Defense against cyber-attacks on the Hydro Power Plant connected in parallel with Energy System

Mladenov, Valeri; Chobanov, Veselin; Sarigiannidis, Panagiotis; Radoglou-Grammatikis, Panagiotis; Hristov, Anton; Zlatev, Pencho

doi:10.1109/bulef51036.2020.9326016

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…Moreover, secure communication protocols are critical for mitigating the risks associated with communication interference and ensuring the integrity and confidentiality of data transmitted over open waters [30]. Furthermore, incorporating machine learning and deep learning rigorously improves defense mechanisms' adaptability, allowing autonomous systems to evolve and respond dynamically to emerging cyber threats [31] .…”

Section: A Related Studiesmentioning

confidence: 99%

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Tabish,

Chaur-Luh

2024

IEEE Access

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Maritime Autonomous Surface Ships (MASS) have brought a transformative wave in the marine industry, yielding unprecedented operational advancements and efficiency gains. However, this paradigm shift has concurrently raised substantial cybersecurity concerns that demand meticulous attention to mitigate the impact of attacks on MASS stability. This study presents a comprehensive examination of the cybersecurity landscape, shedding light on prevalent issues concerning the data independence, integrity, and connectivity of MASS. The effectiveness of existing defense strategies against both inbound and outbound cyberattacks, focusing on attack detection and classification, is rigorously scrutinized. This analysis encompasses regulatory frameworks, encryption methods, intrusion detection systems, and a systematic approach to identifying and categorizing cyber threats. Additionally, the study anticipates future developments in cyber threats and proposes innovative solutions to fortify MASS defenses against potential cybersecurity vulnerabilities. To align with cybersecurity standards, this paper integrates challenges, possible solutions, and future advancements in a structured and cohesive manner, ensuring a thorough understanding of the intricate cybersecurity ecosystem surrounding Maritime Autonomous Surface Ships.

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Section: A Related Studiesmentioning

confidence: 99%

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Tabish,

Chaur-Luh

2024

IEEE Access

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“…A cyber-physical system (CPS) is defined as a system in which computing, information and communication technology functions, and physical processes and dynamics, interact in close coordination with each other to achieve a common goal [5][6][7]. Today s An energy shield and SDN microSENSE are two innovative technologies that are designed to improve energy efficiency and sustainability.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

Alzahrani

Aldhyani

2023

Sustainability

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Online food security and industrial environments and sustainability-related industries are highly confidential and in urgent need for network traffic analysis to attain proper security information to avoid attacks from anywhere in the world. The integration of cutting-edge technology such as the Internet of things (IoT) has resulted in a gradual increase in the number of vulnerabilities that may be exploited in supervisory control and data acquisition (SCADA) systems. In this research, we present a network intrusion detection system for SCADA networks that is based on deep learning. The goal of this system is to defend ICSs against network-based assaults that are both conventional and SCADA-specific. An empirical evaluation of a number of classification techniques including k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), convolution neural network (CNN), and integrated gated recurrent unit (GRU) is reported in this paper. The suggested algorithms were tested on a genuine industrial control system (SCADA), which was known as the WUSTL-IIoT-2018 and WUSTL-IIoT-20121 datasets. SCADA system operators are now able to augment proposed machine learning and deep learning models with site-specific network attack traces as a result of our invention of a re-training method to handle previously unforeseen instances of network attacks. The empirical results, using realistic SCADA traffic datasets, show that the proposed machine learning and deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerging threats. The accuracy performance attained by the KNN and RF algorithms was superior and achieved a near-perfect score of 99.99%, whereas the CNN-GRU model scored an accuracy of 99.98% using WUSTL-IIoT-2018. The Rf and GRU algorithms achieved >99.75% using the WUSTL-IIoT-20121 dataset. In addition, a statistical analysis method was developed in order to anticipate the error that exists between the target values and the prediction values. According to the findings of the statistical analysis, the KNN, RF, and CNN-GRU approaches were successful in achieving an R2 > 99%. This was demonstrated by the fact that the approach was able to handle previously unknown threats in the industrial control systems (ICSs) environment.

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A novel approach detection for IIoT attacks via artificial intelligence

Karacayılmaz,

Artuner

2024

Cluster Comput

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The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices.

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Defense against cyber-attacks on the Hydro Power Plant connected in parallel with Energy System

Cited by 11 publications

References 5 publications

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

A novel approach detection for IIoT attacks via artificial intelligence

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