A Review of SUSAN: A Deep Learning based anomaly detection framework for sustainable industry [Póster]

Abstract

Nowadays, sustainability is pivotal in green tech nologies, crucial for industries striving to cut carbon emissions and optimize energy use. Alongside this concern, cyberattacks impacting sustainability in industries are on the rise. These attacks target industrial systems managing processes, often needing specialized knowledge and evading traditional cyber security measures. To tackle this, SUSAN, a Deep Learning based framework, is introduced, aimed at detecting cyberattacks on industrial sustainability. SUSAN’s modular design allows combining multiple detectors for precise detection. Demonstrated in a water treatment plant using the SWaT testbed, SUSAN achieved a high recall rate (0.910) and acceptable precision (0.633), resulting in an F1-score of 0.747. It successfully detected all individual cyberattacks, surpassing related work with a worst recall rate of 0.64.