliminary results indicate that the proposed model can detect malicious nodes in the network quickly and accurately

Hybrid and multifaceted context-aware misbehavior detection model for vehicular ad hoc network

Vehicular Ad Hoc Networks (VANETs) have emerged mainly to improve road safety and traffic efficiency and provide user comfort. The performance of such networks’ applications relies on the availability of accurate and recent mobility-information shared among vehicles. This means that misbehaving vehicles that share false mobility information can lead to catastrophic losses of life and property. However, the current solutions proposed to detect misbehaving vehicles are not able to cope with the dynamic vehicular context and the diverse cyber-threats, leading to a decrease in detection accuracy and an increase in false alarms. This paper addresses these issues by proposing a Hybrid and Multifaceted Context-aware Misbehavior Detection model (HCA-MDS), which consists of four phases: data-collection, context-representation, context-reference construction, and misbehavior detection. Data-centric and behavioral-detection-based features are derived to represent the vehicular context. An online and timely updated context-reference model is built using unsupervised nonparametric statistical methods, namely Kalman and Hampel filters, through analyzing the temporal and spatial correlation of the consistency between mobility information to adapt to the highly dynamic vehicular context. Vehicles’ behaviors are evaluated locally and autonomously according to the consistency, plausibility, and reliability of their mobility information. The results from extensive simulations show that HCA-MDS outperforms existing solutions in increasing the detection rate by 38% and decreasing the false positive rate by 7%. These results demonstrate the effectiveness and robustness of the proposed HCA-MDS model to strengthen the security of VANET applications and protocols

A survey on vehicular communication for cooperative truck platooning application

Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks.The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

VANSec Attack Resistant VANETs Security Algorithm in terms of Trust Computation Error and Normalized Routing Overhead

open access articleVANET is an application and subclass of MANETs, a quickly maturing, promising, and emerging technology these days. VANETs establish communication among vehicles (V2V) and roadside infrastructure (V2I). As vehicles move with high speed, hence environment and topology change with time. There is no optimum routing protocol which ensures full-pledge on-time delivery of data to destination nodes, and an absolutely optimum scheme design for flawless packet exchange is still a challenging task. In VANETs, accurate and on-time delivery of fundamental safety alert messages (FSAMs) is highly important to withstand against maliciously inserted security threats affectively. In this paper, we have presented a new security-aware routing technique called VANSec. The presented scheme is more immune and resistive against different kinds of attacks and thwarts malicious node penetration attempts to the entire network. It is basically based on trust management approach. The aim of the scheme is to identify malicious data and false nodes. The simulation results of VANSec are compared with already existing techniques called trust and LT in terms of trust computation error (TCE), end-to-end delay (EED), average link duration (ALD), and normalized routing overhead (NRO). In terms of TCE, VANSec is 11.6% and 7.3% efficient than LT and trust, respectively, while from EED comparison we found VANSec to be 57.6% more efficient than trust and 5.2% more efficient than LT. Similarly, in terms of ALD, VANSec provides 29.7% and 7.8% more stable link duration than trust and LT do, respectively, and in terms of NRO, VANSec protocol has 27.5% and 14% lesser load than that of trust and LT, respectively

Neural Network Based Approach for Detecting Location Spoofing in Vehicular Communication

Vehicular Ad hoc Network (VANET) is an evolving subset of MANET. It\u27s deployed on the roads, where vehicles act as mobile nodes. Active security and Intelligent Transportation System (ITS) are integral applications of VANET, which require stable and uninterrupted vehicle-to-vehicle communication technology. VANET, is a type of wireless network, due to which it is quite prone to security attacks. Extremely dynamic connections, sensitive data sharing and time-sensitivity of this network make it a vulnerable to security attacks. The messages shared between the vehicles are the basic safety message (BSM), these messages are broadcasted by each vehicle in the network to report its status to the other vehicles and Road Side Unit (RSU). One common attack is to use position falsification to hamper the roadside safety, leading to road accidents and congestion. Identifying malicious nodes involved in such attacks is crucial to ensure safety in the network. The proposed research presents a neural network based approach for detecting position falsification attacks in VANET. The proposed Deep Learning-based detection of attackers is done using the dataset called Vehicular Reference Misbehavior Dataset (VeReMi). VeReMi dataset provides five classes of attackers, each broadcasting fabricated coordinates concerning the type. This MLP-based model uses resampled single BSM and two consecutive BSM to detect these attacks

Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing

VANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio