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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 · 18 cites

   Abstract 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 prote

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

   Adam Grabowski, Shengjie Xu · 2025 · Journal of Cybersecurity Education, Research and Practice · 3 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

3. 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

4. Artificial Intelligence in Cybersecurity: Machine Learning and Deep Learning for Intelligent Threat Detection

   Rita Dewanjee · 2026 · International Journal of Science and Research (IJSR)

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

6. Proactive Cybersecurity Using Machine Learning: Threat Detection, Diagnosis, and Propagation Analysis

   Deeksha Karthik, Anand Zutshi · 2025 · International Journal of Science and Research (IJSR)

7. Artificial Intelligence-Driven Cybersecurity Framework for Enterprise Threat Detection: A Machine Learning Approach

   Sanjida Akter Tisha · 2026 · The American Journal of Engineering and Technology

   The increasing complexity of cyber threats has exposed the limitations of traditional signature-based intrusion detection systems, creating a need for intelligent and adaptive cybersecurity solutions. This study proposes an artificial intelligence-driven cybersecurity framework for enterprise threat detection using the CICIDS2017 benchmark dataset. The framework incorporates data preprocessing, feature engineering, and supervised machine learning to classify network traffic as benign or malicious. Seven machine learning algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Extra Trees, LightGBM, and XGBoost, were evaluated using accuracy, precision,

8. Machine Learning and Deep Learning-Based Intrusion Detection for Cybersecurity

   Sudhakara Reddy Peram · 2026 · Journal of AI-Driven Cybersecurity Systems

   The study unequivocally demonstrates how machine learning algorithms may increase the accuracy of intrusion detection. Research Importance: Cyber security threats are on the rise, and as such, intrusion detection systems need to be more intelligent to detect potential cyber threats. The prediction of packet rates helps to improve intrusion detection accuracy, thus improving the reliability, scalability, and reliability of network security. Methodology: The paper makes use of various methodologies to improve intrusion detection accuracy by applying machine learning algorithms to predict packet rates. The paper makes use of statistical analysis to understand the relationships between variables