The wide use of Internet of Things (IoT) devices in residences and industries has brought unexpected ease, but concurrently, unprecedented new privacy attacks and cybersecurity threats. Classical security measures lag in tackling the dynamic and complex nature of IoT ecosystems due to limited resources and device variety. This study examines the use of Artificial Intelligence (AI) and Machine Learning (ML) methods to enhance the security posture of IoT ecosystems, specifically to counter data breaches and protect user privacy. Publicly available datasets, the TONIoT and CICIDS2018 datasets, were used to benchmark the performance of several machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. The models were trained and tested on classifying and labelling cyberattacks such as DoS attacks, reconnaissance, and data exfiltration attempts in IoT network traffic and telemetry logs. The findings indicate that CNN recorded the best detection accuracy (94. 3% on TONIoT and 96. 2% on CICIDS2018) and performed better than traditional algorithms, whereas Random Forest recorded the best compromise between performance and computational cost and was thus appropriate for real-time use. The research affirms that intrusion detection in IoT networks can be dramatically enhanced through AI/ML methods and that model choice must be determined on the basis of deployment factors like available computational resources, as well as whether real-time processing is required.
Ighofiomoni et al. (Mon,) studied this question.