IPCA-SAMKNN: A Novel Network IDS for Resource Constrained Devices

Intrusion Detection Systems (IDSs) in traditional computing systems have played a significant role in detecting and preventing cyber-attacks. Unsurprisingly, the same technology is used to detect and prevent cyber attacks in Internet of Things (IoT) environments. However, due to the computational constraints of IoT devices, traditional computing-based IDS is challenging to deploy on IoT devices. Moreover, IDS for IoT environments should have high classification performance, low complexity models, and small model sizes. Despite numerous advances in IoT-based intrusion detection, developing models that achieve high classification performance while being less complex and smaller in size remains difficult. This study proposes a novel IDS for resource-constrained devices like IoT systems by using a blend of incremental principal component analysis (IPCA) and Self Adjusting Memory KNN (SAM-KNN) to develop a lightweight machine learning model to detect intrusions in IoT systems. The proposed system was deployed on a Raspberry Pi Model B, representing a resource-constrained device, and evaluated using the UNSW-NB15 dataset. The experimental results show a superior accuracy of 98.91%, a memory overhead of 1.4%, 1.6% and 2% overhead for CPU and energy, respectively.