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

A Lightweight and Attack Resistant Authenticated Routing Protocol for Mobile Adhoc Networks

In mobile ad hoc networks, by attacking the corresponding routing protocol, an attacker can easily disturb the operations of the network. For ad hoc networks, till now many secured routing protocols have been proposed which contains some disadvantages. Therefore security in ad hoc networks is a controversial area till now. In this paper, we proposed a Lightweight and Attack Resistant Authenticated Routing Protocol (LARARP) for mobile ad hoc networks. For the route discovery attacks in MANET routing protocols, our protocol gives an effective security. It supports the node to drop the invalid packets earlier by detecting the malicious nodes quickly by verifying the digital signatures of all the intermediate nodes. It punishes the misbehaving nodes by decrementing a credit counter and rewards the well behaving nodes by incrementing the credit counter. Thus it prevents uncompromised nodes from attacking the routes with malicious or compromised nodes. It is also used to prevent the denial-of-service (DoS) attacks. The efficiency and effectiveness of LARARP are verified through the detailed simulation studies.Comment: 14 Pages, IJWM

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics