Artificial Immune Systems

This paper addresses the problem of automatic classification of fish species, by using image analysis techniques and artificial immune systems. Unlike most common methodologies, which are based on manual estimations that lead to significant time and financial constraints, we present an automatic framework based on (i) two well-known robust feature extraction techniques: Scale-Invariant Feature Transform and Principal Component Analysis for parameterizing shape, appearance and motion, (ii) two immunological algorithms: Artificial Immune Network and Adaptive Radius Immune Algorithm for clustering individuals of the same species, and (iii) a simple nearest neighbor classification strategy. The framework was successfully validated with images of fish species that have significant economic impact, achieving overall accuracy as high as 92%.

AIS based intrusion detection systems have traditionally performed self non-self discrimination and suffer from issues such as scalability, false positives, problems with detector generation/holes, need for an initial learning phase, etc. A relatively newer immunological discovery, the Danger Theory, now paves the way for designing more efficient, 2nd generation artificial immune systems. In this paper, we develop a dendritic cell based distributed misbehavior detection system, BeeAIS-DC, for a Bio/Nature inspired MANET routing protocol, BeeAdHoc. In MANETs, the frequent node movements cause the system self to change, thus increasing the rate of false positives. Our proposed system inspires from the danger theory and models the behavior of the dendritic cells to detect the presence or absence of danger to provide a tolerogenic or immunogenic effect. We have implemented our proposed framework, BeeAIS-DC, in network simulator, ns-2, and evaluated its security and network performance. Our results indicate that modelling the dendritic cells allows the BeeAIS-DC to dynamically update its detector set to cater for a changing self due to node mobility, and at the same time provides protection against the routing attacks. The network performance evaluation shows that the AIS overhead of BeeAIS-DC does not cause significant degradation of its performance, which is vital for a battery/bandwidth constrained mobile node.

This paper proposes the real-coded clonal selection algorithm (RCSA) for use in electromagnetic design optimization. Some features of the algorithm, such as the number of clones, mutation range, and the fraction of the population selected each generation are discussed. The TEAM Workshop problem 22 is investigated, in order to illustrate the performance of the algorithm in a real electromagnetic problem. The results obtained, a set of optimal solutions representing a broader range of options for the designer, are compared with those achieved by a genetic algorithm, showing the efficiency of the RCSA in practical optimization problems.

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or modelling biologically plausible dynamical systems, with little overlap between. Although the balance is latterly beginning to be redressed (e.g.

Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biolog ically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low cost, low-energy sensor nodes connected to physical signals. The network is made up of sensor nodes and gateways, where the server nodes acquire physical world data, while the gateway forwards the data to the end-user. While the spread of viruses in wired systems has been studied in-depth, applying trust in wireless sensor network nodes is an emerging area. This paper uses machine learning techniques to first dif1"erentiate between fraudulent and good nodes in the system. Next, it derives inspiration from the human immune system to present an idea of virtual antibodies in the system, to disable the fraudulent nodes in the system.

Neutrigenomics In perceiving that supplements: 1) change quality articulation, 2) may modify ordinary digestion, and 3) influence wellbeing status.the academic network has revitalized to characterize between connections between eating regimen, wellbeing and illness procedures utilizing atomic methods. This subdiscipline of practical genomics, named nourishing genomics or nutrigenomics, attempts to determine the impact of dietary synthetic concoctions upon the genome and to expand our comprehension of how dietary constituents impact digestion. Nutrigenomics utilizes a wide arrangement of 'omic' strategies in light of the fact that no single method is appropriate for breaking down every extraordinary sort of particle or results. In nourishment inquire about GEP might be utilized for three unmistakable purposes. 1) To aid the ID and portrayal of essential sub-atomic pathways that might be affected, either decidedly or contrarily, by supplements; 2) To give bits of knowledge upon explicit components that trigger such useful or negative impacts and; 3) To recognize explicit qualities changed by supplements that may demonstrate significant as sub-atomic biomarkers or supplement sensors and in quality disclosure. Fundamental foci for GEP in aquaculture will be creature wellbeing and welfare-and nourishment related and for specific rearing. This is on the grounds that on a worldwide premise ailment causes billions of dollars in misfortunes and customers request meat items untainted by synthetic compounds and anti-toxins. Accordingly, advancement of feed-based immunoprotectants, particularly those that are normally happening, speaks to a levelheaded way to deal with fighting infection and a significant decrease of dependence upon synthetic concoctions.

11 th International Conference on Cryptography and Information Security (CRYPIS 2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of Cryptography, Coding and Information security. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

La vida artificial es una ciencia de frontera que intenta comprender la esencia y la naturaleza de la vida mediante la construcción (simulación o síntesis) de sistemas artificiales que exhiban propiedades normalmente asociadas a los organismos vivos. Se trata, por lo tanto, del primer esfuerzo de investigación sistemático orientado a desentrañar la lógica de la vida -y de los sistemas (artificiales) que exhiben características o comportamientos propios de la vida, entendiendo la vida como un fenómeno de complejidad creciente. Este libro es una introducción a la ciencia de la vida artificial, un tema sobre el que la literatura en español es escasa. En su núcleo, el texto reivindica el papel de la vida artificial como un proyecto de investigación científica por derecho propio y pone de manifiesto su valor científico, filosófico e ingenieril de cara al nuevo marco del conocimiento y a las nuevas formas de acción sobre el mundo que se condensan en las ciencias de la complejidad y en la ingeniería de sistemas complejos, respectivamente.

11th International Conference on Cryptography and Information Security (CRYPIS
2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to
bring together scientists, researchers and students to exchange novel ideas and results in all aspects of
Cryptography, Coding and Information security.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in the
following areas, but are not limited to

Accurate and fast islanding detection of distributed generation is highly important for its successful operation
in distribution networks. Up to now, various islanding detection technique based on communication,
passive, active and hybrid methods have been proposed. However, each technique suffers from
certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques,
due to their robustness and flexibility in dealing with complex nonlinear systems, is an option
that might solve this problem. This paper aims to provide a comprehensive review of computational
intelligence based techniques applied for islanding detection of distributed generation. Moreover, the
paper compares the accuracies of computational intelligence based techniques over existing techniques
to provide a handful of information for industries and utility researchers to determine the best method
for their respective system.

With increased global interconnectivity, reliance on e-commerce, network services, and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer-virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current antivirus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral stains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java, and tested. The results validate the use of a distributed-agent biological-system approach toward the computer-security problems of virus elimination and ID.

Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag `non standard' transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.

Artificial Immune Systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve complex problems. In this paper; description, problem solving technique and industrial applications of artificial immune systems are presented. Biologically inspired algorithms are artificial intelligence techniques that in recent years, have been are improved to solve Non Polinomial-NP problems. These are; genetic algorithms, ant colonies optimization and artificial immune systems. Also in this paper the two biologically inspired techniques; artificial immune systems and genetic algorithms are compared, to determine their strong and weak aspects.

11 th International Conference on Cryptography and Information Security (CRYPIS 2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of Cryptography, Coding and Information security. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

The complex web of the global information grid will undergo explosive changes over coming decades. As advances in science and technology converge, a myriad array of discoveries in biotechnology, nanotechnology and information technology will produce unpredictable effects that must be accounted for in any estimate of what the world will look like in this future. A strategically important feature of this world will be the emerging trend of information warfare. Though still immature at present day, this trend will become increasingly dominant in the years to come. The information warfare of tomorrow will be radically different from its prototype today. No longer will it be confined to the mainframes of the Internet or to corporate databases: the battleground of the future will draw into its scope the scientific advances being made today in bio and nano technologies. The divisions between man and machine will blur. When networked technologies are ubiquitous, a state-sponsored attack on electronic networks can have far-reaching, and devastating, physical consequences.

Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex network theory, particularly in the area of small-world and scalefree topologies. Computational studies which attempt to understand the structure-function relationship usually proceed by defining a representation of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the networks that can arise-furthermore, we show that although simple topologies can be produced via representation and affinity measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting scalefree functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free and regular topologies, finding that a key immunological property-tolerance-is promoted by bipartite and heterogeneous topologies. The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial immune systems.

Entropy can be defined as the antithesis of well-formed true reports that agree with each other and with the material facts accessible through the experience of one or more competent observers. The abstract convergence (strictly formal, logical agreement) of true narrative representations (TNRs)-ordinary valid reports of facts of experience-makes them formally more complete than fictions, errors, lies, and nonsense. A limit of absolute entropy is theoretically reached if all resemblance to a TNR is lost. As argued here, TNRs-formally defined along the lines of Peirce's exact logic-provide the necessary foundation for functional human languages and for biosemiotic systems. The theoretical concepts of pragmatic mapping-the fitting of a TNR to whatever facts it represents-and the constructive cycle of abstraction that enables a child to discover the systems underlying such mappings are introduced and illustrated from child development and then shown to apply to the human neuroarchitecture, genetics, fetal development, and our immune systems. It is also argued that biological disorders and disease conditions logically must involve corrupted (damaged, undeveloped, or otherwise incomplete) representations at one or many levels.

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

11th International Conference on Cryptography and Information Security (CRYPIS2022) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of
Cryptography, Coding and Information security

Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.

Artificial Immune Systems (AIS) is an emerging bio-inspired computer science technique which embody the principles of biological immune systems for tackling complex real-world problems such as pattern recognition. Among the several immune-computing models, Artificial Immune Recognition System (AIRS) is one of the widely used for classification problems. Meanwhile, the issues related to writer identification are currently at the heart of numerous concerns in our modern day's society. Writer identification for Arabic text is receiving a renewed attention. Many popular machine learning techniques have been used in writer identification systems but only one limited attempt has been done with AIS. In this paper, we apply AIRS to perform Arabic writer identification based on a set of features extracted from Grey Level Co-occurrence Matrices. Some feature selection techniques are applied to improve computation time and accuracy results. Three traditional classifiers have also been used in our experiments for performance comparison. The obtained results show the promising ability of AIRS in Writer identification.

A spam detection model based on negative selection algorithm is proposed in this paper. The artificial immune system creates techniques to solve complex computations, aiming to developing immune based models. This is done by distinguishing self from non-self. Preliminary mathematical analysis will expose the computation and experimental description of the method and how it is applied to spam detection. A new detector model and matching rule model are also generated for effective matching of both self and non-self in other to burst the detector performance of the model. Our unique matching technique use in the negative selection algorithm help the model to overcome the limitation of a normal negative selection algorithm in defining harmfulness of self and non-self. This improves the requirement of the model and satisfactory requirement in terms of true positive and false positive rates. The experimental result confirms that the proposed model is able to establish a better true positive on an unknown spam .

In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence (specifically, Artificial Immune Systems-AIS) to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in the methodology's ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of AIS algorithms.

This paper compares between two models: Common Genetic algorithm and the new Clonal selection theory in the field of Intrusion Detection. Genetic algorithms (GA) which is a model of genetic evolution, while Clonal selection theory (CST) is from models of the natural immune system NIS, the two models are from two different fields of Artificial Intelligence AI but they have portion of shared operations and objectives. The comparison to be done by applying the two models on some records of Knowledge Discovery and Data mining tools which is known by the name KDD data sets (its records the data of the interring packets to the computer system from the internet), to produce population (in case of GA) or antibodies (in case of CST) can recognize these abnormal records. ‫ﻤﺠﻤﻭﻋﺔ‬ ‫ﻋﻠﻰ‬ ‫ﺍﻟﺴﻼﻟﺔ‬ ‫ﺇﻨﺘﻘﺎﺀ‬ ‫ﻭﻨﻅﺭﻴﺔ‬ ‫ﺍﻟﺠﻴﻨﻴﺔ‬ ‫ﺍﻟﺨﻭﺍﺭﺯﻤﻴﺔ‬ ‫ﺒﻴﻥ‬ ‫ﻋﻤﻠﻴﺔ‬ ‫ﻤﻘﺎﺭﻨﺔ‬ ‫ﺒﻴﺎﻨﺎﺕ‬ KDD ‫ﺃ‬. ‫ﺩ‬. ‫ﺍﻟﺩﺒﺎﻍ‬ ‫ﺒﺩﻴﻊ‬ ‫ﻨﺠﻼﺀ‬ ‫ﻡ‬ ‫ﻤﺴﺎﻋﺩ‬ ‫ﺃﺴﺘﺎﺫ‬ ‫ﻭﺍﻟﺭﻴﺎﻀﻴﺎﺕ‬ ‫ﺍﻟﺤﺎﺴﺒﺎﺕ‬ ‫ﻋﻠﻭﻡ‬ ‫ﻜﻠﻴﺔ‬ / ‫ﺤﺎﺴﺒﺎﺕ‬ ‫ﻋﻠﻭﻡ‬ ‫ﺨﻠﻴل‬ ‫ﻤﺤﺴﻥ‬ ‫ﻤﻔﺎﺯ‬ ‫ﻤﺴﺎﻋﺩ‬ ‫ﻤﺩﺭﺱ‬ ‫ﺍﻟﻌﻠﻭﻡ‬ ‫ﻜﻠﻴﺔ‬ / ‫ﺤﻴﺎﺓ‬ ‫ﻋﻠﻭﻡ‬ ‫ﺍﻟﻤﻠﺨﺹ‬ ‫ﻨﻤﻭﺫﺠﺎﻥ‬ ‫ﻤﺎﺒﻴﻥ‬ ‫ﺍﻟﺒﺤﺙ‬ ‫ﻫﺫﺍ‬ ‫ﻴﻘﺎﺭﻥ‬ : ‫ﺍﻟـﺴﻼﻟﺔ‬ ‫ﺍﻨﺘﻘـﺎﺀ‬ ‫ﻭﻨـﻀﺭﻴﺔ‬ ‫ﺍﻟﻤﻌﺭﻭﻓﺔ‬ ‫ﺍﻟﺠﻴﻨﻴﺔ‬ ‫ﺍﻟﺨﻭﺍﺭﺯﻤﻴﺔ‬ ‫ﺍﻟﺘﻁﻔل‬ ‫ﻜﺸﻑ‬ ‫ﻤﺠﺎل‬ ‫ﻓﻲ‬ ‫ﺍﻟﺠﺩﻴﺩﺓ‬. ‫ﺘﻌﺘﺒﺭ‬ ‫ﺤﻴﺙ‬ ‫ﺍﻟﺠﻴﻨﻴﺔ‬ ‫ﺍﻟﺨﻭﺍﺭﺯﻤﻴﺔ‬) GA (،‫ﺍﻟﺠﻴﻨﻲ‬ ‫ﻟﻠﺘﻁﻭﺭ‬ ‫ﻨﻤﻭﺫﺝ‬ ‫ﺒﻴﻨﻤﺎ‬ ‫ﺍﻟﺴﻼﻟﺔ‬ ‫ﺍﻨﺘﻘﺎﺀ‬ ‫ﻨﻅﺭﻴﺔ‬ ‫ﺘﻌﺘﺒﺭ‬) CST (‫ﺤﻘﻠﻴﻥ‬ ‫ﻤﻥ‬ ‫ﺃﻨﻬﻤﺎ‬ ‫ﺃﻱ‬ ،‫ﺍﻟﻁﺒﻴﻌﻲ‬ ‫ﺍﻟﻤﻨﺎﻋﺔ‬ ‫ﻨﻅﺎﻡ‬ ‫ﻨﻤﺎﺫﺝ‬ ‫ﻤﻥ‬ ‫ﻤﺨﺘﻠﻔـﻴﻥ‬ ‫ﺍﻟﻤﺸﺘﺭﻜﺔ‬ ‫ﻭﺍﻻﻫﺩﺍﻑ‬ ‫ﺍﻟﻌﻤﻠﻴﺎﺕ‬ ‫ﺒﻌﺽ‬ ‫ﻟﺩﻴﻬﻤﺎ‬ ‫ﻭﻟﻜﻥ‬ ‫ﺍﻻﺼﻁﻨﺎﻋﻲ‬ ‫ﻟﻠﺫﻜﺎﺀ‬. ‫ﺍﻟﻨﻤﻭﺫﺠﺎﻥ‬ ‫ﺒﺘﻁﺒﻴﻕ‬ ‫ﺍﻟﻤﻘﺎﺭﻨﺔ‬ ‫ﺘﻤﺕ‬ ‫ﺍﻟـ‬ ‫ﺒﻴﺎﻨﺎﺕ‬ ‫ﻤﺠﻤﻭﻋﺎﺕ‬ ‫ﺴﺠﻼﺕ‬ ‫ﺒﻌﺽ‬ ‫ﻋﻠﻰ‬ KDD) ‫ﺍﻟﺩﺍﺨﻠـﺔ‬ ‫ﺍﻟﺤﺯﻡ‬ ‫ﺒﻴﺎﻨﺎﺕ‬ ‫ﺘﺴﺠل‬ ‫ﺍﻟﺘﻲ‬ ‫ﻨﻅـﺎﻡ‬ ‫ﺇﻟـﻰ‬ ‫ﺍﻹﻨﺘﺭﻨﺕ‬ ‫ﺨﻼل‬ ‫ﻤﻥ‬ ‫ﺍﻟﺤﺎﺴﻭﺏ‬ (‫ﺠﻴل‬ ‫ﻹﻨﺘﺎﺝ‬ ،) ‫ﺍﻟﺠﻴﻨﻴﺔ‬ ‫ﺍﻟﺨﻭﺍﺭﺯﻤﻴﺔ‬ ‫ﺤﺎﻟﺔ‬ ‫ﻓﻲ‬ (‫ﺤﻴـﺔ‬ ‫ﻤﻀﺎﺩﺍﺕ‬ ‫ﺃﻭ‬ Abs) ‫ﺤﺎﻟﺔ‬ ‫ﻓﻲ‬ ‫ﺍﻟﺴﻼﻟﺔ‬ ‫ﺍﻨﺘﻘﺎﺀ‬ ‫ﻨﻅﺭﻴﺔ‬ (‫ﺍﻟﻤﻬﺎﺠﻤﺔ‬ ‫ﺃﻭ‬ ‫ﻁﺒﻴﻌﻴﺔ‬ ‫ﺍﻟﻐﻴﺭ‬ ‫ﺍﻟﺴﺠﻼﺕ‬ ‫ﺘﻤﻴﺯ‬ ‫ﻴﻤﻜﻨﻬﻤﺎ‬ .

This paper presents two approaches for multiobjective simultaneous coordinated tuning of damping controllers, a modified artificial immune network (MAINet) algorithm and a multiobjective immune algorithm (MOIA). The weighted-sum approach is used to handle the multiobjective optimization problem in the MAINet, while the Pareto-optimization approach is used in the MOIA. To investigate the ability of the proposed algorithms in designing the damping controllers, one small and one large power systems are considered. Two power-system stabilizers (PSSs) are designed for the small power system, while one PSS for a generator and one supplementary controller for a static var compensator (SVC) are designed for the large power system. The simulation studies show that the controllers designed by MOIA perform better than those by MAINet in damping the power-system low-frequency oscillations.

Road traffic causes many problems, such as pollution, insecurity and congestion in great cities. Consequently, it becomes a necessity to develop regulation support systems to improve the fluidity of traffic in urban areas and especially at intersections. In this context, we develop a Traffic Signal Control System (TSCS) based on an Artificial Immune System (AIS) to supervise an isolated intersection and build a regulation strategy as soon as a disturbance (such as congestion or an accident) is detected. AIS is inspired by biological immune system, which exhibits many interesting features, including learning, identification, memory and distributed parallel processing. Our aim is to draw new analogies from biological immunity to take advantage of these features. Thus, the focus of this paper is to identify and implement new AIS concepts and mechanisms to control the traffic fluidity at intersections. The obtained results show that the new concepts and regulation strategies identified,...

Captain, USAF Approved: AFIT/GCS/ENG/07-05 Dedicated to my mother of 63 years who unexpectedly passed away near the completion of this research. You never pushed me to be someone you wanted; rather, you trusted and supported every decision I made for myself. I know you'll be at my graduation-just not in the seat next to me. iv AFIT/GCS/ENG/07-05 Abstract Today's predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 256 65535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of tradeoff solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events based on only the detector's own data structure and the application domain truth set, responding heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate. v Acknowledgments My first thanks always to my Lord and Savior Jesus Christ for giving me all I have. Proverbs 16:9 states, "A man's heart deviseth his way, but the LORD directeth his steps." It is to my good fortune the LORD decided I should attend AFIT. This knowledge and experience serve a greater purpose I have yet to discover. My sincere thanks and heartfelt appreciation to my thesis advisor, Dr. Gary Lamont for showing me that insight begins with the pedagogical example. I also wish to thank my academic advisor and thesis committee member Dr. Paul Williams, Maj, USAF, for his hours of technical expertise and thesis I first looked at that inspired me to formulate the methodology and mechanics of my core research. Thanks to Dr. Peterson who gave and taught the tools to decipher this research's data sets, saving me hours of analysis. I also wish to thank Lt. Col. Timothy Halloran, USAF, for his software engineering precepts and provided project skeletons that unwittingly became the foundation of my software design, GUI layout and seamless use of XML in data saving and loading.

Artificial Immune System (AIS) is a multidisciplinary research area that combines the principles of immunology and computation. Negative Selection Algorithms (NSA) is one of the most popular models of AIS mainly designed for one-class learning problems such as anomaly detection. Positive Selection Algorithms (PSA) is the twin brother of NSA with quite similar performance for AIS. Both NSAs and PSAs comprise of two phases: generating a set D of detectors from a given set S of selves (detector generation phase); and then detecting if a given cell (new data instance) is self or non-self using the generated detector set (detection phase). In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs.

The use of artificial immune systems in intrusion
detection is an appealing concept for two reasons. Firstly, the
human immune system provides the human body with a high
level of protection from invading pathogens, in a robust, selforganized and distributed manner. Secondly, current
techniques used in computer security are not able to cope with
the dynamic and increasingly complex nature of computer
systems and their security.
The objective of our system is to combine several
immunological metaphors in order to develop a forbidding
IDS. The inspiration come from: (1) Adaptive immunity
which is characterized by learning, adaptability, and memory
and is broadly divided into two branches: humoral and cellular
immunity. And (2) The analogy of the human immune systems
multilevel defense could be extended further to the intrusion
detection system itself. This is also the objective of intrusion
detection which need multiple detection mechanisms to obtain
a very high detection rate with a very low false alarm rate.