An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors

In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. In this paper we build on our previous work and investigate the use of four concepts: (1

Analysis of a Reputation System for Mobile Ad-Hoc Networks with Liars

The application of decentralized reputation systems is a promising approach to ensure cooperation and fairness, as well as to address random failures and malicious attacks in Mobile Ad-Hoc Networks. However, they are potentially vulnerable to liars. With our work, we provide a first step to analyzing robustness of a reputation system based on a deviation test. Using a mean-field approach to our stochastic process model, we show that liars have no impact unless their number exceeds a certain threshold (phase transition). We give precise formulae for the critical values and thus provide guidelines for an optimal choice of parameters.Comment: 17 pages, 6 figure

AIS for Misbehavior Detection in Wireless Sensor Networks: Performance and Design Principles

A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices (nodes) are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We show that (i) mechanism of the AIS have to be carefully applied in order to avoid security weaknesses, (ii) the choice of genes and their interaction have a profound influence on the performance of the AIS, (iii) randomly created detectors do not comply with limitations imposed by communications protocols and (iv) the data traffic pattern seems not to impact significantly the overall performance. We identified a specific MAC layer based gene that showed to be especially useful for detection; genes measure a network's performance from a node's viewpoint. Furthermore, we identified an interesting complementarity property of genes; this property exploits the local nature of sensor networks and moves the burden of excessive communication from normally behaving nodes to misbehaving nodes. These results have a direct impact on the design of AIS for sensor networks and on engineering of sensor networks.Comment: 16 pages, 20 figures, a full version of our IEEE CEC 2007 pape

A bio-inspired object tracking algorithm for minimising power consumption

This electronic document is a 'live' template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. A wireless sensor network (WSN) is a distributed information processing system with the capabilities of sensing, wireless communication and data processing. Individual sensor modules of such a network sense the environment, perform data processing locally and cooperate with other sensors via communication. One very important issue in the deployment of a wireless sensor network is the problem of optimizing energy consumption as these networks may be deployed in places where energy supply are not readily available such as in a seaport container terminal and they are required to work with a long lifespan. The main objective of our research is to develop an algorithm for controlling the power consumption of sensor modules in a wireless sensor network for mobile object tracking. The algorithm determines the actions of an individual sensor module to enter a low power state to conserve energy while maintaining its functionality to track objects and to optimize the lifespan of the entire sensor network by reducing overall power consumption. A control framework and corresponding algorithms for controlling the actions of a sensor is designed and experimentation is done to show its efficiency in controlling power consumption of a sensor network. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Green Circuits and Systems (ICGCS 2010), Shanghai, China, 21-23 June 2010. In Proceedings of ICGCS, 2010, p. 355-36

Exploiting the plasticity of primary and secondary response mechanisms in artificial immune systems

One of the key properties of the human immune system is to detect the presence of pathogens, and as such there are numberous immune algorithm which perform anomaly detection and pattern recognition. An additional facet of the human immune system is that an appropriate effector response is generated upon the detection of a pathogen - a process termed the primary response. Additionally the human immune system has the ability to remember the appropriate response to a particular pathogen - the secondary response. The complex orchestration of both the primary and secondary responses are highly dynamic - described in immunological terms as plastic. In this paper we present an overview of the the exact mechanisms of the generation of a T-helper cell primary response and the mechanisms by which it instructs secondary responses and discuss how this can be computationally useful in artificial immune system development

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time