Abstract
Internet of Things (IoT) devices work mainly in wireless mediums; requiring different Intrusion Detection System (IDS) kind of solutions to leverage 802.11 header information for intrusion detection. Wireless-specific traffic fea-tures with high information gain are primarily found in data link layers rather than application layers in wired networks. This survey investigates some of the com-plexities and challenges in deploying wireless IDS in terms of data collection methods, IDS techniques, IDS placement strategies, and traffic data analysis tech-niques. This paper's main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions. Spe-cifically, the Knowledge Discovery in Databases (KDD) Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network (WN). The paper starts with a review of various intrusion detection techniques, data collection methods and placement methods. The main goal of this paper is to study the design challenges of deploy-ing intrusion detection system in a wireless environment. Intrusion detection sys-tem deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities. So this paper reviews the traditional wired intrusion detection deployment methods and dis-cusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment. The main wireless environments to look into would be Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET) and IoT as this are the future trends and a lot of attacks have been targeted into these networks. So it is very crucial to design an IDS specifically to target on the wireless networks.