  The generalization of deep learning has helped us, in the past, address
challenges such as malware identification and anomaly detection in the network
security domain. However, as effective as it is, scarcity of memory and
processing power makes it difficult to perform these tasks in Internet of
Things (IoT) devices. This research finds an easy way out of this bottleneck by
depreciating the need for feature engineering and subsequent processing in
machine learning techniques. In this study, we introduce a Featureless machine
learning process to perform anomaly detection. It uses unprocessed byte streams
of packets as training data. Featureless machine learning enables a low cost
and low memory time-series analysis of network traffic. It benefits from
eliminating the significant investment in subject matter experts and the time
required for feature engineering.
