Cyber-Physical Anomaly Detection for Power Grid with Machine Learning

Wang, Pengyuan; Manimaran, G.

doi:10.1007/978-3-030-18214-4_3

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…0: Average attention to policy access. 1: Priority attention to policy access [40] C8: Mobile connected medical devices Lack of updates or lack of patching, a common threat that can have a significant impact on the healthcare organization −1: Low Information Security maintenance 0: Average Information Security maintenance 1: High Information Security maintenance [5] C9: Supplier eligibility criteria Establish security baseline requirements and translate them into eligibility criteria when selecting suppliers The concepts allow complex and critical ecosystem threats to be exploited in a telehealth system. However, the lack or inefficiency of information security planning makes it challenging to identify cybersecurity.…”

Section: −1mentioning

confidence: 99%

Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

et al. 2021

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Hospital organizations have adopted telehealth systems to expand their services to a portion of the Brazilian population with limited access to healthcare, mainly due to the geographical distance between their communities and hospitals. The importance and usage of those services have recently increased due to the COVID-19 state-level mobility interventions. These services work with sensitive and confidential data that contain medical records, medication prescriptions, and results of diagnostic processes. Understanding how cybersecurity impacts the development of telehealth strategies is crucial for creating secure systems for daily operations. In the application reported in this article, the Fuzzy Cognitive Maps (FCMs) translated the complexity of cybersecurity in telehealth services into intelligible and objective results in an expert-based cognitive map. The tool also allowed the construction of scenarios simulating the possible implications caused by common factors that affect telehealth systems. FCMs provide a better understanding of cybersecurity strategies using expert knowledge and scenario analysis, enabling the maturation of cybersecurity in telehealth services.

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Section: −1mentioning

confidence: 99%

Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

et al. 2021

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“…But, as CPS becomes more complex and the attack is more stealthy, this method is more difficult to ensure the status of CPS (for example, protecting multi-variate physical measurement). It requires further domain knowledge (for example, correlation and more components) [10]. An anomaly detection system needs to adapt to capture novel features of CPS.…”

Section: Introductionmentioning

confidence: 99%

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Ramachandran¹,

Gayathri²,

Alkhayyat³

et al. 2023

Computer Systems Science and Engineering

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Cyber-physical system (CPS) is a concept that integrates every computer-driven system interacting closely with its physical environment. Internet-of-things (IoT) is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds. Since the complexity level of the CPS increases, an adversary attack becomes possible in several ways. Assuring security is a vital aspect of the CPS environment. Due to the massive surge in the data size, the design of anomaly detection techniques becomes a challenging issue, and domain-specific knowledge can be applied to resolve it. This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection (AOPTML-AD) technique in the CPS environment. The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment. The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format. Besides, the improved Aquila optimization algorithm-based feature selection (IAOA-FS) algorithm is designed to choose an optimal feature subset. Along with that, the chimp optimization algorithm (ChOA) with an adaptive neuro-fuzzy inference system (ANFIS) model can be employed to recognise anomalies in the CPS environment. The ChOA is applied for optimal adjusting of the membership function (MF) indulged in the ANFIS method. The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset. The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models, with an accuracy of 99.37%.

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A survey of anomaly detection methods for power grids

Madabhushi

Dewri

2023

Int. J. Inf. Secur.

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Cyber-Physical Anomaly Detection for Power Grid with Machine Learning

Cited by 7 publications

References 6 publications

Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

Fuzzy Cognitive Scenario Mapping for Causes of Cybersecurity in Telehealth Services

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

A survey of anomaly detection methods for power grids

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