A Stratified IoT Deep Learning based Intrusion Detection System

Idriss Idrissi, Mostafa Azizi, Omar Moussaoui

20222022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)20 citationsDOI

Abstract

The Internet of Things (IoT) enables billions of intelligent linked devices to communicate through the IP standard. With the Internet and the Cloud Computing environment, nearly any system can be established and turned smarter. Regardless of their size or form, all IoT devices need processing, security, sensing, and actuation to work successfully; yet, there are currently no IoT-specific security standards. Users and IoT devices security are often neglected by IoT product designers and manufacturers. The functionality of some IoT devices may also be manipulated or handicapped by malicious actors, causing infected IoT devices to behave differently when it comes to defending against these attacks, as well as being a part of these attacks. To address these issues, we propose in this paper a Stratified Deep Learning Based-Intrusion Detection System (SDL-IDS) for the IoT environment at the three levels, Edge, Fog, and Cloud, in order to enhance the security of IoT networks, this proposed SDL-IDS is composed of blocks that act in collaboration.

Topics & Concepts

Internet of ThingsComputer scienceIntrusion detection systemCloud computingComputer securityEdge computingEnhanced Data Rates for GSM EvolutionAttack surfaceThe InternetIntrusionWorld Wide WebArtificial intelligenceOperating systemGeochemistryGeologyNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting