We present new results on a distributable changedetection method inspired by the natural immune system. A weakness in the original algorithm was the exponential cost of generating detectors. Two detector-generating algorithms are introduced which run in linear time. The algorithms are analyzed, heuristics are given for setting parameters based on the analysis, and the presence of holes in detector space is examined. The analysis provides a basis for assessing the practicality of the algorithms in specific settings, and some of the implications are discussed. 1.

Detecting anomalies in time series data is a problem of great practical interest in many manufacturing and signal processing applications. This paper presents a novelty detection algorithm inspired by the negative-selection mechanism of the immune system, which discriminates between self and other. Here self is defined to be normal data patterns and non-self is any deviation exceeding an allowable variation. An example application, simulated cutting dynamics in a milling operation, is presented, and the performance of the algorithm in detecting the tool breakage is reported. 1 INTRODUCTION The normal behavior of a system is often characterized by a series of observations over time. The problem of detecting novelties or anomalies can be viewed as finding non permitted deviations of a characteristic property in the system of interest. The detection of novelty is an important task in many diagnostic and monitoring systems. In safety-critical applications, it is essential to detect the o...

This dissertation explores an immunological model of distributed detection, called negative detection, and studies its performance in the domain of intrusion detection on computer networks. The goal of the detection system is to distinguish between illegitimate behaviour (nonself ), and legitimate behaviour (self ). The detection system consists of sets of negative detectors that detect instances of nonself; these detectors are distributed across multiple locations. The negative detection model was developed previously; this research extends that previous work in several ways. Firstly, analyses are derived for the negative detection model. In particular, a framework for explicitly incorporating distribution is developed, and is used to demonstrate that negative detection is both scalable and robust. Furthermore, it is shown that any scalable distributed detection system that requires communication (memory sharing) is always less robust than a system that does not require communication...

Biological systems such as human beings can be regarded as sophisticated information processing systems, and can be expected to provide inspiration for various ideas to science and engineering. Biologically motivated information processing systems can be classified into: brain-nervous systems (neural networks), genetic systems (evolutionary algorithms), and immune systems (artificial immune systems). Among these, nervous systems and genetic systems have been widely applied to various fields. There have been a relative few applications of the immune system. This paper presents a survey of artificial immune systems and provides numerous insights of immunity-based systems applications in science and engineering.

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness ’ and ‘effectiveness. ’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory. ’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

Detection of tool breakage is very important for automated machining operations. This paper presents a negative-selection algorithm for tool breakage detection. The method is inspired by the defense mechanism of the immune system, which discriminates between self and non-self. Here self is defined to be normal cutting operations and non-self is any deviation beyond allowable variation of the cutting force. The proposed algorithm is illustrated with a simulation study of milling operations and the performance of the algorithm in detecting the occurrence of tool breakage is reported. The negative-selection algorithm detected tool breakage in all the test cases. 1 Introduction Manufacturers are always looking for ways to improve productivity without compromising on quality of manufacturing processes. To this end, much attention has been directed towards automated manufacturing. In drilling or high-speed milling industries, on-line monitoring of the tool breakage is a key component in unm...

Since the advent of computers numerous approaches have been taken to create hardware systems that provide a high degree of reliability even in the presence of errors. This paper seeks to address the problem from a biological perspective using the human immune system as a source of inspiration. The immune system uses many ingenious methods to provide reliable operation in the body and so may suggest how similar methods can be used in the future design of reliable systems. The paper addresses this challenge through the implementation of an immunised finite state machine based counter. The proposed methods demonstrate how through a process of self/non-self differentiation the hardware immune system will create a set of tolerance conditions to monitor the change in states of the hardware. Potential faults may then be flagged, assessed and the appropriate recovery action taken.

The problem of protecting computer systems can be viewed in part as the problem of distinguishing self from other. We describe a method for accomplishing this which is based on the way natural immune systems generate T-cells. The two-phase algorithm first generates a set of detectors that do not match self. In the second phase, these detectors are used to monitor self and if a match is ever found, a change is reported. Mathematical analysis reveals the conditions under which the system is feasible, and preliminary experiments illustrate how the method could be applied to the problem of computer virus detection. Keywords: change detection, computer virus detection, immunology, negative selection, self non-self discrimination. 1 Current address: SBS Engineering, 5550 Midway Park Pl.,Albuquerque, N.M. 87109. 2 Current address: Correa Enterprises Inc., 5801 Osuna Rd. N.E. Suite 206, Albuquerque, N.M. 87109. 1 Introduction The security of computer systems depends on such activities ...

Traffic in wireless sensor networks (WSN) is commonly created by sensor readings which can be modelled by various distributions as the occurrence of events triggers the injection of packets. The Poisson distribution is a common example for a widely used event distribution as many processes (for example the arrival of customers or the dissemination of parasits) are known to be Poisson distributed. The impact of such a packet injection model on sensor networks and especially on the performance of misbehaviour detection systems is therefore of interest. In this paper we investigate the impact of the Poisson model and the constant bit rate model on a misbehaviour detection system, namely an artificial immune system (AIS). We state the hypothesis that both models have no significant effect on the detection rate. We examine the influence of the two models on the detection performance and compare the results. We conclude that the differences between the two models show no statistically significant effects on the detection performance supporting our hypothesis. However, we observe that the AIS had a significantly smaller false positives rate for the Poisson model than for the CBR model. 1

Abstract. This paper shows the potential accomplishments of artificial immune systems (in particular, the negative selection algorithm) application to the problem of speaker recognition. Both the use of binary representation of original signal and that of its Fast Fourier Transform in a real-number representation are analysed. A number of experiments are performed on different datasets to examine the performance evolution with respect to the different system parameters. It is found that substantial enhancements of the system capabilities are possible by means of the exploitation of the Fast Fourier Transform.