Оптимизация параметров индексной формальной иммунной сети

This article proposed an index structure for a formal immune network. It introduced a new notation of risk index, set the objective of optimizing index parameters in terms of mean square performance criterion, which was then solved.В статье предложена структура индексной формальной иммунной сети, введено понятие индекса риска, поставлена и решена задача оптимизации параметров индекса в смысле среднеквадратического критерия качества

An ecological approach to anomaly detection: the EIA Model.

The presented work proposes a new approach for anomaly detection. This approach is based on changes in a population of evolving agents under stress. If conditions are appropriate, changes in the population (modeled by the bioindicators) are representative of the alterations to the environment. This approach, based on an ecological view, improves functionally traditional approaches to the detection of anomalies. To verify this assertion, experiments based on Network Intrussion Detection Systems are presented. The results are compared with the behaviour of other bioinspired approaches and machine learning techniques

Индексы психологической устойчивости как новый подход к формированию групп риска

Immunocomputing techniques have been used to create new indices that estimate the risk of an inadaptability of juvenile crisis in a given group of young people in a «hard» environment. Application examples have been provided using the psychological tests of cadets of the Peter the Great Military-Space School in St. Petersburg.На основе методов иммунокомпьютинга предложены новые индексы, характеризующие риск развития дезадаптационных нарушений у подростков в условиях нахождекния в «жесткой» образовательной среде. Применение индексов наглядно продемонстрировано на данных психологического обследования учащихся Военно-космического кадетского корпуса им. Петра Великого г. Санкт-Петербург

Обеспечение прав доступа к системам с мандатной политикой информационной безопасности

The machinery of information protections from unauthorized access by access limitation of the corporative information recourses according to the mandate politics of information security have considered. The processes of control and properties analysis of law access matrix according to objects and subjects protection hierarchy have presented. The conditions and procedures of filling and forming of this matrix with execution of basic demarcation access rules have considered.Представлены механизмы защиты информации от несанкционированного доступа путем ограничения доступа к распределенным информационным ресурсам с учетом мандатной политики информационной безопасности. Формализованы процессы контроля и анализа свойств матрицы прав доступа с учетом иерархии субъектов и объектов защиты. Рассмотрены условия и процедуры заполнения и формирования этой матрицы при выполнении основных правил разграничения доступа

Design of Automated Website Phishing Detection using Sequential Mechanism of RCL Algorithm

The phishing outbreaks in internet has become a major problem in web safety in recent years. The phishers will be stealing crucial economic data regarding the web user to perform economic break-in. In order to predict phishing websites, many blacklist-based phishing website recognition methods are used in this study. Traditional methods of detecting phishing websites rely on static features and rule-based schemes, which can be evaded by attackers. Recently, Deep Learning (DL) and Machine Learning (ML) models are employed for automated website phishing detection. With this motivation, this study develops an automated website phishing detection using the sequential mechanism of RCL algorithm. The proposed model employs Long-Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest (RF) models for the detection of attacks in the URLs and webpages by the similarity measurement of the decoy contents. The proposed model involves three major components namely, RF for URL phishing detection, CNN based phishing webpage detection, and LSTM based website classification (i.e., legitimate and phishing). The experimental result analysis of the RCL technique is tested on the benchmark dataset of Alexa and PhishTank. A comprehensive comparison study highlighted that the RCL algorithm accomplishes enhanced phishing detection performance over other existing techniques in terms of distinct evaluation metrics

Immune systems inspired multi-robot cooperative shepherding

Certain tasks require multiple robots to cooperate in order to solve them. The main problem with multi-robot systems is that they are inherently complex and usually situated in a dynamic environment. Now, biological immune systems possess a natural distributed control and exhibit real-time adaptivity, properties that are required to solve problems in multi-robot systems. In this thesis, biological immune systems and their response to external elements to maintain an organism's health state are researched. The objective of this research is to propose immune-inspired approaches to cooperation, to establish an adaptive cooperation algorithm, and to determine the refinements that can be applied in relation to cooperation. Two immune-inspired models that are based on the immune network theory are proposed, namely the Immune Network T-cell-regulated---with Memory (INT-M) and the Immune Network T-cell-regulated---Cross-Reactive (INT-X) models. The INT-M model is further studied where the results have suggested that the model is feasible and suitable to be used, especially in the multi-robot cooperative shepherding domain. The Collecting task in the RoboShepherd scenario and the application of the INT-M algorithm for multi-robot cooperation are discussed. This scenario provides a highly dynamic and complex situation that has wide applicability in real-world problems. The underlying 'mechanism of cooperation' in the immune inspired model (INT-M) is verified to be adaptive in this chosen scenario. Several multi-robot cooperative shepherding factors are studied and refinements proposed, notably methods used for Shepherds' Approach, Shepherds' Formation and Steering Points' Distance. This study also recognises the importance of flock identification in relation to cooperative shepherding, and the Connected Components Labelling method to overcome the related problem is presented. Further work is suggested on the proposed INT-X model that was not implemented in this study, since it builds on top of the INT-M algorithm and its refinements. This study can also be extended to include other shepherding behaviours, further investigation of other useful features of biological immune systems, and the application of the proposed models to other cooperative tasks

Real life applications of bio-inspired computing models: EAP and NEPs

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 04-07-201