9,659 research outputs found
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Feature Engineering for Detection of Wormhole Attacking in Mobile Ad Hoc Networks with Machine Learning Methods
Due to the self-configuring nature of a Mobile Ad Hoc Network (MANET), each node must participate in the routing process, in addition to its other activities. Therefore, routing in a MANET is especially vulnerable to malicious node activity leading to potentially severe disruption in network communications. The wormhole attack is a particularly severe MANET routing threat since it is easy to launch, can be launched in several modes, difficult to detect, and can cause significant communication disruption. In this paper we establish a practice for feature engineering of network data for wormhole attack prevention and detection with intrusion detection methods based on machine learning
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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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
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