Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning

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. NTRODUCTION Cyber Security experts have found pivotal features in network traffic, including packet captures (pcap). Data scientists have used them to fashion impressive models capable of differentiating malicious traffic from benign [1]. However, most network traffic is emitted over encrypted channels in the current scheme. This security measure has limited experts' ability to contrive meaningful features for machine learning (ML), which can soon become obsolete. This challenge has given birth to analyzing raw bytes to detect malicious behavior in internet flows. In the Internet of Things (IoT) domain, devices are sensors that interact with the environment. They conditionally react to changes in the environment and exchange information over the internet about these changes.