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Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

With increased global interconnectivity and 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 anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains 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.

Smart Perception System-on-a-Chip (i>SoC) i>systems that sense, receive, transmit, and process signals are the i>eyes and ears in the millennium's need of information fusion and embedded intelligence. For intelligent fusion, the process of storing and analyzing the signals acquired from these i>smart perception systems involves the interoperability of mixed-signal systems. Embedded intelligence involves the collaborative blending of mixed-signal systems with the multidisciplinary areas of mixed-technology domains (electrical, mechanical, optical, thermal, biochemical) and mixed-concept performance (electrical, control, digital signal processing). Collaboration of mixed-signal design differences, impose robust design challenges for multidisciplinary design methodology. Integrating application-specific signal processor cores, memory, analog and i>MEMs functions on the same monolithic chip of silicon involves unique collaborative design methodology in both the macro and micro arenas. Macro collaborative automation tools reflecting rapid prototyping techniques, enabling teams of designers to work together from remote sites is today's realization. Micro collaborative approaches in i>SoC submicron manufacturing capability, requires advances in interoperability simulation technology, hardware description languages, design analysis tools, model availability and accuracy, physical design tools, design synthesis tools, pre-developed and tested libraries of intellectual property, design advisors and test generators is tomorrow's future. At present, no micro collaborative design environment is capable of this methodology. Establishment of cross-tool interoperability design interchange standards that can bridge the gap between future and present collaborative design methodology and reduce the cost of i>SoC design and design time is required.

Trends in computing and networking, in terms of physical capability, attack surface, and attacker sophistication, call for automated, fault-tolerant response systems. Military networks present such environments with unique authorities, critical systems, and threats. Within such environments, multi agent systems may make special contributions regarding recognisance and attack scenarios. We survey three multi agent systems designed for cyber operations, with particular emphasis on our classifier for flow-based attacks, which demonstrates the effectiveness of reputation for distributing classifying agents effectively.

Computer network security has become a very serious concern of commercial, industrial, and military organizations due to the increasing number of network threats such as outsider intrusions and insider covert activities. An important security element of course is network intrusion detection which is a difficult real world problem that has been addressed through many different solution attempts. Using an artificial immune system has been shown to be one of the most promising results. By enhancing jREMISA, a multi-objective evolutionary algorithm inspired artificial immune system, with a secondary defense layer; we produce improved accuracy of intrusion classification and a flexibility in responsiveness. This responsiveness can be leveraged to provide a much more powerful and accurate system, through the use of increased processing time and dedicated hardware which has the flexibility of being located out of band.

A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.

In recent years, there has been increased interest in the use of evolutionary algorithms (EAs) in the design of robust image transforms for use in defense and security applications. An EA replaces the defining filter coefficients of a discrete wavelet transform (DWT) to provide improved image quality within bandwidth-limited image processing applications, such as the transmission of surveillance data by swarms of unmanned aerial vehicles (UAVs) over shared communication channels. The evolvability of image transform filters depends upon the properties of the underlying fitness landscape traversed by the evolutionary algorithm. The landscape topography determines the ease with which an optimization algorithm may identify highly-fit filters. The properties of a fitness landscape depend upon a chosen evaluation function defined over the space of possible solutions. Evaluation functions appropriate for image filter evolution include mean squared error (MSE), the universal image quality index (UQI), peak signal-to-noise ratio (PSNR), and average absolute pixel error (AAPE). We conduct a theoretical comparison of these image quality measures using random walks through fitness landscapes defined over each evaluation function. This analysis allows us to compare the relative evolvability of the various potential image quality measures by examining fitness topology for each measure in terms of ruggedness and deceptiveness. A theoretical understanding of the topology of fitness landscapes aids in the design of evolutionary algorithms capable of identifying near-optimal image transforms suitable for deployment in defense and security applications of image processing.

Contemporary signature-based intrusion detection systems are reactive in nature and are storage-limited. Their operation depends upon identifying an instance of an intrusion or virus and encoding it into a signature that is stored in its anomaly database, providing a window of vulnerability to computer systems during this time. Further, the maximum size of an Internet Protocol-based message requires a huge database in order to maintain possible signature combinations. To tighten this response cycle within storage constraints, this paper presents an innovative artificial immune system (AIS) integrated with a multiobjective evolutionary algorithm (MOEA). This new distributed intrusion detection system (IDS) design is intended to measure the vector of tradeoff solutions among detectors with regard to two independent objectives: best classification fitness and multiobjective hypervolume size. AIS antibody 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. Applied to the MIT-DARPA 1999 insider intrusion detection data set, this new software engineered AIS-MOEA IDS called jREMISA correctly classifies normal and abnormal events at a relative high statistical level which is directly attributed to finding the proper detector affinity threshold.

The timely detection and classification of biological agents in a hostile environment is critical for increasing protection in critical areas. This paper proposes a strategy for identifying biological agents via distributed sensors with an artificial immune system (AIS). The proposed system is composed of networked sensors and nodes, communicating via wireless or wired connections. Measurements are continually taken via dispersed redundant sensors strategically placed in threat areas. These sensors continually measure and categorize air or liquid samples, alerting surrounding nodes when possible biological agents are detected. Detection is based upon the biological immune system (BIS) model of antigens and antibodies, and alerts are generated when a measured sample is determined to be a valid biological agent (antigen). Biological agent signatures (antibodies) are continually distributed throughout the system to adapt to changes in the environment or to new antigens.

Research in steganalysis is motivated by the concern that communications associated with illicit activity could be hidden in seemingly innocent electronic transactions. By developing defensive tools before steganographic communication grows, computer security professionals may be better prepared for the threat. This research investigated an artificial immune system approach to novel steganography detection for digital images. The results show promising detection and false positive rates.

Interest in protein structure prediction is widespread, and has been previously addressed using evolutionary algorithms, such as the simple genetic algorithm (GA), messy GA (mga), fast messy GA (fmGA), and linkage learning GA (LLGA). However, past research used off the shelf software such as GENOCOP, GENESIS, and mGA. In this study we report results of a modified fmGA, which is found to be "good" at finding semi-optimal solutions in a reasonable time. Our study focuses on tuning this fmGA in an attempt to improve the effectiveness and efficiency of the algorithm in solving a protein structure and in finding better ways to identify secondary structures. Problem definition, protein model representation, mapping to algorithm domain, tool selection modifications and conducted experiments are discussed.

The Computer Defense Immune System is an artificial immune system for detecting computer viruses and network intrusions. This paper discusses the system architecture, presents current research and results in enhancing the system, and discusses planned future research topics that will be used to improve the system's capabilities.

Current signature-based approaches for detecting the presence of hidden messages in digital files - the initial step in steganalysis - rely on discerning "footprints" of steganographic tools. Of greater recent concern is detecting the use of novel proprietary or "home-grown" tools for which no signature has been established. This research focuses on detecting anomalies in seemingly innocuous images, applying a genetic algorithm within a computational immune system to leverage powerful image processing through wavelet analysis. The sensors developed with this new, synergistic system demonstrated a surprising level of capability to detect the use of steganographic tools for which the system had no previous exposure.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Publisher Summary The protein folding problem (PFP) involves the determination of the 3D structures of proteins given only their amino acid sequences. One reason for the significant interest in general and efficient techniques to solve this type of problem is that such knowledge would facilitate understanding of the tremendous amount of genetic information produced by the Human Genome Project. This chapter refers to efforts aimed at identifying in vivo structures of naturally occurring proteins or structures of arbitrary polypeptides in arbitrary environments. In particular, it looks at the general problem of identifying three-dimensional structures of arbitrary polypeptides in arbitrary environments, and focuses on a particular kind of evolutionary algorithm (the messy genetic algorithm) for use on this task. It concentrates on improved effectiveness through the exploitation of domain constraints (i.e., dihedral-angle constraints inspired by the Ramachandran plot) and the exploitation of prior secondary structure analysis. The work described is performed on high-grade parallel and heterogeneous computing resources.

Computer viruses are widely recognised as a significant computer threat. The "birth rate" of new viruses is high and increasing due to global connectivity, and technology improvements can accelerate their spread. In response to this threat, some contemporary research efforts are aimed at creating computer virus immune systems (CVIS). A CVIS uses the human immune system as a model for identifying, attacking, and eradicating viruses from computers and networks. This paper analyses the requirements of such a computer virus immune system and evaluates current approaches with respect to these requirements. Based on this analysis, we propose a distributed architecture for implementing a CVIS. In particular, we discuss how emerging technologies such as evolutionary algorithms (EA) and intelligent agents (IA) can be employed to give the CVIS a self-adaption capability for new viral threats.

Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.

The concepts of efficiency and effectiveness must be addressed in conducting research into using a Evolutionary Algorithm (EA) for optimization problems. The increased use of evolutionary approaches for real-world applications, containing multiple objectives and high dimensionality, has led to the design and generation of a number of Multiobjective Evolutionary Algorithms (MOEA). When analyzing these algorithms, the issues of effectiveness and efficiency are extremely important and typically drive the urge to parallelize these algorithms. The parallelization of MOEAs is a relatively new concept, with few researchers contributing work in this area. This parallelization process is not a simple task and involves the analysis of various parallel models and the parameters associated with these models. This paper presents a thorough analysis of the various parallel MOEA models, the issues associated with these models and recommendations for using these models in MOEAs. In particular, these parallelization concepts are applied to the Multiobjective Messy Genetic Algorithm II.

Why test MOEAs? To evaluate, compare, classify, and improve algorithm performance (effectiveness and efficiency)! What is an MOEA test? An MOP test function, an MOP test suite, pedagogical functions, or a real-world problem? How to find an appropriate MOEA test? MOEA literature, historical use, test generators, or well known real-world applications. When to test? Incremental algorithm and test development starting early or wait until end of development. How to design an MOEA test? Assumptions, computational platform selection, statistical tools, metric selection, experimental plan, and an on-going process. Therefore, considerable effort must be expended not only to define proper MOP tests and generate the proper design of MOEA experiments, but also to employ the honest selection of appropriate testing metrics and associated statistical evaluation and comparison. In this chapter, the development of various MOP test suites is addressed, and in the next chapter, their use in appropriate MOEA evaluations is discussed.

Regarding the scientific method of experimentation, it is desirable to construct an accurate, reliable, consistent and non-arbitrary representation of MOEA architectures and performance over sets of MOPs. In particular, through the use of standard procedures and criteria, one should attempt to minimize the influence of bias or prejudice of the experimenter when testing an MOEA hypothesis. The design of each experiment must conform then to an accepted “standard” approach as reflected in any generic scientific method. When employing this approach, the detailed design of MOEA experiments can draw heavily from outlines presented by Barr et al. (1995), and Jackson et al. (1991). These generic articles discuss computational experiment design for heuristic methods, providing guidelines for reporting results and ensuring their reproducibility. Specifically, they suggest that a well-designed experiment follow these steps: (1) Define experimental goals; (2) Choose measures of performance (metrics); (3) Design and execute the experiment; (4) Analyze data and draw conclusions; and (5) Report experimental results. This chapter applies these concepts in developing experimental MOEA testing procedures using appropriate MOP test suites from Chapter 3.

Wave optics propagation codes are widely used to simulate the propagation of electromagnetic radiation through a turbulent medium. The basis of these codes is typically the two dimensional Fast Fourier Transform (FFT). Conventional FFTs (i.e. the standard Matlab FFT) do not use parallel processing and for large arrays, the processing time can be cumbersome. This research investigates the use of network- based parallel computing using personal computers. In particular, this study uses the Air Force Institute of Technology (AFIT) Bimodal Cluster a heterogeneous cluster of PCs connected by fast Ethernet for parallel digital signal processing using an FFT algorithm developed for use on this system. The parallel algorithms developed for the Parallel Distributed Computing Laboratory could greatly increase the computational power of current wave optics codes. The objective of this research is to implement current parallel FFT algorithms for use with wave optics propagation codes and quantify performance enhancement. With the parallel version of the FFT implemented into existing wave optics simulation code, high resolution simulations can be run in a fraction of the time currently required using conventional FFT algorithms. We present the results of implementing this parallel FFT algorithm and the enhanced performance achieved over the Matlab FFT2 function.

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.

The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.

The US military sees a great use for software agent technology in its `synthetic battlespace', a Distributed Virtual Environment currently used as a training and planning arena. The Computer Generated Actors (CGAs) currently used in the battlespace display varying capabilities, but state of the art falls short of presenting believable agents. This lack of `believability' directly contributes to simulation and participant inconsistencies. Even if CGAs display believable behavior no formalized methodology exists for judging that display or for comparing CGA performance. A formal method is required to obtain a quantitative measurement of performance for use in assessing a CGA's performance in some simulation, and thus its suitability for use in the battlespace. This paper proposes such a quantitative evaluation method for determining an agent's observed degree of performance as related to skills. Since the method delineates what is being measured and the criteria upon which the measurement is based, it also explains the particular evaluation given for specific military CGAs.

Popular press and congressional record report a belief by the intelligence community that Al Qaeda members communicate through messages embedded invisibly in images shared via the Internet. This is certainly plausible as steganography has a rich history of military and civilian use. Current signature-based approaches for detecting the presence of hidden messages rely on discerning "footprints" of steganographic tools. Of greater recent concern is detecting the use of novel tools for which no signature has been established. This research addresses this concern by using a method for detecting anomalies in seemingly innocuous images, applying a genetic algorithm within a computational immune system to leverage powerful image processing through wavelet analysis. The sensors developed with this system demonstrated a surprising level of capability to detect the use of steganographic tools for which the system had no previous exposure, including one tool designed to be statistically stealthy.

The motivation for this development is a need to construct a robot simulation facility which will assist in the development of effective algorithms to plan and control robot movements. It was thought that a system that presented robot's movements with an animated graphic display would be useful in the design and testing of robot planning and control programs. In this research a "programmer's apprentice", called the Graph Design Assistant (GDA), has been designed to help researchers construct robot simulation models on an Evans and Sutherland PS 300 graphic workstation. A programmer's apprentice is an expert system with knowledge about a programming task. It guides and advises a programmer as he interactively builds a program. This article describes how the system allows a researcher to develop a program on the PS 300 without having to learn the intricacies of the PS 300's dataflow language. In this system, plans are templates for portions of a graph in the functional graph network. The plans are similar to the macros used in the Evans and Sutherland Functional Graph Network Editor. The difference is that plans have slots which can be expanded to arbitrary structures. Some of the other aspects of the GDA are the development of an interactive interface, a library of plans, a user controlled agenda, consistency checker, and plan creation and editing capability.© (1985) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Contemporary computer-aided design (CAD) programs incorporate excellent functional tools but can be enhanced through Artificial Intelligence (AI), particularly expert systems. Expert systems(ES) for design, however, must incorporate "design process models" explicitly. To study the various phases of such a development, a specific expert system has been generated to aid the control engineer in some aspects of control system design. Conventional and AI programming concepts are combined to generate a generic "design associate". Through this development insight evolved regarding concepts, modeling and implementation of general expert systems for design. This paper discusses these aspects as they relate to control system design as well as to the general area of AI expert systems for CAD. The various components of expert system organization including human-machine interfacing, search processing and knowledge representation selections are analyzed with a general ES architecture for CAD control engineering applications provided.