Abstract:
Due to advancements in technology, electrical appliances are now inter-connected. The goal of Internet -of-things (IoT) is to access every appliance or device through the Internet. This is done in order to operate these gadgets from remote locations. The goal is to improve our day-to-day life. However, this technology raises serious privacy and security issues. As IoT devices are resource-constrained, it is impractical to secure them using traditional approaches. Hence, a light-weight Intrusion detection system (IDS) is required. In this work, we implement a machine learning based Network Intrusion Detection (NID) system in a multi-node fog environment using a Raspberry Pi cluster on a local area network. The proposed Pi-IDS system has been evaluated on ADFA-LD datasets. These datasets comprise of new generation system calls for various attacks on different applications. The proposed fog architecture offers significant advantages in terms of latency, energy consumption and cost over traditional cloud or dedicated personal computer systems. The experiments show that we are able to achieve a Recall of 89%in ADFA-LD with the XGBoost model. The proposed system was able to predict intrusion with an inference time 130 ms in comparison to Cloud with 735 ms, with an estimated running cost of 201 INR/month in comparison to the Cloud cost of 2051 INR/month.
