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

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

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative

A New Scheme for Minimizing Malicious Behavior of Mobile Nodes in Mobile Ad Hoc Networks

The performance of Mobile Ad hoc networks (MANET) depends on the cooperation of all active nodes. However, supporting a MANET is a cost-intensive activity for a mobile node. From a single mobile node perspective, the detection of routes as well as forwarding packets consume local CPU time, memory, network-bandwidth, and last but not least energy. We believe that this is one of the main factors that strongly motivate a mobile node to deny packet forwarding for others, while at the same time use their services to deliver its own data. This behavior of an independent mobile node is commonly known as misbehaving or selfishness. A vast amount of research has already been done for minimizing malicious behavior of mobile nodes. However, most of them focused on the methods/techniques/algorithms to remove such nodes from the MANET. We believe that the frequent elimination of such miss-behaving nodes never allowed a free and faster growth of MANET. This paper provides a critical analysis of the recent research wok and its impact on the overall performance of a MANET. In this paper, we clarify some of the misconceptions in the understating of selfishness and miss-behavior of nodes. Moreover, we propose a mathematical model that based on the time division technique to minimize the malicious behavior of mobile nodes by avoiding unnecessary elimination of bad nodes. Our proposed approach not only improves the resource sharing but also creates a consistent trust and cooperation (CTC) environment among the mobile nodes. The simulation results demonstrate the success of the proposed approach that significantly minimizes the malicious nodes and consequently maximizes the overall throughput of MANET than other well known schemes.Comment: 10 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS July 2009, ISSN 1947 5500, Impact Factor 0.42

Big data traffic management in vehicular ad-hoc network

Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety