ABATe: Automatic Behavioral Abstraction Technique to Detect Anomalies in Smart Cyber-Physical Systems
| Autor: | Sandeep Narayanan, Ranjan Bose, Anupam Joshi |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
Computer science Event (computing) media_common.quotation_subject Physical system computer.software_genre Semantics Adaptability Domain (software engineering) Data mining False positive rate Electrical and Electronic Engineering computer Abstraction (linguistics) media_common |
| Zdroj: | IEEE Transactions on Dependable and Secure Computing. 19:1673-1686 |
| ISSN: | 2160-9209 1545-5971 |
| Popis: | Detecting anomalies and attacks in smart cyber-physical systems are of paramount importance owing to their growing prominence in controlling critical systems. However, this is a challenging task due to the heterogeneity and variety of components of a CPS, and the complex relationships between sensed values and potential attacks or anomalies. Such complex relationships are results of physical constraints and domain norms which exist in many CPS domains. In this paper, we propose ABATe, an Automatic Behavioral Abstraction Technique based on Neural Networks for detecting anomalies in smart cyber-physical systems. Unlike traditional techniques which abstract the statistical properties of different sensor values, ABATe learns complex relationships between event vectors from normal operational data available in abundance with smart CPS and uses this abstracted model to detect anomalies. ABATe detected more than 88% of attacks in the publicly available SWaT dataset featuring data from a scaled down Sewage Water Treatment plant with a very low false positive rate of 1%. We also evaluated our technique's ability to capture domain semantics and multi-domain adaptability using a real-world automotive dataset, as well as a synthetic dataset. |
| Databáze: | OpenAIRE |
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