Abstract: There is often the need to update an installed intrusion detection system (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert knowledge, changes to IDSs are expensive and slow. We describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea is to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. These rules can then be used for misuse detection and anomaly detection. New detection models are incorporated into an existing IDS through a meta-learning (or co-operative learning) process, which produces a meta detection model that combines evidence from multiple models. We discuss the strengths of our data mining programs, namely, classification, meta-learning, association rules, and frequent episodes. We report on the results of applying these programs to the extensively gathered network audit data for the 1998 DARPA Intrusion Detection Evaluation Program.

Abstract: Univ Limburg, Dept Appl Econ, B-3590 Diepenbeek, Belgium.Vanhoof, K, Univ Limburg, Dept Appl Econ, Univ Campus, B-3590 Diepenbeek, Belgium.

Abstract: WINNER OF THE 2001 DEGROOT PRIZE! Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature.

Abstract: Differentiating anomalous network activity from normal network traffic is difficult and tedious. A human analyst must search through vast amounts of data to find anomalous sequences of network connections. To support the analyst's job, we built an application which enhances domain knowledge with machine learning techniques to create rules for an intrusion detection expert system. We employ genetic algorithms and decision trees to automatically generate rules for classifying network connections. This paper describes the machine learning methodology and the applications employing this methodology.

Abstract: To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the /spl alpha/-cuts of equivalence relations and the /spl alpha/-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.

Abstract: Most of the current generation expert systems use knowledge which does not represent a deep understanding of the domain, but is instead a collection of “pattern?action” rules, which correspond to the problem-solving heuristics of the expert in the domain. There has thus been some debate in the field about the need for and role of “deep” knowledge in the design of expert systems. It is often argued that this underlying deep knowledge will enable an expert system to solve hard problems. In this paper we consider diagnostic expert systems and argue that given a body of underlying knowledge that is relevant to diagnostic reasoning in a medical domain, it is possible to create a diagnostic problem-solving structure which has all the aspects of the underlying knowledge needed for diagnostic reasoning “compiled” into it. It is argued this compiled structure can solve all the diagnostic problems in its scope efficiently, without any need to access the underlying structures. We illustrate such a diagnostic structure by reference to our medical system MDX. We also analyze the use of these knowledge structures in providing explanations of diagnostic reasoning.

Abstract: The research and system development work described in this paper is aimed at overcoming some of the problems associated with the development of large, complex linear programming problems. The most overwhelming problem is that of size. It is not uncommon for large planning and policy analysis problems to have tens of thousands of constraints and activities. Matrix generator systems have been designed to help in this process. However, the amount of manual labor involved is still very great and the formulation process is subject to errors which are difficult to detect. We provide an overview of a system which uses artificial intelligence and database techniques to help a knowledgeable user formulate large linear programs. The system automates many of the tedious processes associated with large-scale modeling and provides a top-down development environment with a number of different forms of problem representation.

Abstract: This paper reviews five artificial intelligence tools that are most applicable to engineering problems: knowledge-based systems, fuzzy logic, inductive learning, neural networks and genetic algorithms. Each of these tools will be outlined in the paper together with examples of their use in different branches of engineering. The paper concludes by describing some of the engineering applications at the Cardiff Knowledge-based Manufacturing Centre.

Abstract: In the experiment presented in this paper the Elaboration Likelihood Model (ELM). a social psychological theory of persuasion, was applied to explain why users sometimes agree with the incorrect advice of an expert system. Subjects who always agreed with the expert system's incorrect advice (n = 36) experienced less mental effort, scored lower on recall questions, and evaluated the cases as being easier than subjects who disagreed once or more with the expert system (n = 35). These results show that subjects who agreed with the expert system hardly studied the advice but just trusted the expert system. This is in agreement with the ELM. The experiment also covers an investigation into the factors that moderate user agreement. The results have serious implications for the use of expert systems.

Abstract: The online implementation and results from a hybrid short-term electrical load forecaster that is being evaluated by a power utility are documented in this paper. This forecaster employs a new approach involving a parallel neural-fuzzy expert system, whereby Kohonen's self-organizing feature map with unsupervised learning, is used to classify daily load patterns. Post-processing of the neural network outputs is performed with a fuzzy expert system which successfully corrects the load deviations caused by the effects of weather and holiday activity. Being highly automated, little human interference is required during the process of load forecasting. A comparison made between this model and a regression-based model currently being used in the control centre has shown a marked improvement in load forecasting results.

Abstract: This works focuses on using neural networks and expert systems to control a gas/solid sorption chilling machine. In such systems, the cold production changes cyclically with time due to the batchwise operation of the gas/solid reactors. The accurate simulation of the dynamic performance of the chilling machine has proven to be difficult for standard computers when using deterministic models. Additionally, some model parameters dynamically change with the reaction advancement. A new modelling approach is presented here to simulate the performance of such systems using neural networks. The backpropagation learning rule and the sigmoid transfer function have been applied in feedforward, full connected, single hidden layer neural networks. Overall control of this system is divided in three blocks: control of the machine stages, prediction of the machine performance and fault diagnosis.

Abstract: The essence of expert systems , The essence of expert systems , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Abstract: Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

Abstract: Data mining can discover information hidden within valuable data assets. Knowledge discovery, using advanced information technologies, can uncover veins of surprising, golden insights in a mountain of factual data. Data mining consists of a panoply of powerful tools which are intuitive, easy to explain, understandable, and simple to use. These advanced information technologies include artificial intelligence methods (e.g. expert systems, fuzzy logic, etc.), decision trees, rule induction methods, genetic algorithms and genetic programming, neural networks (e.g. backpropagation, associate memories, etc.), and clustering techniques. The synergy created between data warehousing and data mining allows knowledge seekers to leverage their massive data assets, thus improving the quality and effectiveness of their decisions. The growing requirements for data mining and real time analysis of information will be a driving force in the development of new data warehouse architectures and methods and, conversely, the development of new data mining methods and applications.

Abstract: Rule-based expert systems and artificial neural networks are two major systems for developing intelligent decision support systems. The integration of the two systems can generate a new system which shares the strengths of both rule-based and artificial neural network systems. This research presents a computer based mark-up decision support system called InMES (integrated mark-up estimation system) that integrates a rule-based expert system and an artificial neural network (ANN) based expert system. The computer system represents an innovative approach for estimating a contractor's mark-up percentage for a construction project. A rule extraction method is developed to generate rules from a trained ANN. By using the explanation facility embedded in the rule-based expert system, InMES provides users with a clear explanation to justify the rationality of the estimated mark-up output. Cost data derived from a contractor's successful bids were used to train an ANN and, in conjunction with a rule-based expert s...

Abstract: Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of “what if” analysis needed for creative design. The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria. We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.

Abstract: A fundamental problem with knowledge management is the information technology (IT) employed to enable knowledge work appears to target data and information, as opposed to knowledge itself. In contrast, knowledge-based systems (KBS) maintain an explicit and direct focus on knowledge. The research described in this article is focused on innovating knowledge management through KBS technology. We refer to this KBS-enabled transformation of knowledge work as knowledge-based knowledge management. Drawing from the recent literature, we identify a number of key activities associated with knowledge management to establish a set of requirements for knowledge management support. We match these requirements with textbook capabilities of intelligent systems and use this analysis to evaluate KOPeR, a KBS employed to automate and support knowledge management in the reengineering domain. We find KOPeR possesses the capabilities required for knowledge management support. And its field application, as part of a major reengineering engagement, reveals four important knowledge effects enabled by this KBS. From this study, we also find KOPeR to be effective in its automation and support of key knowledge management activities. And through its successful use and knowledge effects in this study, we conclude that KBS can be developed and employed for effective knowledge management support.

Abstract: This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging results.

Abstract: Method of providing technical support using an expert system including exchanging information between the system and the computing devices in a conversational format utilizing the Internet. The expert system is connected to a knowledge base having data relevant to diagnosing operating conditions of at least one product. The knowledge base may have a tree-like arrangement of rules in which each rule includes a number of antecedents and a solution. Incoming text strings to the expert system are free-form descriptions of the operating conditions of the product for which technical support is sought. The outgoing text strings include questions strings and solutions strings, and the identification of user options, which may include requests for explanations and/or summaries of the session progress. Deduction and induction are employed to identify a rule which includes a solution that resolves the problem experienced by the user who is engaged in a session.

Abstract: Neural networks are just one of the many technologies that are giving businesses a competitive edge. This article explains how neural networks work and how they can be applied in the real estate and financial industries.

Abstract: An expert knowledge site-screening methodology has been developed to evaluate naturally occurring reductive dechlorination as a remedial option for sites with TCE-contaminated groundwater. This methodology combines a causative model for the reductive dechlorination of TCE and expert knowledge within a Bayesian Belief Network. The knowledge base for this expert system was obtained from 22 experts via an expert elicitation protocol. The resulting expert system can be used to aid environmental decision making by evaluating the adequacy of reductive dechlorination at TCE-contaminated sites. Comparisons between this expert system and a commonly used screening tool show that this expert system produces predictive models that may better discriminate between locations that were sampled. The 22 elicitations revealed different beliefs and assumptions among experts about the biochemical processes involved in reductive dechlorination. The decision-making value of some types of evidence is a matter of dispute; however...

Abstract: The present invention provides a method for chemical addition utilizing adaptive process control optimizations having a combination of expert system(s), neural network(s) and genetic algorithm(s).

Abstract: Finding the optimum route of ship pipes is a complicated and time-consuming process. Experience of designers is the main tool in this process. To reduce design man-hours and human errors an expert system shell and a geometric modeling kernel are integrated to automate the design process. Existing algorithms for routing problems have been analyzed - most of them are to solve 2-D circuit routing problems. Design of the ship piping system, especially within the engine room, is a complicated, large-scale 3-D routing problem. Methods of expert systems have been implemented to find the routes of ship pipes on the main deck of a bulk carrier. A framework of the intelligent CAD system for pipe auto-routing is suggested The CADDS 5 of Computervision is used as the overall CAD environment, the Nexpert Object of Neuron Data is used as the expert system shell, and the CADDS 5 ISSM is used to build user interface through which geometric models of pipes are created and modified.

Abstract: Fixture design is a complex and intuitive process, which demands rich experience from the designer. Multiple acceptable designs are possible for a given workpiece and hence the solution space is large. Recent advances in CAD/CAM, especially in artificial intelligence, have alleviated this problem by intelligently restricting the search space considered, thus opening the possibility of obtaining better designs. Researchers have used various techniques under the general rubric of artificial intelligence to solve the fixture design problem. The most common of these have been case-based reasoning and expert systems. However, these two common methods do not ensure that the resulting solution is efficient or optimal. In this paper we propose to combine complementarily the strengths of genetic algorithms and neural networks to develop a fixture design system. Results obtained using this combined multiagent approach for the design of fixtures are promising.

Abstract: An expert network analysis system includes multiple phases which offer a user the ability, at the user's option, to establish pretest conditions through a prompted interview which focuses the performance conditions to be analyzed and which allows the expert system to rank the analyzed results in importance with respect to their relationship to the user established conditions

Abstract: An attribute is deemed important in data mining if it partitions the database such that previously unknown regularities are observable. Many information-theoretic measures have been applied to quantify the importance of an attribute. In this paper, we summarize and critically analyze these measures.