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Papers on “cybersecurity intrusion threat detection machine learning”

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

   Basharat Ali · 2025 · Journal of Cybersecurity · 21 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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2. 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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3. 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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4. AI-Driven Cybersecurity Frameworks for Real-Time Intrusion Detection and Threat Intelligence

   Dr. Dinesh Jayawardena, Dr. Kavindi Perera · 2026 · International Journal of Computer Science & Information System · 1 cite

   The rapid expansion of digital infrastructures, cloud ecosystems, Internet of Things (IoT) environments, and intelligent enterprise platforms has significantly increased the complexity and frequency of cybersecurity threats. Traditional security mechanisms based on static rules and signature-based detection approaches are increasingly insufficient against sophisticated attacks involving zero-day exploits, advanced persistent threats, automated malware, and coordinated intrusion campaigns. This research paper presents a comprehensive analysis of AI-driven cybersecurity frameworks designed for real-time intrusion detection and threat intelligence generation. The study explores the integration

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5. Machine Learning For Cybersecurity: Enhancing Intrusion Detection Systems And Threat Mitigation

   Bharath Nagaraju · 2020 · International Journal of Multidisciplinary Engineering in Current Research

   The time of sophistication and frequency of cyberattacks demands more sophisticated security mechanisms. In response, intrusion detection and threat mitigation became a powerful machine learning (ML) problem since it offers automated, real-time responses to cyber threats. In this study, we look at ML-based intrusion detection systems, and threat mitigation techniques as well as ML’s implementation challenges for cybersecurity. The issues of adversarial attacks, data privacy concerns, and model interpretability are discussed in the paper. Through the effort to solve these challenges and the improvement of ML-based security frameworks, organizations can improve their cyber security defenses ag

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6. CLOUD CYBERSECURITY USING MACHINE LEARNING FOR INTRUSION DETECTION AND THREAT MITIGATION

   Mr. H Madhusudhana Rao, Mr. Chakka Venkata Nagababu, Ms. Pemma Radhika · 2026 · International Journal of Engineering Research and Science & Technology

   Cloud computing has become a foundational technology for modern digital infrastructure, enabling scalable, on-demand access to computing resources across diverse applications. However, the increasing adoption of cloud services has also introduced significant cybersecurity challenges, including unauthorized access, data breaches, and sophisticated cyberattacks. Traditional security mechanisms often struggle to cope with the dynamic and distributed nature of cloud environments. This paper presents an AI-driven approach to intrusion detection and mitigation in cloud computing systems, leveraging advanced machine learning and deep learning techniques to enhance security and resilience. The propo

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7. Smart Cybersecurity Using Machine Learning for Intrusion Detection and Threat Prevention

   NVDP Murthy, Gagan Goyal, Md Ankushavali, et al. · 2026 · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)

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8. AI-Powered Cybersecurity: A Machine Learning Framework for Real-Time Intrusion Detection and Threat Analysis

   Hiroshi ONO, Jabulani Khumalo · 2026 · Euro vantage journal of artificial intelligence

   The increasing use of digital networks, cloud platforms, Internet of Things devices, and online services has created new opportunities for cybercriminals to perform sophisticated attacks. Traditional cybersecurity systems mainly depend on predefined rules and signatures, which can make it difficult to identify new and constantly changing threats. Artificial intelligence and machine learning provide new approaches for detecting suspicious activities by learning patterns from network traffic and identifying behavior associated with cyberattacks. This paper presents an AI-powered machine learning framework for real-time intrusion detection and threat analysis. The proposed framework collects ne

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