An Adaptive Intrusion Detection Model based on Machine Learning Techniques

Machine Learning Techniques for Intrusion Detection

An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used in today’s IDS are not able to deal with the dynamic and complex nature of cyber attacks on computer networks. Hence, efficient adaptive methods like various...

Machine Learning Techniques for Network Intrusion Detection

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, Intrusion Detection System (IDS) has established ...

AN EVALUATION OF MACHINE LEARNING TECHNIQUES IN INTRUSION DETECTION By

ACKNOWLEDGEMENTS I would like to thank Gabor Karsai, my advisor, for all of his help on this project. Our discussions on intrusion detection and machine learning techniques allowed me to recognize areas I had overlooked and pointed out interesting areas to explore. I would also like to thank Dr. Fisher, my second reader, for his input on the experiments and thesis background. I would like to th...

Survey on Intrusion Detection System using Machine Learning Techniques

In today’s world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied...

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques