1,937 research outputs found
Intrusion Detection System for Platooning Connected Autonomous Vehicles
The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks
A Contribution to Secure the Routing Protocol "Greedy Perimeter Stateless Routing" Using a Symmetric Signature-Based AES and MD5 Hash
This work presents a contribution to secure the routing protocol GPSR (Greedy
Perimeter Stateless Routing) for vehicular ad hoc networks, we examine the
possible attacks against GPSR and security solutions proposed by different
research teams working on ad hoc network security. Then, we propose a solution
to secure GPSR packet by adding a digital signature based on symmetric
cryptography generated using the AES algorithm and the MD5 hash function more
suited to a mobile environment
Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms
In this paper we present the design and evaluation of intrusion detection
models for MANETs using supervised classification algorithms. Specifically, we
evaluate the performance of the MultiLayer Perceptron (MLP), the Linear
classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and
the Support Vector Machine (SVM). The performance of the classification
algorithms is evaluated under different traffic conditions and mobility
patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks.
The results indicate that Support Vector Machines exhibit high accuracy for
almost all simulated attacks and that Packet Dropping is the hardest attack to
detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200
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