Insider threats of Physical Protection Systems in nuclear power plants: Prevention and evaluation

Zou, Bowen; Yang, Ming; Guo, Jia; Wang, Junbo; Benjamin, Emi-Reynolds; Liu, Huan; Li, Wei

doi:10.1016/j.pnucene.2017.08.006

Cited by 23 publications

(15 citation statements)

References 2 publications

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“…The first addresses the physical protection of a system, whereas the second focuses on cyber protection. Another study by Zou et al [175] used the door and sensor data features to explore the use of the failure mode and effect analysis method. Mavroeidis et al [176] presented an ontological framework to improve physical security and insider threat detection using door access.…”

Section: Physical Behaviorsmentioning

confidence: 99%

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

et al. 2020

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Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

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Section: Physical Behaviorsmentioning

confidence: 99%

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

et al. 2020

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“…Other than that, [30] has proposed the method that is based on finding the possible failure threats and define the effect of each error on the Physical Protection System (PPS) effectiveness. Along with that, the features used was at the source of Estimate of Adversary Sequence Interruption (EASI) technique, the technique of Estimate and Prevention of the Insider Threats (EPIT) recommended for the best approximation of insider threats.…”

Section: 2insider Threats Cause By System Factormentioning

confidence: 99%

“…Other than that, author [41] reported one of the contributing factors of system failure when employee frequently gives respond to any phishing email. For example, from the review of research papers above, we can relate to the framework as an employee might have conceptualizing problem on how the production works which involve operational procedure [42] with the system and next resulting in the documentation or production failure [43] because employee give respond to the malicious email.…”

Section: Framework Of Automated Manufacturing Execution Systemmentioning

confidence: 99%

An Insider Threat Categorization Framework for Automated Manufacturing Execution System

Mohammad

Yassin

Ahmad

et al. 2019

IJIES

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Insider threats become one of the most dangerous threats in the cyber world as compared to outsider as the insiders have knowledge of assets. In addition, the threats itself considered in-visible and no one can predict what, when and how exactly the threat launched. Based on conducting literature, threat in Automated Manufacturing Execution Systems (AMESs) can be divided into three principle factors. Moreover, there is no standard framework to be referring which exist nowadays to categorize such factors in order to identify insider threats possible features. Therefore, from the conducted literature a standard theoretical categorization of insider threats framework for AMESs has been proposed. Hence, three principle factors, i.e. Human, Systems and Machine have considered as major categorization of insider threats. Consequently, the possible features for each factor identified based on previous researcher recommendations. Therefore, via identifying possible features and categorize it into principle factors or groups, a standard framework could be derived. These frameworks will contribute more benefit specifically in the manufacturing field as a reference to mitigate an insider threat. Keywords—automated manufacturing execution systems insider threats, factors and features, insider threat categorization framework.

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“…To keep the system in a reliable condition and to ensure that there is continuous power without any interruption to industries, telecommunication networks, and at the consumer level, there is a need for a fast, stable, and reliable system that can handle such a problem in a quick manner without any delay. In a power system, the protection system is one of the counteragents that detects and clears a fault as soon as possible [4,5].…”

Section: Introductionmentioning

confidence: 99%

Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single‐ and Multi‐Loop Distribution Systems

et al. 2019

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To ensure a safe and trustworthy pattern in contradiction to the possible faults, a precise, reliable, and fast relaying strategy is of high importance in an electrical power system. These challenges give the impression of being more refined in multi-loop distribution systems. More recently, overcurrent relays (OCRs) have evolved as proficient counteragents for such cases. In this way, inaugurating an optimal protection coordination strategy is accepted as the primary precondition in guaranteeing the safe protection of the coordination strategy. This study is aimed at lessening the overall operational time of the main relays in order to reduce the power outages. The coordination problem is conducted by adjusting only one parameter, namely the time multiplier setting (TMS). In electrical power relaying coordination, the objective function to be minimized is the sum of the overall operational time of the main relays. In the prescribed work, the coordination of the OCRs in the single- and multi-loop distribution network is realized as an optimization issue. The optimization is accomplished by means of JAYA algorithm. The suggested technique depends on the idea that the result acquired for a certain issue ought to pass near the finest result and avert the worst result. This technique involves only the common control factors and does not involve specific control factors. JAYA is adopted to OCR problem and run 20 times with the same initial condition for each case study, and it has been realized that for every run, the JAYA algorithm converges to the global optimum values requiring less number of iterations and computational time. The results obtained from JAYA algorithm are compared with other evolutionary and up-to-date algorithms, and it was determined that JAYA outperforms the other techniques.

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Insider threats of Physical Protection Systems in nuclear power plants: Prevention and evaluation

Cited by 23 publications

References 2 publications

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

An Insider Threat Categorization Framework for Automated Manufacturing Execution System

Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single‐ and Multi‐Loop Distribution Systems

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