Toward Detection and Attribution of Cyber-Attacks in IoTenabled Cyber-physical Systems

Abstract

Securing Internet-of-Things (IoT)-
enabled cyber-physical systems (CPS) can be
challenging, as security solutions developed for
general information/operational technology
(IT/OT) systems may not be as effective in a CPS
setting. Thus, this article presents a two-level
ensemble attack detection and attribution
framework designed for CPS, and more specifically
in an industrial control system (ICS). At the first
level, a decision tree combined with a novel
ensemble deep representation-learning model is
developed for detecting attacks imbalanced ICS
environments. At the second level, an ensemble
deep neural network is designed to facilitate attack
attribution. The proposed model is evaluated using
real-world data sets in gas pipeline and water
treatment system. Findings demonstrate that the
proposed model outperforms other competing
approaches with similar computational complexity.