This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune system. The first part of this thesis examines the biological innate immune system, which controls the adaptive immune system. The importance of the innate immune system suggests that AISs should also incorporate models of the innate immune system as well as the adaptive immune system. This thesis presents and discusses a number of design principles for AISs which are modelled on both innate and adaptive immunity. These novel design principles provided a structured framework for developing AISs which incorporate innate and adaptive immune systems in general. These design principles are used to build a software system which allows such AISs to be implemented and explored.

Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that Artificial Immune Systems are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner. Keywords artificial immune systems, immunological modelling, mathematical modelling, computational modelling, applications of artificial immune systems, immune inspired computing, immunocomputing, computational immunology 1

Abstract In this position paper, we outline a vision for a new type of engineering: immuno-engineering, that can be used for the development of biologically grounded and theoretically understood Artificial Immune Systems (AIS). We argue that, like many bio-inspired paradigms, AIS have drifted somewhat away from the source of inspiration. We also argue that through an interdisciplinary approach, it is possible to exploit the underlying biology for computation in a way that, as yet, has not been achieved. Immuno-engineering will not only allow for the potential development of more powerful AIS, but allow for feed back to biology from computation. 1

The natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation – that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.

The growth of intelligent intrusion and diverse attack techniques in network systems stimulate computer scientists and mathematical researchers to challenge the dangers of intelligent attacks. In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. Classification techniques using k-nearest neighbor (k-NN) or Gaussian Mixture (GMM) almost have the common sense that they believe the neighboring data. The algorithm tested use KDD Cup dataset and the result shows a significant improvement in detection accuracy and reducing the false alerts.

This paper presents an approach for intrusion prevention and self-healing system for network security. The approach detects, prevents, and heals harmful events, which are the actual reasons for damage of any of the system’s components. The proposed approach explores the design and implementation of artificial immune systems (AISs) inspired by the human immune system, in solving the problem of network security. A novel approach for network security based on the combination of biological intrusion prevention (IP) and self-healing concepts is proposed. The system integrates an artificial immune intrusion prevention system for network security inspired by the immunology theory known as danger theory and adaptive immune system. The approach is based upon data inspired by the human immune system (HIS), which applied to the autonomous defense system. The present model looks at the danger model and its application to attack defense in order to create a fully decentralized model. The intrusion prevention system (IPS) analyzes the behavior of system processes and network traffic to detect harmful events. Abnormal behaviors are the actual reason for damage of any of the system’s components. The detection of the damage caused by different types of malicious events or attack profiles is used to trigger the self- healing (SH) mechanism. This system is autonomous and enhances the fault repair and system recovery.

(AISs) have appeared as an approach dealing with security systems and classification problems. This paper briefly surveys AIS basic concepts, features and principles, approaches and applications.