A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Kevin Fauvel, Fuxing Chen, Dario Rossi
Abstract
Traffic classification, i.e., the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This task faces some critical challenges that current deep learning approaches do not address. The design of current approaches do not take into consideration the fact that networking hardware (e.g., routers) often runs with limited computational resources. Further, they do not meet the need for faithful explainability highlighted by regulatory bodies. Finally, these traffic classifiers are evaluated on small datasets which fail to reflect the diversity of applications in real-world settings.
Topics & Concepts
Computer scienceConvolutional neural networkTask (project management)Identification (biology)Routing (electronic design automation)Intrusion detection systemThe InternetArtificial neural networkArtificial intelligenceMachine learningComputer networkWorld Wide WebEngineeringSystems engineeringBotanyBiologyInternet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionAdvanced Malware Detection Techniques