Enhancing intrusion detection system performance using machine learning approaches / Hany Mohammed Ahmed ; Supervised Hesham Hefny , Abdelaziz Ahmed

Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences

With the growth of the Internet, more and more people are getting connected to the Internet every day to take advantage of internet benefits. On one side, the Internet brings in wonderful potential to business and individuals in terms of reaching the end users. At the same time it also brings in portion of security risk to the individuals and organizations over the network. With the growth of cyberattacks, information safety has become an important issue all over the world. Intrusion detection systems (IDSs) are an essential element for network security infrastructure The topic of Computer Network Intrusion Detection Systems (IDSs) is a very interesting research issue actively pursued by many investigators. The goal of intrusion detection is to monitor network assets and to detect abnormal behavior and misuse. This concept has been around for the past several years but only recently it has seen a dramatic rise in interest of researchers and system developers for incorporation into the overall information security infrastructure. The need for an IDS has become critical since the number of novel attacks, internet worms and malwares has kept growing in recent years, besides a huge size of datasets in real world in different networks and in the internet used for IDS represents a real challenge for most researcher works. However, most of today{u2019}s IDS generate high detection accuracy and miss many attacks because of a deficiency in their ability to discriminate attacks from legitimate behaviors. These unreliable results damage the main task of IDSs

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