Detection and analysis of cyber-attacks on IoT network devices

One of the most pressing concerns in network forensics is the detection of cyber-attacks in the IoT networks and their devices. Traditional intrusion detection systems based on signature rules are unable to detect current attack types. Hence, the need to urgently develop advanced methods for classifying IoT network traffic that can swiftly detect cyber-attacks becomes inevitable. This research aims to develop machine learning algorithms for cyber-attack detection in IoT-based networks, by analyzing the traffic data composed from the network itself. An ideal IoT network was implemented solely for the attack scenarios and generation of a dataset. The self-generated dataset from the IoT network was utilized for the comparison of machine learning algorithms suitable for the classification of attack-to-normal network traffic data. The algorithms are expected to classify the multi-class data into nine classes (normal traffic inclusive).