Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments

Zhu, Zhiqin; Zeng, Fancheng; Qi, Guanqiu; Li, Yuanyuan; Hou, Jie; Mazur, Neal

doi:10.1016/j.simpat.2021.102272

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Secondly, it will help grid companies conduct special analysis on equipment maintenance, operation and maintenance, improve system reliability, power supply qualification rate, reduce costs, and reduce power outages [ 217 ]. In the field of power grid maintenance, operation and maintenance, through the selection of key indicators of power equipment from the three aspects of safety, benefit, and cost, analysis of the mutual influence of “safety”, “benefit” and “cost” in maintenance management, coordination of the three these factors are comprehensively optimized, and at the same time, real-time online monitoring of the maintenance indicators of power grid enterprises is realized, providing guidance and services for the company’s maintenance strategy formulation [ 218 , 219 , 220 ].…”

Section: Future Trendmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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Section: Future Trendmentioning

confidence: 99%

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

AL-Jumaili

Muniyandi

Hasan

et al. 2023

Sensors

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“…For example, [55] mitigates DDoS Flooding in Software-Defined Networks; [69] introduces a multi-objective reward function to guide an RL agent to learn the most suitable action in mitigating application-layer DDoS attacks; [70] proposes a feature adaption RL approach based on the space-time flow regularities in IoV for DDoS mitigation. Among the works of RL for DDoS attack, there has been recent attention on large-scale solution [54,53] via cooperative RL, low-rate attacks in the edge environment [71], and sparse constraint in cloud computing [72]. Besides DDoS attacks, RL has a wide application in mobile edge caching [73], mobile offloading for cloud-based malware detection [46], and host-based and network-based intrusion detection [74,75].…”

Section: Posture-related Vulnerabilitymentioning

confidence: 99%

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Huang¹,

Huang²,

Zhu³

2021

Preprint

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The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important class of algorithms that epitomize the feedback architectures for cyber resiliency. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. We introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present works that develop several attack models, in which the attacks target the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. We discuss the potential defense methods to secure them and find that there is a lack of works focusing on the defensive mechanisms for RL-enabled systems. Finally, we discuss the future challenges of RL for cyber security and resiliency and emerging applications of RL-based CRMs.

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“…In comparison to linear controllers, nonlinear controllers are more complex structurally and need more installation work. A stability system refers to a power system's ability to sustain an operating equilibrium under ordinary operating conditions and to quickly return to an acceptable equilibrium after a disturbance [5], [6]. For the power system to remain stable, the dynamics of the system under disturbances must be studied.…”

Section: Introductionmentioning

confidence: 99%

Stability Simulation of 150 kV Malang Power Grid

Fujita

2022

FESPE

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This research describes the oscillation control based on a power system that takes the place of the traditional automatic voltage regulators for excitation control in addition to a multimachine power system. The design is built on equilibrium and a potent adaptive critic technique called decreasing time oscillation for each generator. The generators' local measurements serve as the sole basis for the feedback variables. For enhancing the dynamic performance and stability of the power grid under significant shocks that affected oscillation power stations, simulations on multi-machine power systems were shown to be effective.

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Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments

Cited by 13 publications

References 41 publications

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

Reinforcement Learning for Feedback-Enabled Cyber Resilience

Stability Simulation of 150 kV Malang Power Grid

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