Journal
SENSORS
Volume 22, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/s22155626
Keywords
smart home security; anomaly detection; mechanical learning
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The Smart Home has become a target of cyberattacks, and existing security features are inadequate to protect it. A Network-Based Intrusion Detection System is proposed as a forefront security solution. This paper introduces a novel method to assist classification machine learning algorithms in detecting abnormal network behavior of IoT devices in Smart Homes.
The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%.
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