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Cybersecurity 2021年第1期4卷 415-426页

作者： Gauthama Raman M.R. Chuadhry Mujeeb Ahmed Aditya Mathur iTrust Singapore University of Technology and Design(SUTD)8 Somapah RdSingapore 487372Singapore University of Strathclyde 16 Richmond StGlasgow G11XQUnited Kingdom

Gradual increase in the number of successful attacks against industrial control systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process *** this end,a class of anomaly detectors,created using data-centric approaches,are gaining *** machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an *** use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and *** the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale *** this work,we enumerate and discuss such *** presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.

关键词： industrial control systems ICS security Machine learning Intrusion detection Testbed and experimental Study

Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical industrial Infrastructures

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Computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab F.J.Alsolami Hani Choudhry Ibrahim Rizqallah Alzahrani Information systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

industrial control systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： industrial control systems internet of things artificial intelligence feature selection deep learning

Deriving invariant checkers for critical infrastructure using axiomatic design principles

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Cybersecurity 2021年第1期4卷 67-90页

作者： Cheah Huei Yoong Venkata Reddy Palleti Rajib Ranjan Maiti Arlindo Silva Christopher M Poskitt Singapore University of Technology and Design 8 Somapah Road487372 SingaporeSingapore Indian Institute of Petroleum and Energy 2nd FloorAU Engg College Main BlockAndhra University530003 VisakhapatnamIndia Birla Institute of Technology and Science PilaniHyderabad Campus Jawahar NagarKapra Mandal Medchal District500078 TelanganaIndia Singapore Management University 80 Stamford Road178902 SingaporeSingapore

Cyber-physical systems(CPSs)in critical infrastructure face serious threats of attack,motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants,*** properties over sensor and actuator states that should always be *** approaches for identifying invariants attempt to do so automatically,typically using data logs,but these can miss valid system properties if relevant behaviours are not well-represented in the ***,as the CPS is already built,resolving any design flaws or weak points identified through this process is *** this paper,we propose a systematic method for deriving invariants from an analysis of a CPS design,based on principles of the axiomatic design methodology from design *** method iteratively decomposes a high-level CPS design to identify sets of dependent design parameters(*** and actuators),allowing for invariants and invariant checkers to be derived in parallel to the implementation of the *** apply our method to the designs of two CPS testbeds,SWaT and WADI,deriving a suite of invariant checkers that are able to detect a variety of single-and multi-stage attacks without any false ***,we reflect on the strengths and weaknesses of our approach,how it can be complemented by other defence mechanisms,and how it could help engineers to identify and resolve weak points in a design before the controllers of a CPS are implemented.

关键词： Cyber-physical systems Critical infrastructure industrial control systems Systematic design framework Axiomatic design Invariants Anomaly detection Supervised machine learning

Cascading effects of cyber-attacks on interconnected critical infrastructure

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Cybersecurity 2021年第1期4卷 104-122页

作者： Venkata Reddy Palleti Sridhar Adepu Vishrut Kumar Mishra Aditya Mathur Indian Institute of Petroleum and Energy 2nd FloorAU Engg College Main BlockAndhra University530003 VisakhapatnamIndia Singapore University of Technology and Design 8 Somapah Road487372 SingaporeSingapore

Modern critical infrastructure,such as a water treatment plant,water distribution system,and power grid,are representative of Cyber Physical systems(CPSs)in which the physical processes are monitored and controlled in real *** source of complexity in such systems is due to the intra-system interactions and ***,these systems are a potential target for *** one or more of these infrastructure are attacked,the connected systems may also be affected due to potential cascading *** this paper,we report a study to investigate the cascading effects of cyber-attacks on two interdependent critical infrastructure namely,a Secure water treatment plant(SWaT)and a Water Distribution System(WADI).

关键词： industrial control systems Water treatment Water distribution Interconnected critical infrastructure Cyber-attacks Cascading effects