  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 data as input. We introduce a feature
engineering-less machine learning (ML) process to perform malware detection on
IoT devices. Our proposed model, "Feature engineering-less-ML (FEL-ML)," is a
lighter-weight detection algorithm that expends no extra computations on
"engineered" features. It effectively accelerates the low-powered IoT edge. It
is trained on unprocessed byte-streams of packets. Aside from providing better
results, it is quicker than traditional feature-based methods. FEL-ML
facilitates resource-sensitive network traffic security with the added benefit
of eliminating the significant investment by subject matter experts in feature
engineering.
