Intrusion Detection Systems for Smart Home IoT Devices: Experimental Comparison Study
Faisal Alsakran, Gueltoum Bendiab, Stavros Shiaeles, Nicholas, Kolokotronis

TL;DR
This paper compares open-source network intrusion detection systems (NIDS) for smart home IoT devices, evaluating their detection accuracy and resource consumption to identify the most suitable option.
Contribution
It provides an experimental comparison of Snort, Suricata, and Bro IDS specifically for smart home environments, highlighting Suricata as the best performer.
Findings
Suricata has the highest detection accuracy.
Suricata consumes less CPU and memory.
Snort and Bro are less efficient for smart homes.
Abstract
Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the…
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