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.Comment: 19 pages, 4 tables, 2 figures, Handbook of Research on Information Security and Assuranc

Mathematical and Statistical Opportunities in Cyber Security

The role of mathematics in a complex system such as the Internet has yet to be deeply explored. In this paper, we summarize some of the important and pressing problems in cyber security from the viewpoint of open science environments. We start by posing the question "What fundamental problems exist within cyber security research that can be helped by advanced mathematics and statistics?" Our first and most important assumption is that access to real-world data is necessary to understand large and complex systems like the Internet. Our second assumption is that many proposed cyber security solutions could critically damage both the openness and the productivity of scientific research. After examining a range of cyber security problems, we come to the conclusion that the field of cyber security poses a rich set of new and exciting research opportunities for the mathematical and statistical sciences

On the Infeasibility of Modeling Polymorphic Shellcode

Polymorphic malcode remains a troubling threat. The ability formal code to automatically transform into semantically equivalent variants frustrates attempts to rapidly construct a single, simple, easily verifiable representation. We present a quantitative analysis of the strengths and limitations of shellcode polymorphism and consider its impact on current intrusion detection practice. We focus on the nature of shellcode decoding routines. The empirical evidence we gather helps show that modeling the class of self-modifying code is likely intractable by known methods, including both statistical constructs and string signatures. In addition, we develop and present measures that provide insight into the capabilities, strengths, and weaknesses of polymorphic engines. In order to explore countermeasures to future polymorphic threats, we show how to improve polymorphic techniques and create a proof-of-concept engine expressing these improvements. Our results indicate that the class of polymorphic behavior is too greatly spread and varied to model effectively. Our analysis also supplies a novel way to understand the limitations of current signature-based techniques. We conclude that modeling normal content is ultimately a more promising defense mechanism than modeling malicious or abnormal content