An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.

Ethnicity, Social Capital and the Internet

This article explores web sites developed to express the interests and experiences of young Chinese people in Britain. Drawing on content analysis of site discussions and dialogues with site users, we argue these new communicative practices are best understood through a reworking of the social capital problematic. Firstly by recognising the irreducibility of Internet-mediated connections to the calculative instrumentalism underlying many applications of social capital theory. Secondly, by providing a more differentiated account of social capital. The interactions we explore comprise a specifically “second generation” form of social capital, cutting across the binary of bonding and bridging social capital. Thirdly judgement on the social capital consequences of Internet interactions must await a longer-term assessment of whether British Chinese institutions emerge to engage with the wider polity.

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.