Features Quality Impact on Cyber Physical Security Systems
Alaa Alhowaide, Izzat Mahmoud Alsmadi, Jian Tang · 2019
With the rapid growth of the number of connected devices on the internet, a significant increase in internet and network applications in the last decade led to the appearance of function-based networks called the Internet of Things (IoT). Such interest and dependency come with exponentially increasing security threats. Those threats are very dynamic, and change continuously despite the fact that IoT devices may not go through the same speed for security fixes, updates, etc. As a result, research trends focus on utilizing data analytics to build intelligence and autonomous mechanisms to enable IoT systems to respond to threats in real time. To this end, our project goal is to evaluate methods to build fast and efficient intrusion detection systems (IDS) that meet the high scalability and dynamicity of IoT environments.In this research, we studied three different datasets used to represent IoT network traffic and used them as a benchmark to evaluate many IDSs' performances. Results showed that features' quality has an impact on classification models and thus IDS performance. Our results showed also that the optimum time-window of an IDS preferred to be long (decay factor 0.01 or 0.1) when evaluating the BotNet_IoT dataset.