Research of Immune Controllers

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

Agent-based modeling and simulation is a way to model the behavior of populations of components and their interactions within a system. The key of this approach is to model the components of the system as autonomous agents and to simulate their behavior for evaluating the system as a whole. That is very useful for observing the emergence of properties in social, biological, environmental or financial systems, among others. Artificial immune systems, which is a sub field of artificial intelligence, comprises systems modeled by simplifying models from the biological immune system. If agent-based modeling and simulation is used as a laboratory for understanding the biological immune system then, it can also be used for transferring the observed principles into artificial immune system models or for evaluating models that have been already adapted for solving technical problems. This paper presents first, a methodology for transferring principles of the biological immune system into the field of artificial immune systems. Then, it presents a brief explanation of the behavior of the cells of the biological immune system, which are treated as internal agents inside a biological organism. Afterwards, the modeling of some selected type of cells and the simulation of the whole simplified system are presented. In the end, the principles of that simplified system are transferred into the design of an alarm management system for the smart grid.

A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

The aim of this paper is to integrate the artificial immune systems and adaptive fuzzy control for the automobile suspension system, which is regarded as a multiobjective optimization problem. Moreover, the fuzzy control rules and membership controls are then introduced for identification and memorization. It leads fast convergence in the search process. Afterwards, by using the diversity of the antibody group, trapping into local optimum can be avoided, and the system possesses a global search capacity and a faster local search for finding a global optimal solution. Experimental results show that the artificial immune system with the recognition and memory functions allows the system to rapidly converge and search for the global optimal approximate solutions.

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

An artificial immune system (ARTIS) enlightened by the biological immune system is emerging as a new field of computation intelligence research. At present, it showed us many properties after the research to the immune system, including distributed computation, self-organization and lightweight, which were discovered to meet the requests of ARTIS. So the Immunology applied to the intrusion detection will be a brand-new area. This paper, firstly introduces the development of intrusion detection technology, it discusses the Immunity-based technical research to detect network intrusions, then it analyzes the problems exited in the Immunity-based technical to detect network intrusions, lastly summarize the works of my group and expects the research emphasis and development trend in the future.

The Artificial Immune System (AIS) is a biologically inspired computation system based on specifically human immune system. AIS applications in last one decade have been developed to address the complex computational and engineering problems related to classification, optimisation and anomaly detection. Many investigations have been conducted to understand the principles of immune system to translate the knowledge into AIS applications. However, a clear understanding of principles and responses of immune system is still required for application of AIS to Swarm Robotics. This article after a review of AIS models and algorithms proposes an integration of AIS and Swarm Robotics by developing a very clear understanding of immune system structures and associated functions.

Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research.

The current RFID systems are fragile to external attacks, due to the limitations of encryption authentication and physical protection methods used in implementation of RFID security systems. In this paper, we propose a collaborative RFID intrusion detection method that is based on an artificial immune system (AIS). The new method can enhance the security of RFID systems without need to amend the existing technical standards of RFID. Mimicking the immune cell collaboration in biological immune systems, RFID operations are defined as self and nonself antigens, representing legal and illegal RFID operations, respectively. Data models are defined for antigens' epitopes. Known RFID attacks are defined as danger signals represented by nonself antigens. We propose a method to collect RFID data for antigens and danger signals. With the antigen and danger signal data available, we use a negative selection algorithm to generate adaptive detectors for self antigens as RFID legal operations. We use an immune based clustering algorithm aiNet to generate collaborative detectors for danger signals of RFID intrusions. Simulation results have shown that the new RFID intrusion detection method has effectively reduced the false detection rate. The detection rate on known types of attacks was 98% and the detection rate on unknown type of attacks was 93%.

Nowadays, Structural Health Monitoring (SHM) is essential in order to prevent damages occurrence in civil structures. This is a particularly important issue as the number of aged structures is increasing. Damage detection algorithms are often based on changes in the modal properties like natural frequencies, modal shapes and modal damping. In this paper, damage detection is completed by using Artificial Immune System (AIS) theory directly on acceleration data. Inspired from the biological immune system, AIS is composed of several models like negative selection which has a great potential for this study. The negative selection process relies on the fact that T-cells, after their maturation, are sensitive to non self cells and can not detect self cells. Acceleration data were provided by using the numerical model of a 3-story frame structure. Damages were introduced, at particular times, by reduction of story's stiffness. Based on these acceleration data, undamaged data (equivalent to self data) and damaged data (equivalent to non self data) can be obtained and represented in the Hamming shape-space with a binary representation. From the undamaged encoded data, detectors (equivalent to T-cells) are derived and are able to detect damaged encoded data really efficiently by using the rcontiguous bits matching rule. Indeed, more than 95% of detection can be reached when efficient combinations of parameters are used. According to the number of detected data, the localization of damages can even be determined by using the differences between story's relative accelerations. Thus, the difference which presents the highest detection rate, generally up to 89%, is directly linked to the location of damage.

This work focuses on an aspect of artificial immune systems not yet addressed by research, and demonstrates that as immune system metaphors grow and evolve in complexity, overall network health can play a role in the vulnerability and strength of the network. Recent works have suggested the analogous role of the immune system to computer security systems. As artificial immune system research progresses, a growing number of components of the human immune system have been incorporated, raising opportunities for further research and understanding. In particular, this work focuses on the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS) in artificial immune systems. CRS stems from a comparison of the overall strength of the host entity of an immune system to individual agent strength, this case in an artificial immune system (AIS). This principle has been employed to facilitate selection in a model of an AIS. Using the development and testing of an agent-based model of a complex network to demonstrate and evaluate the concept of comparative relative strength (CRS), this research introduces this concept.

This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This article also, summarizes the direction of current research and suggests areas that might most profitably be given further scholarly attention.

Peer‐to‐peer (P2P) file‐sharing systems are characterized by highly replicated content that is distributed among nodes with enormous aggregate resources for storage and communication. File consistency is often compromised by undesirable changes, which should be detected and corrected in a timely fashion. The artificial immune system (AIS) is a novel evolutionary paradigm inspired by aspects of the biological immune system (BIS), such as protection, decentralization, autonomy, and anomaly detection. The AIS paradigm suggests a wide variety of mechanisms for solving complex computer problems. In this paper, we propose the ImmunoJXTA framework for file consistency management and file recovery using the main aspects of AIS in P2P systems. We implemented ImmunoJXTA on the JXTA P2P framework to recover distributed inconsistent files between peers efficiently. Promising results are achieved from experimental runs of the proposed framework. Copyright © 2010 John Wiley & Sons, Ltd.

Biology has produced living creatures that exhibit remarkable fault tolerance. The immune system is one feature that enables this. The acquired immune system learns during the life of the individual to differentiate between self (that which is normally present) and non-self (that which is not normally present). This paper describes an artificial immune system (AIS) that is used as an error detection system and is applied to two different robot based applications; the immunization of a fuzzy controller for a Khepera robot that provides object avoidance and a control module of a BAE Systems RASCAL/sup TM/ robot. The AIS learns normal behavior (unsupervised) during a fault free learning period and then identifies all error greater that a preset error sensitivity. The AIS was implemented in software but has the potential to be implemented in hardware. The AIS can be independent to the system under test, just requiring the inputs and outputs. This is not only ideal in terms of common mode and design errors but also offers the potential of a general, off-the-shelf, error detection system; the same AIS was applied to both the applications.