Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning. (arXiv:2301.03532v1 [cs.CR])

Through the generalization of deep learning, the research community has
addressed critical challenges in the network security domain, like malware
identification and anomaly detection. However, they have yet to discuss
deploying them on Internet of Things (IoT) devices for day-to-day operations.
IoT devices are often limited in memory and processing power, rendering the
compute-intensive deep learning environment unusable. This research proposes a
way to overcome this barrier by bypassing feature engineering in the deep
learning pipeline and using raw packet …

anomaly detection attack challenges community compute critical deep learning detection devices discuss domain engineering environment identification internet internet of things iot iot devices machine machine learning malware malware identification memory network network security operations power research security things