Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions
Mohammed Awad, F.M.A. Salam, Khouloud Salameh, Aneesa Al Redhaei
Abstract
The past few years have witnessed a substantial increase in cyberattacks on Internet of Things (IoT) devices and their networks. Such attacks pose a significant threat to organizational security and user privacy. Utilizing Machine Learning (ML) in Intrusion Detection Systems (NIDS) has proven advantageous in countering novel zero-day attacks. However, the performance of such systems relies on several factors, one of which is prediction time. Processing speed in anomaly-based NIDS depends on a few elements, including the number of features fed to the ML model. NetFlow, a networking industry-standard protocol, offers many features that can be used to predict malicious attacks accurately. This paper examines NetFlow features and assesses their suitability in classifying network traffic. Our paper presents a model that detects attacks with (98-100%) accuracy using as few as 13 features. This study was conducted using a large dataset of over 16 million records released in 2021.