Cyber Attacks Detection and Attribution in Iot-Based Cyber Physical Systems

Recent scenario says that there are many challenges for Internet of Things (IoT)-enabled cyber-physical systems (CPS) related to Industry 4.0 such as data protection and data security, lack of benefit quantification and prioritization by top management and so on. Thus, this paper presents a way to identify attack detection and attribution framework w h i c h i s designed for CPS, and more specifically in an industrial control system (ICS). It has a two-step ensemble attack detection and attribution framework. At the first step, a decision tree is used to differentiate the attacked and un-attacked data from the dataset. At the second step, using Deep Neural Network (DNN) models                  the accurate attack type in CPS is predicted.