A novel industrial wireless sensor networks dataset based on low-energy adaptive clustering hierarchy (LEACH) protocol for detecting blackhole and sinkhole attacks using a lightweight machine learning based intrusion detection system

Abstract

Wireless sensor networks (WSNs) are widely applied in industrial scenarios for monitoring energy consumption, and their security should be guaranteed at all times. Unfortunately, they are not sufficiently secure because they have limited energy; therefore, they cannot afford to implement large-scale intrusion detection systems (IDSs). In addition, there is a lack of proper datasets related to industrial WSNs (IWSNs) to evaluate IDSs; thus, this study proposes an IWSN dataset based on OMNeT++. The proposed dataset is implemented using the low-energy adaptive clustering hierarchy (LEACH) protocol, and it validates the sinkhole and blackhole attack. We design a lightweight IDS named IWSN-SLEACH-IDS, which employs classification and regression trees (CART) to detect the sinkhole and blackhole attack, because the sinkhole and blackhole attack damage the factory, which relies on high-quality data. The defective product will cause millions of dollars of loss; therefore, CART achieves nearly 100% detection rate for the attack, and it consumes little additional energy, thus making it a suitable solution.