Web Anomaly Host-Based IDS, Using Computational Intelligence Approach, M.Sc. Thesis Sharif University of Technology ; Azmi, Reza (Supervisor)
Abstract
In this thesis we propose a two-layer hybrid fuzzy genetic algorithm for designing anomaly based an Intrusion Detection System. Our proposed algorithm is based on two basic Genetic Based Machine Learning Styles (i.e. Pittsburgh and Michigan). The Algorithm supports multiple attack classifications; it means that the algorithm is able to detect five classes of network patterns consisting of Denial of Service, Remote to Local, User to Root, Probing and Normal class.
Our proposed algorithm has two approaches. In the first approach we choose Pittsburgh style as the base of the algorithm that provides a global search. Then combine it with Michigan style to support local search. In this... Cataloging briefWeb Anomaly Host-Based IDS, Using Computational Intelligence Approach, M.Sc. Thesis Sharif University of Technology ; Azmi, Reza (Supervisor)
Abstract
In this thesis we propose a two-layer hybrid fuzzy genetic algorithm for designing anomaly based an Intrusion Detection System. Our proposed algorithm is based on two basic Genetic Based Machine Learning Styles (i.e. Pittsburgh and Michigan). The Algorithm supports multiple attack classifications; it means that the algorithm is able to detect five classes of network patterns consisting of Denial of Service, Remote to Local, User to Root, Probing and Normal class.
Our proposed algorithm has two approaches. In the first approach we choose Pittsburgh style as the base of the algorithm that provides a global search. Then combine it with Michigan style to support local search. In this... Find in contentBookmark |
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