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Journal articles on the topic 'Critical infrastructure Machine Learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Ashwini Kumar Verma. "Malware Detection in Critical Infrastructure using Machine Learning." Power System Technology 49, no. 1 (2025): 145–63. https://doi.org/10.52783/pst.1420.

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Operational Technology (OT) play a crucial role in maintaining the safety and efficient operation of critical infrastructure across various industries. OT are vulnerable to various cyberthreats, including malware, which can have serious consequences for critical infrastructure. Malware refers to any type of malicious software that is intended harm and cause trouble. This may includes Virus, Worms, Trojan, Trojan-Downloader, Trojan-Dropper, and worms. In this paper portable executables of malware, usually found in the OT, which is front line of critical infrastructure, utilized by the parsing t

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Moradpoor, Naghmeh, Ezra Abah, Andres Robles-Durazno, and Leandros Maglaras. "Adversarial Attacks on Supervised Energy-Based Anomaly Detection in Clean Water Systems." Electronics 14, no. 3 (2025): 639. https://doi.org/10.3390/electronics14030639.

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Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such systems appear to be increasing in both frequency and severity. Various machine learning approaches have been employed for anomaly detection in Critical National Infrastructure, given their success in identifying both known and unknown attacks with high accuracy.

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Kumar, Avinash, and Jairo A. Gutierrez. "Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure." Information 16, no. 7 (2025): 515. https://doi.org/10.3390/info16070515.

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In the realm of critical infrastructure protection, robust intrusion detection systems (IDSs) are essential for securing essential services. This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. The study evaluates supervised learning algorithms alongside unsupervised learning algorithms. The analysis reveals that supervised learning algorithms exhi

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Kalaskar, Chetankumar, and S. Thangam. "Fault Tolerance of Cloud Infrastructure with Machine Learning." Cybernetics and Information Technologies 23, no. 4 (2023): 26–50. http://dx.doi.org/10.2478/cait-2023-0034.

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Abstract Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed

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Siddiqui, Muhammad Rawish. "Harnessing Big Data for Machine Learning (Strategies, Approaches, and Challenges)." Journal of Electrical Electronics Engineering 3, no. 6 (2024): 01–02. http://dx.doi.org/10.33140/jeee.03.06.01.

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This paper explores the dynamic relationship between big data and machine learning, highlighting the key strategies, methodologies, and challenges associated with their integration. The convergence of these technologies presents transformative opportunities across industries, but it also introduces complexities in terms of data management, infrastructure, and real-time processing. This study examines the role of big data in fueling machine learning models, discusses critical success factors, and identifies best practices for implementing machine learning at scale.

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Padmanaban, Harish. "Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 1–26. http://dx.doi.org/10.60087/jaigs.vol03.issue01.p26.

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Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.

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Padmanaban, Harish. "Machine Learning Algorithms Scaling on Large-Scale Data Infrastructure." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 171–96. http://dx.doi.org/10.60087/jaigs.v3i1.113.

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Scalability is a critical aspect of deploying machine learning (ML) algorithms on large-scale data infrastructure. As datasets grow in size and complexity, organizations face challenges in efficiently processing and analyzing data to derive meaningful insights. This paper explores the strategies and techniques employed to scale ML algorithms effectively on extensive data infrastructure. From optimizing computational resources to implementing parallel processing frameworks, various approaches are examined to ensure the seamless integration of ML models with large-scale data systems.

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Rafeeq War, Muhammed, Yashwant Singh, Zakir Ahmad Sheikh, and Pradeep Kumar Singh. "Review on the Use of Federated Learning Models for the Security of Cyber-Physical Systems." Scalable Computing: Practice and Experience 26, no. 1 (2025): 16–33. https://doi.org/10.12694/scpe.v26i1.3438.

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The field of critical infrastructure has undergone significant expansion over the past three decades, spurred by global economic liberalization and the pursuit of development, industrialization, and privatization by nations worldwide. This rapid growth has led to a proliferation of critical infrastructure across various sectors, necessitating decentralization efforts to manage the associated burdens effectively. With the advent of artificial intelligence and machine learning, computer scientists have sought innovative approaches to detect and respond to the evolving landscape of cyber threats.

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Kalnawat, Aarti, Dharmesh Dhabliya, Kasichainula Vydehi, Anishkumar Dhablia, and Santosh D. Kumar. "Safeguarding Critical Infrastructures: Machine Learning in Cybersecurity." E3S Web of Conferences 491 (2024): 02025. http://dx.doi.org/10.1051/e3sconf/202449102025.

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It has become essential to protect vital infrastructures from cyber threats in an age where technology permeates every aspect of our lives. This article examines how machine learning and cybersecurity interact, providing a thorough overview of how this dynamic synergy might strengthen the defence of critical systems and services. The hazards to public safety and national security from cyberattacks on vital infrastructures including electricity grids, transportation networks, and healthcare systems are significant. Traditional security methods have failed to keep up with the increasingly sophis

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Abbagalla, Sindhooja, and Srividhya Gavini. "SMART SECURITY SOLUTIONS FOR CLOUD INFRASTRUCTURE USING MACHINE LEARNING." International Journal of Interpreting Enigma Engineers 01, no. 02 (2024): 08–14. http://dx.doi.org/10.62674/ijiee.2024.v1i02.002.

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Cloud computing has become a ubiquitous storage, processing, and data management tool. However, providing strong security measures inside cloud infrastructure remains a primary priority.The purpose of this study is to give an overview of the process of integrating cloud infrastructure with machine learning. The main objective of this work is to leverage machine learning approaches and models for threat identification, anomaly detection, and access control methods in order to protect sensitive data and reduce growing risks in the cloud infrastructure. Ultimately, this research endeavours to enh

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Okusi, Oluwatobiloba. "Leveraging AI and Machine Learning for the Protection of Critical National Infrastructure." Asian Journal of Research in Computer Science 17, no. 10 (2024): 1–11. http://dx.doi.org/10.9734/ajrcos/2024/v17i10505.

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No nation can exist or survive without critical infrastructure (CI), which is why a nation’s growth, development, welling, standard of living, possessions, and even governance are weighed by the kind of CI obtained therein. There are growing concerns about the need and how to protect CI from cyber threats in the 21st century era of digitalization. This descriptive survey research aims at showing how artificial intelligence (AI) and machine learning (ML) can be leveraged for the protection of critical national infrastructure (CNI). The study relies on secondary data, which are subjected to them

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Kumar, Ajit, Neetesh Saxena, Souhwan Jung, and Bong Jun Choi. "Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling." Energies 15, no. 1 (2021): 212. http://dx.doi.org/10.3390/en15010212.

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Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastruct

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Soni, Neha. "Impact of AI and Machine Learning on Supply Chain Optimization in Developing Economies." Journal of Technology Management for Growing Economies 14, no. 2 (2023): 1–7. https://doi.org/10.15415/jtmge/2023.142001.

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Background: Emerging economies face various unsolved issues that limit supply chain development, such as inefficiency and unhealthy competition, lack of transparency, and an underdeveloped technological infrastructure. Purpose: This paper describes how Artificial Intelligence and Machine Learning can solve these problems with the help of supply chain management in various areas. In developing economies, Artificial Intelligence and Machine Learning are transforming supply chain management, offering exceptional opportunities for optimization. In this paper, the innovative potential of AI and Ml

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Mohammed, Abdul Muqtadir. "Scalable AI: Leveraging Cloud Infrastructure for Large-Scale Machine Learning." International Journal of Advances in Engineering and Management 7, no. 2 (2025): 382–90. https://doi.org/10.35629/5252-0702382390.

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This comprehensive article explores the evolution and implementation of cloud infrastructure for large-scale machine learning systems. The article examines critical aspects of distributed training architectures, resource optimization strategies, and performance enhancement techniques in cloud environments. It addresses the challenges of scaling artificial intelligence workloads while maintaining efficiency and cost-effectiveness. The article analyzes various approaches to infrastructure design, including parameter server and ringallreduce architectures, along with methods for optimizing data p

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O’Donnell, William, David Mahon, Guangliang Yang, and Simon Gardner. "Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications." Particles 8, no. 1 (2025): 33. https://doi.org/10.3390/particles8010033.

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The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a const

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Pasupuleti, Murali Krishna. "ecuring Critical Infrastructure with World Models that Learn from Real-World Sensor and Activity Logs." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 234–42. https://doi.org/10.62311/nesx/rp1825.

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Abstract: Critical infrastructure systems—such as power grids, water treatment facilities, and transportation networks—are increasingly reliant on interconnected digital technologies. This interdependence exposes them to a myriad of cyber and physical threats. Traditional security measures often fall short in addressing the dynamic and complex nature of these threats. This paper explores the development and application of AI-driven world models that learn from real-world sensor and activity logs to enhance the security of critical infrastructure. By integrating data from various sources and em

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Shan, Ali, and Seunghwan Myeong. "Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application." Sensors 24, no. 15 (2024): 4888. http://dx.doi.org/10.3390/s24154888.

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Cyber-security challenges are growing globally and are specifically targeting critical infrastructure. Conventional countermeasure practices are insufficient to provide proactive threat hunting. In this study, random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), AdaBoost, and hybrid models were applied for proactive threat hunting. By automating detection, the hybrid machine learning-based method improves threat hunting and frees up time to concentrate on high-risk warnings. These models are implemented on approach devices, access, and principal servers. The efficacy

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Tapash, Paul, and Saha Jay. "Predictive Maintenance of Railway Point Machine Using Machine Learning Algorithm." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 1–3. https://doi.org/10.5281/zenodo.7811159.

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Indian Railways is one of the largest railway networks in the world, and the efficient functioning of its infrastructure is essential for safe and timely transportation. The point machine is one of the most critical components of the railway system, which controls the movement of trains at junctions. Regular maintenance of the point machine is necessary to ensure its proper functioning and prevent failures that can lead to accidents and delays. Predictive maintenance is a proactive approach that can significantly improve the reliability and availability of the point machine. This research pape

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Bhanuprakash, Madupati. "Machine Learning for Cybersecurity in Industrial Control Systems (ICS)." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 1 (2020): 1–14. https://doi.org/10.5281/zenodo.14208812.

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ICS (Industrial Control Systems) are the backbone of power, water, and manufacturing, amongst other critical infrastructure sectors. Like everything else, traditional ICS is evolving with the modern Information and Communication Technologies (ICT) getting integrated into its stack, exposing itself to potential cyber-attacks. Because of the real-time operational requirements and legacy technology, traditional security methods are frequently ineffective in protecting ICS. Machine Learning (ML) techniques are the major solution to improve Intrusion Detection Systems (IDS) or Intrusion Prevention

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Harish, Keerthi B., W. Nicholson Price, and Yindalon Aphinyanaphongs. "Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges." JMIR Formative Research 6, no. 4 (2022): e33970. http://dx.doi.org/10.2196/33970.

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Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon

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I. Selim, Gamal Eldin, EZZ El-Din Hemdan, Ahmed M. Shehata, and Nawal A. El-Fishawy. "Anomaly Activities Detection System in Critical Water SCADA Infrastructure Using Machine Learning Techniques." Menoufia Journal of Electronic Engineering Research 28, no. 1 (2019): 343–84. http://dx.doi.org/10.21608/mjeer.2019.69027.

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D, JYOTHI, M.A.H FARQUAD Dr., and G. NARSIMHA Dr. "A NETWORK SECURITY FRAMEWORK FOR HYBRID BOTNET DETECTION IN CRITICAL INFRASTRUCTURE BY USING MACHINE LEARNING ALGORITHMS." Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology 56, no. 01 (2023): 251–67. https://doi.org/10.5281/zenodo.8285603.

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<strong>Abstract</strong> Botnet attacks can carry out a variety of criminal activities besides aim of causing harm and collecting data from vulnerable machines, they have always been a severe issue for Critical Infrastructure and business organizations. In this research, we used Software Defined Networks, which is capable of recognizing botnet behavior by utilizing a machine learning approach and detection of related botnet attacks. We have detected the botnets by creating a monitoring frame work in the SDN environment to identify Botnet in the network flow.

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RUPADEVI, RUPADEVI. "Electric Vehicle Energy Demand Prediction: A Critical and Systematic Overview." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03035.

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Abstract: Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In o

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Chevuri, Rajeev Reddy. "Demystifying MLOps: Core Principles for Scalable Machine Learning." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 884–90. https://doi.org/10.35629/5252-0703884890.

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This article examines Machine Learning Operations (MLOps) as a critical discipline bridging the gap between experimental model development and production-ready AI systems. By integrating principles from DevOps, data engineering, and machine learning, MLOps creates structured frameworks that streamline the entire machine learning lifecycle. The content explores core infrastructure components including cloud computing, containerization with Docker, and orchestration through Kubernetes that form the foundation for scalable AI solutions. It details how continuous integration and deployment pipelin

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Aragonés Lozano, Mario, Israel Pérez Llopis, and Manuel Esteve Domingo. "Threat Hunting System for Protecting Critical Infrastructures Using a Machine Learning Approach." Mathematics 11, no. 16 (2023): 3448. http://dx.doi.org/10.3390/math11163448.

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Cyberattacks are increasing in number and diversity in nature daily, and the tendency for them is to escalate dramatically in the forseeable future, with critical infrastructures (CI) assets and networks not being an exception to this trend. As time goes by, cyberattacks are more complex than before and unknown until they spawn, being very difficult to detect and remediate. To be reactive against those cyberattacks, usually defined as zero-day attacks, cyber-security specialists known as threat hunters must be in organizations’ security departments. All the data generated by the organization’s

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Armaan Siddiqui. "Property Price Prediction Using Machine Learning." Journal of Information Systems Engineering and Management 10, no. 41s (2025): 625–32. https://doi.org/10.52783/jisem.v10i41s.7980.

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To make smart investment choices in the rapidly evolving property sector market of today, it's critical to have reliable tools for estimating property prices This study aims to come up with a comprehensive machine learning-based system for forecasting property prices. The proposed solution incorporates historical property data and takes into consideration the future development plans to enhance prediction accuracy. Using crucial factors that effects the property prices such as location, square footage, count of bedrooms, and bathrooms, this study presents a machine learning model that assists

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Bobowska, Barbara, Michał Choraś, and Michał Woźniak. "Advanced Analysis of Data Streams for Critical Infrastructures Protection and Cybersecurity." JUCS - Journal of Universal Computer Science 24, no. (5) (2018): 622–33. https://doi.org/10.3217/jucs-024-05-0622.

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Cyber threats are nowadays a major danger to critical infrastructures and to homeland security. For several years now, the focus have been targeted at the physical protection of critical infrastructures. Currently, experts realize that the critical infrastructure can be also attacked via the application layer of computer networks. In order to efficiently protect such critical systems, the huge amount of data has to be efficiently analyzed and correlated. Therefore, this paper focuses on the overview of the advanced data stream processing methods to be applied in the domain of cybersecurity and

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Kelli, Vasiliki, Vasileios Argyriou, Thomas Lagkas, George Fragulis, Elisavet Grigoriou, and Panagiotis Sarigiannidis. "IDS for Industrial Applications: A Federated Learning Approach with Active Personalization." Sensors 21, no. 20 (2021): 6743. http://dx.doi.org/10.3390/s21206743.

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Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closel

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Swapnil Chawande. "AI-driven threat modeling for critical infrastructure." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 1142–55. https://doi.org/10.30574/wjaets.2024.13.1.0476.

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The research investigates how Artificial Intelligence (AI) enhances the security of vital national and global infrastructure through threat modeling systems evaluation. The main research goal is to evaluate how well AI-based systems detect infrastructure weaknesses while reducing security threats affecting power grids, transportation, and healthcare services and facilities. The research depends on case study approaches combined with an assessment of AI implementations through real-world scenarios, machine learning algorithms, and anomaly detection methods. The analysis reveals AI succeeds in a

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Chittibala, Dinesh Reddy, and Srujan Reddy Jabbireddy. "Security in Machine Learning (ML) Workflows." International Journal of Computing and Engineering 5, no. 1 (2024): 52–63. http://dx.doi.org/10.47941/ijce.1714.

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Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as these challenges encompass data privacy breaches, unauthorized access, model theft, adversarial attacks, and vulnerabilities within the computational infrastructure. Methodology: To counteract these threats, we propose a holistic suite of str

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Imteaj, Ahmed, Irfan Khan, Javad Khazaei, and Mohammad Hadi Amini. "FedResilience: A Federated Learning Application to Improve Resilience of Resource-Constrained Critical Infrastructures." Electronics 10, no. 16 (2021): 1917. http://dx.doi.org/10.3390/electronics10161917.

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Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-suffic

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Verma, Saurabh, Pankaj Pali, Pavani Kori, and Muskan Tiwari. "Advanced Threat Detection Systems for Protecting Critical Infrastructure." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 06 (2023): 8896–901. http://dx.doi.org/10.15680/ijircce.2023.1106077.

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As critical infrastructure increasingly relies on digital technologies and interconnected systems, the security landscape becomes more complex, necessitating advanced threat detection systems to protect these essential assets. This paper reviews contemporary approaches and technologies for threat detection in critical infrastructure, including machine learning algorithms, anomaly detection techniques, and hybrid security models. It investigates the effectiveness of these systems in real-time monitoring, integration of advanced analytics, and the use of predictive models to anticipate and mitig

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Hallowe Andrew. "Botnets and Critical Infrastructure Security: A Survey." GSC Advanced Research and Reviews 22, no. 1 (2025): 330–61. https://doi.org/10.30574/gscarr.2025.22.1.0445.

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Botnets have emerged as a significant threat to the security and resilience of critical infrastructure systems. These decentralized networks of compromised devices enable malicious actors to execute sophisticated cyberattacks, such as Distributed Denial of Service (DDoS) attacks, data exfiltration, and ransomware deployment, which can disrupt essential services and compromise national security. This paper examines the evolving landscape of botnet threats to critical infrastructure, highlighting the vulnerabilities inherent in increasingly interconnected systems, including industrial control sy

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Almaleh, Abdulaziz, and David Tipper. "Risk-Based Criticality Assessment for Smart Critical Infrastructures." Infrastructures 7, no. 1 (2021): 3. http://dx.doi.org/10.3390/infrastructures7010003.

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Today, critical infrastructure is more interconnected, which allows more vulnerabilities in the case of disasters. In addition, the effect of one infrastructure can lead to one or more cascading failures in another infrastructure due to the dependency complexity between them. This article introduces a holistic approach using network indicators and machine learning to better understand the geographical representation of critical infrastructure. Previous work on a similar model was based on a single measure; such as in fashion, this paper introduces four measures utilized to identify the most vi

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Noetzold, Darlan, Anubis G. D. M. Rossetto, Valderi R. Q. Leithardt, and Humberto J. de M. Costa. "Enhancing Infrastructure Observability: Machine Learning for Proactive Monitoring and Anomaly Detection." Journal of Internet Services and Applications 15, no. 1 (2024): 508–22. https://doi.org/10.5753/jisa.2024.4509.

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This study addresses the critical challenge of proactive anomaly detection and efficient resource management in infrastructure observability. Introducing an innovative approach to infrastructure monitoring, this work integrates machine learning models into observability platforms to enhance real-time monitoring precision. Employing a microservices architecture, the proposed system facilitates swift and proactive anomaly detection, addressing the limitations of traditional monitoring methods that often fail to predict potential issues before they escalate. The core of this system lies in its pr

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Panagiotis, Fountas, Kouskouras Taxiarxchis, Kranas Georgios, Leandros Maglaras, and Mohamed Amine Ferrag. "Intrusion Detection in Critical Infrastructures: A Literature Review." Smart Cities 4, no. 3 (2021): 1146–57. http://dx.doi.org/10.3390/smartcities4030061.

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Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.

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Samudra, Shreyas, Mohamed Barbosh, and Ayan Sadhu. "Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring." Sensors 23, no. 7 (2023): 3365. http://dx.doi.org/10.3390/s23073365.

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The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks. Structural health monitoring (SHM) of these infrastructures is essential to reduce life-cycle costs, and determine their remaining life using advanced sensing techniques and data fusion methods. However, the data obtained from the SHM systems describing the health condition of the infrastructure systems may contain anom

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Raman, Gauthama, and Aditya Mathur. "Fusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants." Electronics 13, no. 12 (2024): 2267. http://dx.doi.org/10.3390/electronics13122267.

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Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on data-driven models that utilize machine learning to interpret complex data patterns. Challenges in creating these detectors arise from factors such as dynamic operating conditions, lack of design knowledge, and the complex interdependencies among heterogeneous compon

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Ankita Sappa. "Adaptive Machine Learning Algorithms for Anomaly Detection in Enterprise IT Infrastructure." Research Briefs on Information and Communication Technology Evolution 9 (December 30, 2023): 206–27. https://doi.org/10.69978/rebicte.v9i.201.

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The increasing scale and complexity of enterprise IT infrastructures renders traditional rule-based and static anomaly detection systems that are automated as well as manual, incapable of dealing with evolving threats, system dynamics, and concept drift. This paper proposes an adaptable Machine Learning (ML) architecture which can autonomously detect real-time anomalies within critical IT environments, including network, application, and host systems. By employing ensemble learning, streaming models, and drift-aware system architectures, the system detection performance degradation with regard

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Mmaduekwe, Ebuka. "AI-Driven Cyber Threat Detection for Securing National Critical Infrastructure." Asian Journal of Research in Computer Science 18, no. 6 (2025): 424–31. https://doi.org/10.9734/ajrcos/2025/v18i6711.

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This research explores the application of Artificial Intelligence (AI) in enhancing cyber threat detection mechanisms aimed at protecting national infrastructure. The purpose of the study is to evaluate how AI-driven approaches, particularly machine learning and deep learning techniques, can improve the speed, accuracy, and adaptability of cybersecurity systems in the face of increasingly sophisticated and persistent threats targeting critical sectors such as energy, transportation, water, and communications. The methodology involves a comparative analysis of traditional signature-based detect

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Nikitin, Petr Vladimirovich, and Rimma Ivanovna Gorokhova. "Analysis of modern intelligent methods for protecting critical information infrastructure." Вопросы безопасности, no. 3 (March 2024): 14–38. http://dx.doi.org/10.25136/2409-7543.2024.3.69980.

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Critical information infrastructure (CII), including the financial sector, plays a key role in ensuring the sustainable functioning of economic systems and the financial stability of States. However, the growing digitalization of the financial industry and the introduction of innovative technologies are opening up new attack vectors for attackers. Modern cyber attacks are becoming more sophisticated, and traditional defenses are proving ineffective against new, previously unknown threats. There is an urgent need for more flexible and intelligent cybersecurity systems. Thus, the subject of the

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BHAVANA, N. "Flood and Landslide Prediction using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03903.

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ABSTRACT Floods and landslides are among the most destructive natural disasters, causing significant loss of life, infrastructure damage, and economic disruption. Timely prediction of these events is critical for minimizing their impact and enhancing disaster preparedness. This study presents a machine learning-based approach for predicting floods and landslides by analyzing historical data, weather patterns, and environmental factors. The proposed system leverages various machine learning algorithms, including decision trees, support vector machines, and random forests, to process and classif

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Ramya Boorugula. "Demystifying Data Pipelines: A Beginner's Guide to ML Data Infrastructure." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 470–75. https://doi.org/10.32996/jcsts.2025.7.3.53.

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Data pipelines constitute the foundation of machine learning systems, serving as the critical infrastructure that transforms raw data into valuable insights. This article demystifies the complex world of ML data pipelines for newcomers, breaking down essential components and considerations through accessible concepts and practical guidance. The article begins with fundamental pipeline architecture, examining the journey data takes from collection through transformation to model delivery. Key distinctions between ML pipelines and traditional data workflows illuminate the unique requirements of

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Abubakar, Mansir, Alwatben Batoul Rashed, Mohamad Yusof Darus, and Armayau Z. Umar. "INTELLIGENT MODELS FOR INTRUSION DETECTION OVER CLOUD INFRASTRUCTURE: A LITERATURE REVIEW." Journal of Information System and Technology Management 10, no. 38 (2025): 162–80. https://doi.org/10.35631/jistm.1038011.

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Cloud Computing has revolutionized the information technology (IT) landscape, enabling scalable and on-demand access to resources. However, its reliance on shared infrastructure introduces vulnerabilities, necessitating advanced security measures. Traditional intrusion detection systems (IDSs) struggle to cope with the complexity and scale of cloud environments. Machine Learning (ML) has emerged as a promising approach, offering automation, adaptability, and enhanced detection capabilities, thus, ensuring intelligence in intrusion detection systems. With the increasing reliance on cloud infras

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Lysov, Bogdan, Vira Huskova, Tetiana Prosiankina-Zharova, and Artem Khalygov. "Information technology for the classification of data monitoring systems and data transmission networks for cyber threat detection." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling" 1, no. 1 (13) (2025): 129–47. https://doi.org/10.20998/2411-0558.2025.01.09.

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Nowadays, the security of critical infrastructure (CI) is a key challenge in the modern digital environment. The growing complexity of information systems and cyberattack methods necessitates the development of effective approaches for threat detection and classification. This paper presents a methodology for classifying types of threats for critical infrastructure based on network data. It proposes the use of machine learning and anomaly detection methods to identify malicious activities. Modern threat classification algorithms and neural networks are analyzed. The obtained results demonstrat

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Bandukda, Zoya, Muhammad Ahmed Abid, Muhammad Talha Akhtar, Muhammad Nawaz, and Tahir Mehmood. "Pakistan’s Cyber Defense Revolution: AI & Machine Learning for Threat Mitigation." Advance Social Science Archive Journal 4, no. 1 (2025): 38–47. https://doi.org/10.55966/assaj.2025.4.1.041.

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Pakistan faces escalating cybersecurity threats, including ransomware, phishing, and state-sponsored attacks, which jeopardize businesses, government institutions, and critical infrastructure. Traditional defense mechanisms are increasingly inadequate, necessitating advanced solutions like Artificial Intelligence (AI) and Machine Learning (ML) to revolutionize cyber defense. This article explores how AI and ML enhance threat mitigation through real-time anomaly detection, behavioral analysis, and automated response systems, with specific applications in Pakistan’s financial sector, government

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Dr., Suman Jain. "Optimization Techniques in Machine Learning and Artificial Intelligence." International Journal on Science and Technology 3, no. 4 (2012): 1–10. https://doi.org/10.5281/zenodo.15363878.

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This paper explores the application of optimization techniques in machine learning and artificial intelligence (AI) within the Indian context, highlighting their significance, challenges, and future research directions. Optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) play a crucial role in improving the accuracy and efficiency of machine learning models, especially in sectors like healthcare, agriculture, and energy. The paper provides a detailed review of the methodologies, including the integration of hybrid models, parallel computing, and domain

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B. Rupadevi and Sambaiahpalem Adikesavulu. "Electric Vehicle Energy Demand Prediction Techniques: A Critical and Systematic Review." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 98–101. https://doi.org/10.47001/irjiet/2025.iccis-202515.

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Abstract - Accurately predicting energy demand is crucial for managing charging infrastructure, maximising vehicle performance, and guaranteeing effective energy distribution as EV adoption picks up speed. This study offers a thorough and organised analysis of EV energy demand prediction methods, covering deep learning frameworks, machine learning models, and conventional statistical methods. It also presents a useful implementation using a web application built with Flask that forecasts EV energy use depending on variables like speed, temperature, battery capacity, and distance travelled. In

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Bae, Junseo, Sang-Guk Yum, and Ji-Myong Kim. "Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects." Sustainability 13, no. 11 (2021): 6376. http://dx.doi.org/10.3390/su13116376.

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Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-

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Xu, Chongchong, Zhicheng Liao, Chaojie Li, Xiaojun Zhou, and Renyou Xie. "Review on Interpretable Machine Learning in Smart Grid." Energies 15, no. 12 (2022): 4427. http://dx.doi.org/10.3390/en15124427.

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In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unres

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