A METHOD AND ALGORITHMS FOR DETECTING CYBERATTACKS IN INTERNET OF THINGS NETWORKS BASED ON ENERGY CONSUMPTION ANALYSIS
DOI:
https://doi.org/10.17721/2519-481X/2025/88-07Keywords:
Internet of Things, cyberattack, DDoS, cryptomining, malware detection, energy consumption, sequential pattern mining, opcode analysisAbstract
A «Smart home» is a system for managing the essential life support processes of both small systems (commercial, office spaces, apartments, cottages) and large automated commercial and industrial complexes. An important task that the modern «Smart home» concept must address is preventing the spread of malicious software within the Internet of Things (IoT) infrastructure. Despite the vast number of developed methods for detecting and preventing cyberattacks, IoT devices are still extremely vulnerable and suffer a wide range of cyberattacks, which have both financial and reputational consequences. Therefore, there is a need to develop new approaches for detecting malicious software in the IoT infrastructure. One possible approach for detecting the anomalous behavior of malware-infected IoT devices is to monitor their energy consumption. Infected devices often exhibit an increase in energy consumption, which can be an indicator of malicious activity, such as DDoS attacks or cryptomining.
This article presents a method for detecting attacks in the IoT infrastructure based on the analysis of IoT device energy consumption, which takes into account user preference modes related to device energy consumption. To enhance the accuracy of cyberattack detection and the localization of malicious software on devices, an analysis of software opcode sequences is applied. The proposed approach provides the ability to effectively detect cyberattacks, such as DoS/DDoS, with an accuracy of approximately 99.88%, and to localize malicious software on devices. Thus, monitoring the energy consumption of smart heating, ventilation, air conditioning (HVAC) devices, and other IoT devices is an approach to increasing the effectiveness of cyberattack detection in the IoT infrastructure.
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