This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.

This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

This paper proposes a novel RBF training algorithm based on immune operations for dynamic problem solving. The algorithm takes inspiration from the dynamic nature of natural immune system and locally-tuned structure of RBF neural network. Through immune operations of vaccination and immune response, the RBF network can dynamically adapt to environments according to changes in the training set. Simulation results demonstrate that RBF equalizer based on the proposed algorithm obtains good performance in nonlinear time-varying channels.

With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.

Immune system protects living body from various attacks by foreign invades. Based on the immune response principles, we propose an improved lateral interaction artificial immune system model in this paper. Considering that the different epitopes on the surface of antigen can be recognized by a set of different paratopes expressed on the surface of immune cells, we build a neighborhood set that consists of immune cells with different affinities to a certain input antigen. We update all the weights of the immune cells located in neighborhood set according to their affinities. Simulations on noisy pattern recognition illustrate that the proposed artificial immune system model has stronger noise tolerance ability and is more effective at recognizing noisy patterns than that of our previous models.

In this paper, we propose a self-nonself recognition algorithm based on positive and negative selection used in the developing process of T cells. The anomaly detection algorithm based on negative selection is a representative model among self-recognition method and it has been applied to computer immune systems in recent years. In biological immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigens, or nonself cells. In this paper, we propose a self-recognition algorithm based on the positive selection and also propose a fusion algorithm based on both positive and negative selection. To verify the effectiveness of the proposed system, we show simulation results for detecting some infected data obtained from cell changes and string changes in the self-file.

This paper describes the adaptability of communication software through a biologically-inspired policy coordination. Many research efforts have developed adaptable systems that allow various users or applications to meet their specific requirements by configuring different design and optimization policies. Navigating through many policies manually, however, is tedious and error-prone. Developers face the significant manual and ad-hoc work of engineering an system. In contrast, we propose to provide autonomous adaptability in communication endsystem with OpenWebServer/iNexus, which is both a web server and an object-oriented framework to tailer various web services and applications. The OpenWebServer's modular architecture allows to abstract and maintain a wide range of aspects in a HTTP server, and reconfigure the system by adding, deleting, changing, or replacing their policies. iNexus is a tool for automated policy-based management of OpenWebServer. Its design is inspired by the natural immune system, particularly immune network, a truly autonomous decentralized system. iNexus inspects the current system condition of OpenWebServer periodically, measures the delivered quality of service, and selects suitable set of policies to reconfigure the system dynamically by relaxing constraints between them. The policy coordination process is performed through decentralized interactions among policies without a single point of control, as the natural immune system does. This paper discusses communication software can evolve continuously in the piecemeal way with biological concepts and mechanisms, adapting itself to ever-changing environment.

We described short time span idiotype immune network reactions by rigorous mathematical equations. For each idiotype, we described the temporal changes in concentration of (1) single bound antibody, one of its two Fab arms has bound to the complemental receptor site on the B cell. (2) double bound antibody, both of its two Fab arms have bound to the complemental receptor sites on the B cell and (3) an immune complex which is a product of reaction among the antibodies. Stimulation and secretion processes of an antibody in the idiotype network were described by non linear differential equations characterized by the magnitude of cross-linking of the complemental antibody and B cell receptor. The affinity between the mutually complemental antibody and receptor was described by an weighted affinity matrix. The activating process was expressed by an exponential function with threshold. The rate constant for the linkage of the second Fab arm of an antibody was induced from the molecular diffusion process that was modified by the Coulomb repulsive force. By using reported experimental data, we integrated 60 non linear differential equations for the idiotype immune network to obtain the temporal behavior of concentrations of the species in hour span. The concentrations of the idiotype antibody and immune complex changed synchronously. The influence of a change in one rate constant extended to all the members of the idiotype network. The concentrations of the single bound antibody, double bound antibody and immune complex oscillated as functions of the concentration of the free antibody particularly at its low concentration. By comparing to the reported experimental data, the present computational approach seems to realize biological immune network reactions.

With the rising innovative antigens (such as intruders and viruses) through Internet, reliable security mechanisms are required to perceptively detect and put them down. However, defense techniques of the current host system over Internet may not properly analyze Internet antigens, because trends of attacks are unexpectedly shifted. In this paper, we introduce an Antibody Layer that mediates proper security services based on the biological mechanism to rapidly disclose and remove innovative antigens. The proposed Antibody Layer also employs a new topology called antibody cooperation protocol to support real-time security QoS for one host as well as host alliance.

More than forty thousands computer viruses have appeared so far since the first virus. Six computer viruses on average appear every day. Enormous expansion of the computer network opened a thread of explosive spread of computer viruses. In this paper, we propose a distributed approach against computer virus using the computer network that allows distributed and agent-based approach. Our system is composed of an immunity-based system similar to the biological immune system and recovery system similar to the recovery mechanism by cell division. The immunity-based system recognizes "non-self" (which includes computer viruses) using the "self" information. The immunity-based system uses agents similar to an antibody, a natural killer cell and a helper T-cell. The recover system uses a copy agent which sends an uninfected copy to infected computer on LAN, or receives from uninfected computer on LAN. We implemented a prototype with JAVATM known as a multi-platform language. In experiments, we confirmed that the proposed system works against some of existing computer viruses that can infect programs for MS-DOSTM.