Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network

The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications for dealing with such incomplete data are introduced, and performance is assessed on an emitter identification task using a data base of radar pulsesDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409) (S.G. and M.A.R); National Science Foundation (IRI-97-20333) (S.G.); Natural Sciences and Engineerging Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

К организации интеллектуальной защиты информации

The systems of protection are considered which are adaptive to dynamics of threats and computer attacks. The two-level hierarchical model of adaptive protection is offered. The bottom adaptive level is intended for operative reaction to dynamics of an external environment. Therefore it should be intellectual (by analogy with immune mechanisms of biological system, which work automatically). The top adaptive level corresponds to processes of generalization and storing of the central nervous system. It is focused on use of intelligence of the safety manager as a component of model.Рассмотрены вопросы организации системы защиты информации (СЗИ), структура которой ориентирована на процессы адаптации к динамике угроз и компьютерных атак. Показано, что в двухуровневой иерархической модели адаптивной СЗИ нижний адаптивный уровень, ответственный за оперативную реакцию на динамику внешнего окружения, должен быть интеллектуальным (по аналогии с иммунными механизмами биологической системы, которые работают автоматически, практически без коррекции со стороны центральной нервной системы), а верхний адаптивный уровень (соответствует процессам обобщения и запоминания центральной нервной системы) ориентирован на использование интеллекта администратора безопасности в качестве компонента модели

Fuzzy-Neural Cost Estimation for Engine Tests

This chapter discusses artificial computational intelligence methods as applied to cost prediction. We present the development of a suite of hybrid fuzzy-neural systems for predicting the cost of performing engine tests at NASA’s Stennis Space Center testing facilities. The system is composed of several adaptive network-based fuzzy inference systems (ANFIS), with or without neural subsystems. The output produced by each system in the suite is a rough order of magnitude (ROM) cost estimate for performing the engine test. Basic systems predict cost based solely on raw test data, whereas others use preprocessing of these data, such as principal components and locally linear embedding (LLE), before entering the fuzzy engines. Backpropagation neural networks and radial basis functions networks (RBFNs) are also used to aid in the cost prediction by merging the costs estimated by several ANFIS into a final cost estimate

A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets

The project objective in this work is to create an accurate cost estimate for NASA engine tests at the John C. Stennis Space Center testing facilities using various combinations of fuzzy and neural systems. The data set available for this cost prediction problem consists of variables such as test duration, thrust, and many other similar quantities, unfortunately it is small and incomplete. The first method implemented to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inference system (ANFIS). The second method is a two stage system that uses various ANFIS with either single or multiple inputs for a cost estimate whose outputs are then put through a backpropagation trained neural network for the final cost prediction. Finally, method 3 uses a radial basis function network (RBFN) to predict the engine test cost

A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets

The project objective in this work is to create an accurate cost estimate for NASA engine tests at the John C. Stennis Space Center testing facilities using various combinations of fuzzy and neural systems. The data set available for this cost prediction problem consists of variables such as test duration, thrust, and many other similar quantities, unfortunately it is small and incomplete. The first method implemented to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inference system (ANFIS). The second method is a two stage system that uses various ANFIS with either single or multiple inputs for a cost estimate whose outputs are then put through a backpropagation trained neural network for the final cost prediction. Finally, method 3 uses a radial basis function network (RBFN) to predict the engine test cost

Анализ биоинспирированных подходов для защиты компьютерных систем и сетей

Nowadays more and more different bio-inspired approaches (based on a biological metaphor) for the computer and networks security systems are mentioned and advertised. Traditional computer-based systems and their functionality are often limited by different conditions. Due to frequent minor errors, these systems are subject of failure. They lack scalability, have low adaptation ability to changeable conditions of functioning and its goals. As opposed to traditional computer-based systems, biological systems are often quite reliable. They have great self-protection mechanisms, highly scalable, adaptable and able to self regeneration. These properties of biological systems can be used to construct technical systems (including information security systems). The paper considers different approaches to the protection of computer systems and networks, which are based on a biological metaphor.В настоящее время в области безопасности компьютерных систем и сетей все чаще упоминаются и рекламируются различные биоинспирированные подходы, то есть подходы, основанные на биологической метафоре. Действительно, традиционные компьютерные методы и системы, как правило, ограничены по своим функциональным возможностям, подвержены частому выходу из строя из-за незначительных ошибок, имеют недостаточную масштабируемость, не обладают способностью к адаптации к условиям функционирования и изменению целей. В противоположность этому, биологические системы, как правило, реализуют развитые механизмы самозащиты, достаточно надежны, обладают высокой масштабируемостью, адаптивны и способны к саморегенерации. Указанные свойства биологических систем стимулируют использование принципов их построения и механизмов их функционирования в технических системах, включая системы защиты информации. В данной статье рассматриваются различные подходы к защите компьютерных систем и сетей, в основе которых лежит биологическая метафора

Anomaly detection in unknown environments using wireless sensor networks

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications

Classification of incomplete data using the Fuzzy ARTMAP neural network

CENTER FOR ADAPTIVE SYSTEMS AND DEPARTMENT OF COGNITIVE AND NEURA