Showing papers on "Artificial immune system published in 1999"

Immunity by design: an artificial immune system

Abstract: We describe an artificial immune system (AIS) that is distributed, robust, dynamic, diverse and adaptive. It captures many features of the vertebrate immune system and places them in the context of the problem of protecting a network of computers from illegal intrusions.

Immunity-Based Intrusion Detection System: A General Framework

Abstract: This paper focuses on investigating immunological principles in designing a multi-agent system for intrusion/anomaly detection and response in networked computers. In this approach, the immunity-based agents roam around the machines (nodes or routers), and monitor the situation in the network (i.e. look for changes such as malfunctions, faults, abnormalities, misuse, deviations, intrusions, etc.). These agents can mutually recognize each other's activities and can take appropriate actions according to the underlying security policies. Specifically, their activities are coordinated in a hierarchical fashion while sensing, communicating and generating responses. Such an agent can learn and adapt to its environment dynamically and can detect both known and unknown intrusions. This research is the part of an effort to develop a multi-agent detection system that can simultaneously monitor networked computer's activities at different levels (such as user level, system level, process level and packet level) in order to determine intrusions and anomalies. The proposed intrusion detection system is designed to be flexible, extendible, and adaptable that can perform real-time monitoring in accordance with the needs and preferences of network administrators. This paper provides the conceptual view and a general framework of the proposed system. 1. Inspiration from the nature: Every organism in nature is constantly threatened by other organisms, and each species has evolved elaborate set of protective measures called, collectively, the immune system. The natural immune system is an adaptive learning system that is highly distributive in nature. It employs multi-level defense mechanisms to make rapid, highly specific and often very protective responses against wide variety of pathogenic microorganisms. The immune system is a subject of great research interest because of its powerful information processing capabilities [5,6]. Specifically, its' mechanisms to extract unique signatures from antigens and ability to recognize and classify dangerous antigenic peptides are very important. It also uses memory to remember signature patterns that have been seen previously, and use combinatorics to construct antibody for efficient detection. It is observed that the overall behavior of the system is an emergent property of several local interactions. Moreover, the immune response can be either local or systemic, depending on the route and property of the antigenic challenge [19]. The immune system is consists of different populations of immune cells (mainly B or T cells) which circulate at various primary and secondary lymphoid organs of the body. They are carefully controlled to ensure that appropriate populations of B and T cells (naive, effector, and memory) are recruited into different location [19]. This differential migration of lymphocyte subpopulations at different locations (organs) of the body is called trafficking or homing. The lymph nodes and organs provide specialized local environment (called germinal center) during pathogenic attack in any part of the body. This dynamic mechanism support to create a large number of antigen-specific lymphocytes (as effector and memory cells) for stronger defense through the process of the clonal expansion and differentiation. Interestingly, memory cells exhibit selective homing to the type of tissue in which they first encountered an antigen. Presumably this ensures that a particular memory cell will return to the location where it is most likely to re-encounter a subsequent antigenic challenge. The mechanisms of immune responses are self-regulatory in nature. There is no central organ that controls the functions of the immune system. The regulation of the clonal expansion and proliferation of B cells are closely regulated (with a co-stimulation) in order to prevent uncontrolled immune response. This second signal helps to ensure tolerance and judge between dangerous and harmless invaders. So the purpose of this accompanying signal in identifying a non-self is to minimize false alarm and to generate decisive response in case of a real danger[19]. 2. Existing works in Intrusion Detection: The study of security in computer networks is a rapidly growing area of interest because of the proliferation of networks (LANs, WANs etc.), greater deployment of shared computer databases (packages) and the increasing reliance of companies, institutions and individuals on such data. Though there are many levels of access protection to computing and network resources, yet the intruders are finding ways to entry into many sites and systems, and causing major damages. So the task of providing and maintaining proper security in a network system becomes a challenging issue. Intrusion/Anomaly detection is an important part of computer security. It provides an additional layer of defense against computer misuse (abuse) after physical, authentication and access control. There exist different methods for intrusion detection [7,23,25,29] and the early models include IDES (later versions NIDES and MIDAS), W & S, AudES, NADIR, DIDS, etc. These approaches monitor audit trails generated by systems and user applications and perform various statistical analyses in order to derive regularities in behavior pattern. These works based on the hypothesis that an intruder's behavior will be noticeably different from that of a legitimate user, and security violations can be detected by monitoring these audit trails. Most of these methods, however, used to monitor a single host [13,14], though NADIR and DIDS can collect and aggregate audit data from a number of hosts to detect intrusions. However, in all cases, there is no real analysis of patterns of network activities and they only perform centralized analysis. Recent works include GrIDS[27] which used hierarchical graphs to detect attacks on networked systems. Other approaches used autonomous agent architectures [1,2,26] for distributed intrusion detection. 3. Computer Immune Systems: The security in the field of computing may be considered as analogous to the immunity in natural systems. In computing, threats and dangers (of compromising privacy, integrity, and availability) may arise because of malfunction of components or intrusive activities (both internal and external). The idea of using immunological principles in computer security [9-11,15,16,18] started since 1994. Stephanie Forrest and her group at the University of New Mexico have been working on a research project with a long-term goal to build an artificial immune system for computers [911,15,16]. This immunity-based system has much more sophisticated notions of identity and protection than those afforded by current operating systems, and it is suppose to provide a general-purpose protection system to augment current computer security systems. The security of computer systems depends on such activities as detecting unauthorized use of computer facilities, maintaining the integrity of data files, and preventing the spread of computer viruses. The problem of protecting computer systems from harmful viruses is viewed as an instance of the more general problem of distinguishing self (legitimate users, uncorrupted data, etc.) from dangerous other (unauthorized users, viruses, and other malicious agents). This method (called the negative-selection algorithm) is intended to be complementary to the more traditional cryptographic and deterministic approaches to computer security. As an initial step, the negativeselection algorithm has been used as a file-authentication method on the problem of computer virus detection [9].

Artificial immune systems in industrial applications

Abstract: Artificial immune system (AIS) is a new intelligent problem-solving technique that is being used in some industrial applications. This paper presents an immunity-based algorithm for tool breakage detection. The method is inspired by the negative-selection mechanism of the immune system, which is able to discriminate between the self (body elements) and the nonself (foreign pathogens). However, in our industrial application, the self is defined to be normal cutting operations and the nonself is any deviation beyond allowable variations of the cutting force. The proposed algorithm is illustrated with a simulation study of milling operations. The performance of the algorithm in detecting the occurrence of tool breakage is reported. The results show that the negative-selection algorithm detected tool breakage in all test cases.

An artificial immune model for network intrusion detection

Abstract: This paper investigates the subject of intrusion detection over networks. Existing network-based IDS’s are categorised into three groups and the overall architecture of each group is summarised and assessed. A new methodology to this problem is then presented, which is inspired by the human immune system and based on a novel artificial immune model. The architecture of the model is presented and its characteristics are compared with the requirements of network-based IDS’s. The paper concludes that this new approach shows considerable promise for future network-based IDS’s.

From GAs to artificial immune systems: improving adaptation in time dependent optimization

Abstract: The paper explores Time Dependent Optimization (Tdo) as a measure of adaptiveness in artificial systems. We first discuss this choice and review classical Tdo models to propose a canonic benchmark. Then we underline the central role of diversity in adaptive dynamics for biological and cybernetic systems and illustrate by a state of the art of evolutionary Tdo (Etdo). A Simple Artificial Immune System (Sais) is then proposed and experimentally compared to Etdo. Encouraging results are explained by strong analogies between Sais and GAs as well as Sais's ability to manage stable heterogeneous populations as a model of Idiotypic Networks. We conclude by discussing the relevance of artificial immune systems as genuinely adaptive artificial systems.

An immune system approach to scheduling in changing environments

Abstract: This paper describes the application of an artificial immune system, (AIS), model to a scheduling application, in which sudden changes in the scheduling environment require the rapid production of new schedules. The model operates in two phases: In the first phase of the system, the immune system analogy, in conjunction with a genetic algorithm, (GA), is used to detect common patterns amongst scheduling sequences frequently used by a factory. In phase II, some of the combinatoric features of the natural immune system are modelled in order to use the detected patterns to produce new schedules, either from scratch or starting from a partially completed schedule. The results are compared to those calculated using an exhaustive search procedure to generate patterns. The AIS/GA analogy appears to be extremely promising, in that schedules corresponding to situations previously encountered can easily be reconstructed, and also in that the patterns are shown to incorporate sufficient information to potentially construct schedules for previously unencountered situations.

Negative selection and niching by an artificial immune system for network intrusion detection

Abstract: This paper presents a negative selection algorithm with niching by an artificial immune system, for network intrusion detection. The paper starts by introducing the advantages of negative selection algorithm as a novel distributed anomaly detection approach for the development of a network intrusion detection system. After discussing the problems of existing approaches using negative selection for network intrusion detection, this paper presents a modified negative selection algorithm with niching, which shows diversity, generality and requires less computation time. The network packet data used in this work is then introduced and a novel genotype encoding scheme to handle this data and a corresponding fitness function is explained.

Jisys: The Envelopment of an Artificial Immune System for Real World Applications

Abstract: This chapter Enscribes a machine learning system based on metaphors taken from the human immune system. This learning system, known as an Artificial Immune System (AIS), has been Enveloped over the past 3 years. The current implementation,Jisys,embodies the results of this research. However, the Jisys implementation requires further Envelopment as well as application to complex real world problems. This chapter Enscribes future Envelopments ofJisysas well as consiEnration of how it can be applied to a complex problem in the domain of mortgage fraud Entection. It should not be read as a Ensign document, although it contains elements of such a document, rather it should be read as an indication of the directions which need to be followed, the issues which need to be addressed and some suggested solutions

Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons

Abstract: Knowledge discovery in databases (KDD) is still a relatively new and expanding field. To aid the KDD process, data mining methods are used to extract previously unknown patterns and trends in vast amounts of data. There exist a number of data mining techniques, taking methods from the machine learning, statistical analysis and pattern recognition communities, to name a few. Each technique has something different to offer over other techniques and each is suitable for different purposes giving certain benefits in varying situations. This paper examines a novel data analysis technique that is inspired by the human immune system: the artificial immune system (AIS). Immune system principles act as inspiration, allowing the creation of a network of cells that in effect clusters similar patterns and trends together. It is inspired by but not a model of the human immune system. This clustering allows the human user to effectively identify areas of similarity from the training data set that would previously have been unobtainable.

Decentralized Behavior Arbitration Mechanism for Autonomous Mobile Robot Using Immune Network

Abstract: In recent years much attention has been focused on behavior-based artificial intelligence, (AI) which has already Enmonstrated its robustness and flexibility against dynamically changing world. However, in this approach, the followings problems have not yet been resolved: 1) how do we construct an appropriate arbitration mechanism, and 2) how do we prepare appropriate competence modules (behavior primitives). One of the promising approaches to tackle the problems is a biologically inspired approach. Among them, we Particlely focus on the immune system, since it is Endicated to self-preservation unEnr hostile environment (needless to say autonomous mobile robots must cope with dynamically changing environment). Therefore, we construct a new Encentralized behavior arbitration mechanism inspired by the biological immune system. And we apply it to the garbage-collecting problem of autonomous mobile robot that takes into account of the concept of selfsufficiency. To verify the feasibility of our method, we carry out some experiments using a real robot. In addition, we investigate two types of adaptation mechanisms to construct an appropriate artificial immune network without human intervention

An Anomaly Entection Algorithm Inspired by the Immune Syste

Abstract: Entecting anomaly in a system or a process behavior is very important in many real-world applications such as manufacturing, monitoring, signal processing etc. This chapter presents an anomaly Entection algorithm inspired by the negative-selection mechanism of the immune system, which discriminates betweensellandother.Here self is Enfined to benormal data patternsand non-self is any Enviation exceeding an allowable variation. Experiments with this anomaly Entection algorithm are reported for two data sets - time series data, generated using the Mackey-Glass equation and a simulated signal.Compared to existing methods, this method has the advantage of not requiring prior knowledge about all possible failure moEns of the monitored system. Results are reported to display the performance of the Entection algorithm

SIMMUNE, a tool for simulating and analyzing immune system behavior

Abstract: We present a new approach to the simulation and analysis of immune system behavior The simulations that can be done with our software package called SIMMUNE are based on immunological data that describe the behavior of immune system agents (cells, molecules) on a microscopial (ie agent-agent interaction) scale by defining cellular stimulus-response mechanisms Since the behavior of the agents in SIMMUNE can be very flexibly configured, its application is not limited to immune system simulations We outline the principles of SIMMUNE's multiscale analysis of emergent structure within the simulated immune system that allow the identification of immunological contexts using minimal a priori assumptions about the higher level organization of the immune system

Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system

Abstract: In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.

Immune Network: An Example of Complex Adaptive Systems

Abstract: The phenomenon of immunological memory has been known for a long time. But, the unEnrlying mechanism is poorly unEnrstood. According to the theory of clonal selection the response to a specific invading antigen (e.g., bacteria) is offered by a specific clone of the cells. Some of the lymphocytes activated during the primary response remain dormant and keep circulating in the immune system for a long time carrying the memory of the encounter and, therefore, these long-lived cells are called memory cells. Proponents of the alternative network theory maintain that the immune response is offered by a “network” of clones in a collective manner. In recent years several possible scenarios of the “structure” and function of the immune network have been consiEnred. We have Enveloped mathematical Models for Enscribing the population dynamics of the immunocompetent cells in a unified manner. We have incorporated intra-clonal as well as inter-clonal interactions in a discrete formulation and also studied a continuum version of this Model.

Immune algorithm with immune network and MHC for adaptive problem solving

Abstract: Adaptive problem solving techniques such as neural networks and genetic algorithms become so popular in the AI field. The biological immune system is one of the adaptive biological systems whose functions are to identify and to eliminate foreign materials. In this paper, we propose an adaptive optimization algorithm based on immune model with immune network and major histocompatibility complex (MHC). In biological immune system, immune network controls immune responses by changing its structure. The MHC is used to distinguish a "self" from other "not self". In our model, immune network is used to produce adaptive behaviors of agents, which are computing subject for problem solving. MHC is used to induce competitive behaviors among agents. To investigate an adaptation ability of the proposed algorithm, we apply it to the n-th agent's travelling salesman problem called n-TSP. This algorithm performs adaptive behaviors for distributed cooperation.

The Biological Basis of the Immune System as a Model for Intelligent Agents

Abstract: This paper describes the human immune system and its functionalities from a computational viewpoint. The objective of this paper is to provide the biological basis for an artificial immune system. This paper will also serve to illustrate how a biological system can be studied and how inferences can be drawn from its operation that can be exploited in intelligent agents. Functionalities of the biological immune system (e.g., content addressable memory, adaptation, etc.) are identified for use in intelligent agents. Specifically, in this paper, an intelligent agent will be described for task allocation in a heterogeneous computing environment. This research is not intended to develop an explicit model of the human immune system, but to exploit some of its functionalities in designing agent-based parallel and distributed control systems.

Some design principles for immune system recognition

Abstract: The immune system is a complex system that learns, remembers what it has learned, and acts to protect us from a variety of pathogens. Here we address the question of how the immune system is able to recognize and learn about pathogens that can rapidly evolve and hence potentially change so as to avoid immune recognition. Submitted to Complexity.

John Holland’s Invisible Hand: An Artificial Immune System

Abstract: We describe an artificial immune system (AIS) that is distributed, robust, dynamic, diverse and adaptive. It captures many features of the vertebrate immune system and places them in the context of the problem of protecting a network of computers from illegal intrusions. The AIS resembles a classifier system in many important ways. Similarities and differences are discussed.

An immune feedback mechanism based adaptive learning of neural network controller

Abstract: Both neural networks and immunity-based systems are biologically inspired techniques that have the capability of identifying and controlling. The information processing principles of these natural systems inspired the development of intelligent problem solving techniques, namely, the artificial neural network and the artificial immune system. An adaptive learning method for a neural network (NN) controller using an immune feedback law is proposed. The immune feedback law features rapid response to foreign matter and rapid stabilization of biological immune systems. Several improvements can be made to improve gradient descent NN learning algorithms. The use of an adaptive learning rate attempts to keep the learning step size as large as possible while keeping learning stable. In the proposed method, because the immune feedback law changes the learning rate of the NN individually and adaptively, it is expected that a cost function is rapidly minimized and learning time is decreased. In the control structure, a reference signal self-organizing control system using NNs for flexible microactuators is used. In this system, the NN functions as a reference input filter, setting new reference signals in the closed loop system. Numerical and experimental results show that the proposed control system is effective in tracking a reference signal.

Design Multilayer Feed-forward Networks Based on Immune Evolutionary Programming

Abstract: The authors use an immune evolutionary programming to design multilayer feed-forward networksin this paper. The immune evolutionary programming retains the ability of stochastic global searching of tradi-tional evolutionary programming, and draws into the interaction mechanism based on density and the diversitymaintaining mechanism which exists in living beings' immune procedure, The immune evolutionary program-ming has better global convergence and very strong self-adaptive ability with enviornment. The experimentalresults prove the high efficiency of the immune evolutionary programming in designing neural networks.

A Multiple-Valued Immune Network and Its Applications

Abstract: This paper describes a new model of multiplevalued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one. key words: multiple-valued, immune network, immune response, pattern recognition