Abderezak Touzene, Ahmed Al Farsi and Nasser Al Zeid, College of Science, Sultan Qaboos University, Oman

With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic (Big Data) are challenging to store, deal with and analyse to detect normal or cyber-attack traffic. In this paper we developed an Intrusion Detection System (NMF- IDS) based on Non-Negative Matrix Factorization dimension reduction technique to handle the large traffic datasets and efficiently analyses them in order to detect with a good precision the normal and attack traffic. The experiments we conducted on the proposed IDS-NMF give better results than the traditional ML-based intrusion detection systems, we have got an excellent detection accuracy of 98%.

Intrusion Detection Systems, Machine Learning, Dimensionality Reduction, IoT traffic