Deep learning network security - Wang, Si (The Institution of Engineering and Technology, 2021)
The explosion of data usage has contributed to the requirement of processing extensive amount of data for most of the applications on smart devices and edge- and fog- computing nodes. Due to the scale and complexity of the tasks, decision support systems can greatly benefit from the use of machine learning (ML) techniques to correlate multimodal sensing to make accurate predictions and powerful inferences. Traditional ML algorithms have to be fed with previously extracted features. These features are usually identified in advance to reduce the complexity of the data and increase the visibility of the patterns to the learning algorithms [1]. Furthermore, in some circumstances, like multiple object detection, the task needs to be divided into parts and solved individually and the partial results are combined at the final stage. The required human intervention and discontinuity in the process of accomplishing the tasks contribute to the reduced efficiency of the conventional ML algorithms in the face of massive raw data and intricate tasks. Deep learning (DL), also referred to as deep neural network (DNN), has overcome the weakness of the need for human’s participation on effective feature identification and hard-core feature extraction. It learns the high-level features from raw data in an incremental manner and solves the problems end-to-end. As a result, DL has now become a preferred option for handling majority of the challenging tasks in image classification [2], speech recognition and language processing [3].