Martin Drozda | Searcharxiv

arXiv2010

An Immuno-Inspired Approach to Misbehavior Detection in Ad Hoc Wireless Networks

We propose and evaluate an immuno-inspired approach to misbehavior detection in ad hoc wireless networks. Node misbehavior can be the result of an intrusion, or a software or hardware failure. Our approach is motivated by co-stimulatory signals present in the Biological immune system. The results show that co-stimulation in ad hoc wireless networks can both substantially improve energy efficiency of detection and, at the same time, help achieve low false positives rates. The energy efficiency improvement is almost two orders of magnitude, if compared to misbehavior detection based on watchdogs. We provide a characterization of the trade-offs between detection approaches executed by a single node and by several nodes in cooperation. Additionally, we investigate several feature sets for misbehavior detection. These feature sets impose different requirements on the detection system, most notably from the energy efficiency point of view.

cs.NI↗

arXiv2009

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.

cs.NI↗