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Papers on “cybersecurity intrusion threat detection machine learning”

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1. An Edge Computing-Based and Threat Behavior-Aware Smart Prioritization Framework for Cybersecurity Intrusion Detection and Prevention of IEDs in Smart Grids With Integration of Modified LGBM and One Class-SVM Models

   Abdulmohsen Algarni, Zulfiqar Ahmad, Mohammed Alaa Ala'anzy · 2024 · IEEE Access · 29 cites

   The smart grid environment, which emphasizes sustainability, dependability, and efficiency through smart components such as Intelligent Electronic Devices (IEDs), communication networks, and control systems, marks a revolutionary change in the way traditional power distribution is carried out. As smart grids grow and are integrated into energy distribution networks, these systems become more vulnerable to cybersecurity threats due to their increased connectivity, usage of IEDs, and reliance on digital communication channels. This study presents an edge computing-based, threat behavior-aware smart prioritization framework with binary and multidimensional classification and detection of cybers

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2. On the fog’s frontline: a federated machine learning approach for industrial network threat detection and intrusion prevention

   Basharat Ali · 2025 · Journal of Cybersecurity · 23 cites

   The rapidly increasing field of industrial network security has led to the rapid growth of interconnecting devices, significantly enlarging attack surfaces and exposing flaws that older intrusion detection systems (IDS) cannot even handle due to scalability and privacy constraints. This work addresses the shortcomings by presenting an advanced federated framework for machine learning tailored toward intrusion detection in industrial networks. Using the detailed UNSW-NB15 dataset, known to represent realistic network traffic, we have analysed numerous machine learning methods in great detail to build a robust, adaptive, and privacy-preserving model for network protection. In a decentralized f

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3. Threat Detection Driven by Artificial Intelligence: Enhancing Cybersecurity with Machine Learning Algorithms

   Heyao Chen, Zepeng Shen, Yong Wang, et al. · 2024 · Journal of improved oil and gas recovery technology. · 17 cites

   This paper aims to explore the applications of artificial intelligence (AI) and machine learning (ML) in the field of cybersecurity, particularly in the development of end-to-end solutions for threat detection. By analyzing the current challenges in cybersecurity and the limitations of traditional threat detection methods, this paper seeks to demonstrate how AI/ML technologies can enhance the efficiency, accuracy, and automation levels of threat detection. The paper begins by introducing the core concepts of cybersecurity threat detection, including traditional methods such as signature-based detection, behavior-based detection, and rule-based detection systems. It then elaborates on the app

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4. Advancing cybersecurity with artificial intelligence and machine learning: Architectures, algorithms, and future directions in threat detection and mitigation

   Souratn Jain · 2025 · World Journal of Advanced Engineering Technology and Sciences · 14 cites

   The ever-growing number of and development of more elaborate threats require different levels of protection than formal regulation. AI & ML technology provide a promising outlook that even exists in the security domain and has become universal to design better, more dynamic security measures with better preparedness. In particular, the current paper discusses the correlation between AI, ML, and cybersecurity regarding architectures, algorithms, and potential for further development. The chosen AI-based architectures are captured here, like deep learning models, federated learning frameworks, and graph-based techniques to detect malware, phishing, ransomware, and insider threats. The paper th

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5. MACHINE LEARNING FOR CYBERSECURITY: THREAT DETECTION AND PREVENTION

   Husna Sultana · 2024 · ShodhKosh: Journal of Visual and Performing Arts · 6 cites

   The increasing sophistication and frequency of cyber threats pose significant challenges for organizations worldwide, necessitating advanced solutions for threat detection and prevention. Traditional cybersecurity measures, such as signature-based detection and rule-based systems, often fall short in identifying novel and complex attacks. This paper explores the application of machine learning (ML) as a transformative approach to enhance cybersecurity, focusing on its effectiveness in threat detection and prevention. Machine learning algorithms enable systems to learn from historical data, recognize patterns, and adapt to new threats in real-time. By leveraging techniques such as supervised,

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6. Zero-Day Threat Detection A Machine Learning Paradigm for Intrusion Prevention

   G. Gowthami, S. Priscila · 2024 · 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) · 6 cites

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7. Cybersecurity Threat Detection using Machine Learning and Network Analysis

   Amaresh Kumar · 2024 · Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 · 5 cites

   Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns of successful attacks. However, two significant drawbacks hinder the widespread adoption of ML in security analysis: high computing overheads and the need for specialized frameworks. This study aims to quantify the extent to which a hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet of Things (IoT) network

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8. Advancing Cybersecurity Practice: Explainable Machine Learning for Network Intrusion Detection

   Adam Grabowski, Shengjie Xu · 2025 · Journal of Cybersecurity Education, Research and Practice · 4 cites

   This research investigates explainable artificial intelligence (XAI) integration within machine learning (ML)-based intrusion detection systems (IDS), focusing on distinguishing malicious from benign network activities. We employed Random Forest and XGBoost models evaluated on widely recognized datasets, including NSL-KDD and UNSW-NB15, using both binary and multi-class classification tasks. The objective was to enhance cybersecurity operations through improved model transparency and interpretability. By integrating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), the study offers comprehensive global and local insights into model decision-maki

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