Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are not keeping pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research detects malware based on its qualia, or essence rather than its low-level implementation details. By looking for the underlying concepts that make a piece of software malicious, this research avoids the pitfalls of static solutions that focus on predefined bit sequence signatures or anomaly thresholds. 14. ABSTRACT This research develops a novel, hierarchical modeling method to represent a computing system and demonstrates the representation's effectiveness by modeling the Blaster worm. Using Latent Dirichlet Allocation and Support Vector Machines abstract concepts are automatically generated that can be used in the hierarchical model for malware detection. Finally, the research outlines a novel system that uses multiple levels of individual software agents that sharing contextual relationships and information across different levels of abstraction to make decisions. This qualia-based system provides a framework for developing intelligent classification and decision-making systems for a number of application areas.
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Paperback. Etat : new. Paperback. Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are not keeping pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research detects malware based on its qualia, or essence rather than its low-level implementation details. By looking for the underlying concepts that make a piece of software malicious, this research avoids the pitfalls of static solutions that focus on predefined bit sequence signatures or anomaly thresholds. 14. ABSTRACT This research develops a novel, hierarchical modeling method to represent a computing system and demonstrates the representation's effectiveness by modeling the Blaster worm. Using Latent Dirichlet Allocation and Support Vector Machines abstract concepts are automatically generated that can be used in the hierarchical model for malware detection. Finally, the research outlines a novel system that uses multiple levels of individual software agents that sharing contextual relationships and information across different levels of abstraction to make decisions. This qualia-based system provides a framework for developing intelligent classification and decision-making systems for a number of application areas.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781025088167
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