This Book discusses hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization classification. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. The aim of this book is to introduce research chapters for hybrid approach that able to reduce the rate of false positive alarm, to improve the detection rate and detect zero-day attackers and to get high accuracy for classify intrusion. The NSL-KDD dataset has been used to evaluate the proposed technique. In order to improve classification performance, some steps have been taken on the dataset like feature selection. The classification has been performed by using (Sequential Minimal Optimization SMO + K-mean clustering). After training and testing the hybrid machine learning technique, the results have shown that the proposed technique has achieved a positive detection rate, reduce the false alarm rate and get high accuracy.

Autorentext

Saad Galal received the B.Sc. degree and the M.Sc in Computer Engineering from the Sudan University of Science and Technology (SUST), Khartoum, Sudan, in the field of Computer Engineering Rania Abdelhameed received PhD degrees from the University Putra Malaysia, Kuala Lumpur, and is currently associate professor at faculty of engineering(SUST).