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Transformers: Intrusion Detection Data In Disguise

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Published
Publication date18/09/2020
<mark>Original language</mark>English
Event3rd International Workshop on Attacks and Defences for Internet-of-Things - Online, Surrey, United Kingdom
Duration: 19/09/202019/09/2020
http://adiot2020.compute.dtu.dk/

Workshop

Workshop3rd International Workshop on Attacks and Defences for Internet-of-Things
Abbreviated titleADIoT
CountryUnited Kingdom
CitySurrey
Period19/09/2019/09/20
Internet address

Abstract

IoT cyber security deficiencies are an increasing concern for users, operators, and developers. With no immediate and holistic devicelevel fixes in sight, alternative wraparound defensive measures are required. Intrusion Detection Systems (IDS) present one such option, and represent an active field of research within the IoT space. IoT environments offer rich contextual and situational information from their interaction with the physical processes they control, which may be of use to such IDS. This paper uses a comprehensive analysis of the current stateof-the-art in context and situationally aware IoT IDS to define the often misunderstood concepts of context and situational awareness in relation to their use within IoT IDS. Building on this, a unified approach to transforming and exploiting such a rich additional data set is proposed to enhance the efficacy of current IDS approaches.