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Research papers on Cybersecurity threat detection

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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 citations

    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

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

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

    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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  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 citations

    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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  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,

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  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

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  10. Machine Learning for Cybersecurity Enhancing Threat Detection Systems

    Dr. John Doe · 2025 · American Journal of Machine Learning

    The rise of cyber threats has highlighted the need for advanced methods in cybersecurity. Machine learning (ML) has emerged as a powerful tool to enhance threat detection systems by enabling the automated identification of complex patterns and anomalies. This paper explores the role of ML in cybersecurity, focusing on its applications in threat detection, risk assessment, and intrusion detection systems. Key ML techniques, such as supervised learning, unsupervised learning, and reinforcement learning, are evaluated for their effectiveness in identifying security threats. Challenges and future directions for ML-driven cybersecurity solutions are also discussed, providing insights into the ong

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  11. A Review of Machine Learning Techniques for Network Intrusion Detection Systems

    Mr. Madhav Sharma · 2026 · International Journal of Cyber Threat Intelligence and Secure Networking

    Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS. This covers several detection methods, including signature-based, anomaly-based, specification-based, and behavior-based approaches, as well as their advantages and disadvantages in recognizing both existing and new cyber threats. The re

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  12. Cybersecurity Threat Intelligence Using Machine Learning Classification Techniques

    Mr. Madhav Sharma · 2026 · International Journal of Cyber Threat Intelligence and Secure Networking

    New attacks are getting smarter and more sophisticated, so the old signature-based intrusion detection and prevention systems can't find them. This work proposes a machine learning approach to build a cybersecurity threat intelligence framework for effective multiclass intrusion detection, in which the Decision Tree classifier is used. The CICIDS2017 benchmark dataset, which contains both benign network traffic and several types of cyberattacks, is used to build and test the suggested model. The goal of the preparation process is to enhance classification performance by cleaning and separating data, utilizing Standard Scaler to scale features, and SMOTE to balance classes. Metrics like as re

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