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

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Quarantine region scheme to mitigate spam attacks in wireless sensor networks

The Quarantine Region Scheme (QRS) is introduced to defend against spam attacks in wireless sensor networks where malicious antinodes frequently generate dummy spam messages to be relayed toward the sink. The aim of the attacker is the exhaustion of the sensor node batteries and the extra delay caused by processing the spam messages. Network-wide message authentication may solve this problem with a cost of cryptographic operations to be performed over all messages. QRS is designed to reduce this cost by applying authentication only whenever and wherever necessary. In QRS, the nodes that detect a nearby spam attack assume themselves to be in a quarantine region. This detection is performed by intermittent authentication checks. Once quarantined, a node continuously applies authentication measures until the spam attack ceases. In the QRS scheme, there is a tradeoff between the resilience against spam attacks and the number of authentications. Our experiments show that, in the worst-case scenario that we considered, a not quarantined node catches 80 percent of the spam messages by authenticating only 50 percent of all messages that it processe

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated