Preliminaries of orthogonal layered defence using functional and assurance controls in industrial control systems

Industrial Control Systems (ICSs) are responsible for the automation of different processes and the overall control of systems that include highly sensitive potential targets such as nuclear facilities, energy-distribution, water-supply, and mass-transit systems. Given the increased complexity and rapid evolvement of their threat landscape, and the fact that these systems form part of the Critical National infrastructure (CNI), makes them an emerging domain of conflict, terrorist attacks, and a playground for cyberexploitation. Existing layered-defence approaches are increasingly criticised for their inability to adequately protect against resourceful and persistent adversaries. It is therefore essential that emerging techniques, such as orthogonality, be combined with existing security strategies to leverage defence advantages against adaptive and often asymmetrical attack vectors. The concept of orthogonality is relatively new and unexplored in an ICS environment and consists of having assurance control as well as functional control at each layer. Our work seeks to partially articulate a framework where multiple functional and assurance controls are introduced at each layer of ICS architectural design to further enhance security while maintaining critical real-time transfer of command and control traffic

Anomaly Detection Based on Zone Partition for Security Protection of Industrial Cyber-Physical Systems

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A developing trend of traditional industrial systems is the integration of the cyber and physical domain to improve flexibility and the efficiency of supervision, management and control. But, the deep integration of these Industrial Cyber-Physical Systems (ICPSs), increases the potential for security threats. Attack detection, which forms initial protective barrier, plays an important role in overall security protection. However, most traditional methods focused on cyber information and ignored any limitations that might arise from the characteristics of the physical domain. In this paper, an anomaly detection approach based on zone partition is designed for ICPSs. In detail, initially an automated zone partition method ensuring crucial system states can be observed in more than one zone is designed. Then, methods of building zone function model which do not require any prior knowledge of the physical system are presented before analyzing the anomaly based on zone information. Finally, an experimental rig is constructed to verify the effectiveness of the proposed approach. The results demonstrate that the approach presents a high accuracy solution which also performs effectively in realtime

Industrial control systems cybersecurity analysis and countermeasures

Industrial Control Systems (ICS) are frequently used in the manufacturing industry and critical infrastructures, such as water, oil and transportation. Disruption of these industries could have disastrous consequences, leading to financial loss or even human lives. Over time, technological development has improved ICS components; however, little research has been done to improve its security posture. In this research, a novel attack vector addressed to the Input and Output memory of the latest SIMATIC S7-1500 PLC is presented. The results obtained during the experimentation process show that attacks on the PLC memory can have a significantly detrimental effect on the operations of the control system. Furthermore, this research describes implements and evaluates the physical, hybrid and virtual model of a Clean Water Supply System developed for the cybersecurity analysis of the Industrial Control System. The physical testbed is implemented on the Festo MPA platform, while the virtual representation of this platform is implemented in MATLAB. The results obtained during the evaluation of the three testbeds show the strengths and weaknesses of each implementation. Likewise, this research proposes two approaches for Industrial Control Systems cyber-security analysis. The first approach involves an attack detection and mitigation mechanism that focuses on the input memory of PLC and is implemented as part of its code. The response mechanism involves three different techniques: optimized data blocks, switching between control strategies, and obtaining sensor readings directly from the analogue channel. The Clean Water Supply System described above is employed for the practical evaluation of this approach. The second approach corresponds to a supervised energy-based system to anomaly detection using a novel energy-based dataset. The results obtained during the experimentation process show that machine learning algorithms can classify the variations of energy produced by the execution of cyber-attacks as anomalous. The results show the feasibility of the approach presented in this research by achieving an F1-Score of 95.5%, and 6.8% FNR with the Multilayer Perceptron Classifier