Anomaly Based Intrusion Detection in Computer Networks Using Generative Adversarial Networks, M.Sc. Thesis Sharif University of Technology ; Hemmatyar, Ali Mohammad Afshin (Supervisor)
Abstract
Due to the rapid development of computer networks, security is a major concern. Methods of intruding computer networks are also rapidly developing, and there is a new method every day. These facts corroborate the need for new and more intelligent mechanisms for detecting intrusion. To detect intrusion, one must analyze the network traffic. The most used traditional methods of traffic separation are port-based and payload based detection. The former is not so efficient, and the latter is not only inefficient but also violates the privacy of users. Unsatisfied by such methods, researchers adopted machine learning techniques and tried to develop new solutions for detecting intrusion. Methods...
Cataloging briefAnomaly Based Intrusion Detection in Computer Networks Using Generative Adversarial Networks, M.Sc. Thesis Sharif University of Technology ; Hemmatyar, Ali Mohammad Afshin (Supervisor)
Abstract
Due to the rapid development of computer networks, security is a major concern. Methods of intruding computer networks are also rapidly developing, and there is a new method every day. These facts corroborate the need for new and more intelligent mechanisms for detecting intrusion. To detect intrusion, one must analyze the network traffic. The most used traditional methods of traffic separation are port-based and payload based detection. The former is not so efficient, and the latter is not only inefficient but also violates the privacy of users. Unsatisfied by such methods, researchers adopted machine learning techniques and tried to develop new solutions for detecting intrusion. Methods...
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