A Review of Artificial Immune System Based Security Frameworks for MANET <s> Introduction <s> Abstract The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems. <s> BIB001 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Introduction <s> Biology gives us numerous examples of self-assertional systems whose essence does not precede their existence but is rather revealed through it. Immune system is one of them. The fact of behaving in order not only to satisfy external constraints as a pre-fixed set of possible environments and objectives, but also to satisfy internal "viability" constraints justifies a sharper focus. Adaptability, creativity and memory are certainly interesting "side-effects" of such a tendency for self-consistency. However in this paper, we adopted a largely pragmatic attitude attempting to find the best hybridizing between the biological lessons and the engineering needs. The great difficulty, also shared by neural net and GA users, remains the precise localisation of the frontier where the biological reality must give way to a directed design. <s> BIB002 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Introduction <s> The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. The authors describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. > <s> BIB003
A Mobile Ad hoc Network (MANET) is a collection of wireless mobile devices that form a network without the need for fixed infrastructure except for connections to fixed-wire digital networks. The mobile hosts are not bound to any centralized control facility or system such as base stations or mobile cellular switching centers. Although this offers unrestricted mobility and connectivity to the users, the onus of network management is now entirely a matter for the nodes that form the network. Due to the limited transmission range of many wireless mobile devices, multiple network hops may be needed for one node to exchange data with another across the network. In such a mobile network each node operates not only as a host but also as a relay node and router, forwarding packets for other mobile nodes in the network that may not be within the direct wireless transmission range of each other. Each node communicates using an ad hoc routing protocol that allows it to discover multi-hop paths through the network to any other node. The idea of an ad hoc network is sometimes called infrastructure-less networking since the mobile nodes in the network dynamically establish routing among themselves to form their own network on the fly. Routing capability is a basic functionality for multi-hop MANETs where individual nodes act both as host and as a relay or router that forwards packets for nodes that are not in transmission range of destination nodes. Several route discovery algorithms have been proposed in the literature. The concept of ad hoc networks is attractive for the following reasons: a) ease of deployment; b) speed of deployment; and c) decreased dependence on fixed infrastructure. Ad hoc networks have several salient characteristics and features that give rise to the following challenges: 1) Dynamic topologies. Nodes are free to move arbitrarily, thus the network topology, which is typically multi-hop, may change randomly and rapidly at unpredictable times and may consist of both bidirectional and unidirectional links. 2) Limited physical security. Mobile wireless networks are prone to security threats with an increased possibility of eavesdropping, spoofing and denial of service attacks. Existing fixed link security techniques are often applied within wireless networks to reduce security threats. As an inherent benefit the decentralized nature of network control in MANET provides additional robustness against the single point of failure present in centralized approaches. 3) Energy constrained operation. Some or all of the nodes in ad hoc networks may rely on batteries or other intermittent or exhaustible means for their energy. For these nodes an important system design criteria is energy conservation. Artificial immune system is a branch of artificial intelligence inspired by the principles of invertebrate immune systems. The algorithms in artificial immune system adapt the immune system features of learning and memory to solve a problem. This area of research makes an attempt to bridge the gap between immunology and engineering. Immune system properties are of great interest to engineers and computer scientists. The evolution of AIS has its roots in the work of Farmer, Packard and Perelson BIB001 . The work carried out by Bersini and Varela BIB002 was influential and helped in bridging the gap between computing and immunology. Bersini concentrated on how to build models that mimic immune system memory properties. Forrest BIB003 focussed on network intrusion detection thereby concentrating more on the ability of immune systems to discriminate between self and non-self. These pioneering works led to a great deal of further research on the application of immune inspired approaches to computer security. This paper is organised into five sections. An insight into MANET and danger theory is explained in Section II. Section III provides a brief introduction to artificial immune systems whilst Section IV provides details on selected AIS algorithms and their applications. Section V inspects work carried out so far in MANET using AIS.