Computer Science · Literature

Research papers on Cybersecurity threat detection

Recent and highly-cited academic work on cybersecurity threat detection, gathered from Semantic Scholar, CrossRef and OpenAlex.

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 citations

   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 citations

   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 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 citations

   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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4. MACHINE LEARNING FOR CYBERSECURITY: THREAT DETECTION AND PREVENTION

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

   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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5. 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 citations

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6. Cybersecurity Threat Detection using Machine Learning and Network Analysis

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

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

   Adam Grabowski, Shengjie Xu · 2025 · Journal of Cybersecurity Education, Research and Practice · 4 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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8. Enhancing Cybersecurity Through AI: A Machine Learning-Based Framework for Real-Time Threat Detection and Mitigation

   Abdullah Faiz, Amjad Jumani, Abdul Hafiz, et al. · 2025 · Annual Methodological Archive Research Review · 3 citations

   With the continually increasing evolution of cyber threats in both their complexity and occurrence, the signature based intrusion detection systems have been found inadequate in providing proactive and responsive network protection. The paper proposes a machine learning framework, which maximizes cybersecurity by performing real-time threat detection and mitigation in a layer-based approach by utilizing both unsupervised and supervised models. This framework uses the K-Means clustering method to find anomalies and then uses the Random Forest and Deep Neural Network (DNN) classifier to precisely detect and label the threat. When tested on the CICIDS2017 dataset, the system showed high detecti

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9. Machine Learning Models for Cybersecurity in the USA firms and develop models to enhance threat detection

   Md Shawon Islam · 2024 · Journal of Business Venturing, AI and Data Analytics · 3 citations

   In the context of global digitalization trends, the problem of the impact of cyberattacks on the company is significantly relevant. The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data, and the discovery of machine learning is one of the most successful ways to address this problem. This article is devoted to the impact of cyberattacks on the US firms’ market value since it is an indicator of firm performance and how it can be solved by using machine learning technology. The paper’s central hypothesis is th

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10. Machine Learning in Cybersecurity: A Comprehensive Review of Threat Detection, Prevention, and Response Strategies

    Tanvi Desai, Rakesh Kumar Pal · 2025 · 2025 4th International Conference on Computational Modelling, Simulation and Optimization (ICCMSO) · 2 citations

    The increased complexity and rate of cyberattacks have necessitated a change in the paradigm of cybersecurity strategies. The conventional rule-based systems are increasingly insufficient to address the dynamic and adaptive behaviour of modern threats. Machine learning (ML), with its ability to learn from vast amounts of data and identify complex patterns, has emerged as a powerful ally in combating these threats. This paper offers a comprehensive review of the application of ML in cybersecurity, its applications across the entire threat lifecycle. We present a wide range of ML approaches, including supervised, unsupervised, and deep learning approaches, and their effectiveness in areas of h

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11. Cybersecurity With Machine Learning: Implementing AI Algorithms for Intrusion Prevention, Advanced Data Protection, and Real-Time Threat Analysis

    Krishna Bonagiri, P. Krishnamoorthy, V. Keerthiga, et al. · 2025 · 2025 International Conference on Computational, Communication and Information Technology (ICCCIT) · 2 citations

    In an era where cyber attacks are getting increasingly complex, the incorporation of machine learning (ML) algorithms into cybersecurity processes has emerged as a crucial technique for boosting protection against potential intrusions and data breaches. For the purpose of preventing intrusions, providing superior data protection, and conducting real-time threat analysis, this research investigates the application of machine learning approaches that are driven by artificial intelligence. Organizations are able to increase their preventative defenses against cyber attacks by utilizing algorithms that are capable of analyzing massive volumes of data and identifying patterns that are indicative

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12. Web Attack Intrusion Detection System Using Machine Learning Approaches for Cybersecurity

    Ali E. Takieldeen, Aya El-Sayed El-Metwaly, Rahma Rezk Elzahdany, et al. · 2025 · 2025 International Telecommunications Conference (ITC-Egypt) · 1 citations

    This paper presents a real-time intrusion detection system (IDS) for web attacks that leverages machine learning techniques applied to web server logs. The increasing sophistication of web attacks necessitates advanced detection methods that can adapt to evolving threat landscapes. Our system addresses this challenge by employing a multi-stage approach: (1) comprehensive feature engineering, including novel features extracted from web logs, (2) feature selection to identify the most relevant attributes, (3) classification using various machine learning algorithms (Support Vector Machine, Gradient Boosted Trees, Decision Tree, and Random Forest), and (4) a real-time detection engine that proc

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13. Machine Learning Techniques for Cybersecurity Threat Detection: A Systematic Review

    M. Karatu, Ibrahim Musa Mungadi, Anas Shehu · 2026 · International Journal of Innovative Science and Research Technology · 1 citations

    The increasing complexity and prevalence of cyber threats have made the adoption of intelligent and adaptive security frameworks imperative. Traditional signature-based detection methods have proven insufficient, particularly in identifying zero-day exploits and rapidly evolving attack patterns. In this context, machine learning (ML) has emerged as a robust approach to cybersecurity threat detection, owing to its ability to learn underlying patterns and detect anomalies within large and complex datasets. This study presents a comprehensive review of machine learning techniques applied in cybersecurity, encompassing supervised, unsupervised, and deep learning paradigms. It examines the applic

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14. Machine Learning-Driven Threat Detection and Response Framework for Modern Cybersecurity Systems

    Sathesh P. Sivashanmugam · 2026 · 2026 14th International Symposium on Digital Forensics and Security (ISDFS) · 1 citations

    With the rapid growth of cyber threats in modern network environments, intelligent and automated intrusion detection systems have become essential for ensuring cybersecurity resilience. The paper suggests intelligent threat detection and response mitigation framework that is based on security data and is assessed by the CICIDS-2017 dataset. The methodology consists of systematic data preprocessing, outlier and duplicate removal, and correlation-based feature selection, which reduces the 78 original features to 45 important network-flow attributes. RobustScaler is used to normalize the features, and Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a hybrid stacking ensemble model

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

    Bharath Nagaraju · 2020 · International Journal of Multidisciplinary Engineering in Current Research · 1 citations

    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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16. 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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17. Hybrid Quantum-Classical Machine Learning for Cybersecurity Threat Detection and Intrusion Prediction: a Systematic Review

    Nancy Louise, V. Kavitha · 2026 · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)

    The paper presents a PRISMA 2020-compliant systematic review of hybrid quantum-classical machine learning (HQCML) for cybersecurity intrusion detection, with emphasis on the Store Now, Decrypt Later (SNDL) threat. Searching five databases, 87 primary studies (2018-2024) were identified; 72 passed a Critical Appraisal Skills Programme (CASP) quality threshold$(\geq 6 / 10)$, with sensitivity analysis at$\geq 7 / 10$yielding 58 studies and consistent results ($\kappa=0.83$). Amplitude-encoded variational quantum circuit (VQC) classifiers match deep learning accuracy with significantly fewer trainable parameters. Three key barriers emerge: 80.6 % of studies rely solely on simulation, inference

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

    N. Murthy, Gagan K. Goyal, Md. Ankushavali, et al. · 2026 · 2026 2nd International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)

    The growing complexity and advancement of the modern cyberattacks have proven the insecurity of the old-fashioned cybersecurity tools, in the form of firewall and signature-based intrusion detecting tools. Evolution of advanced protracted threats, phishing, ransomware and constantly-changing malware has made a sort of a defensive strategy rather useless. Artificial Intelligence (AI), specifically, the use of Machine Learning (ML), and Deep Learning (DL) in the real-time detection, classification, and reduction of cyber threats is more dynamic and reactive in this instance. The article discusses the application of AI to cybersecurity and machine learning techniques of intrusion detection, ano

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19. Efficient Threat Detection Machine Learning Techniques for Intrusion Detection Utilizing Data Engineering

    T. Sharath, A. Muthukumaravel · 2024 · 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC)

    This study presents a novel methodology for enhancing intrusion detection systems (IDS) through the integration of state-of-the-art data engineering techniques and machine learning approaches. Traditional intrusion detection systems are facing increasing challenges in keeping pace with the continuously evolving complexity of cyber-attacks. By means of rigorous data preparation, feature extraction, and model modification, The proposed work augments the scalability and precision of threat detection methodologies. Through the integration of machine learning and data engineering, the methodology presents a potential resolution to the obstacle of implementing effective threat detection in the rea

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20. Machine Learning for Cybersecurity Enhancing Threat Detection Systems

    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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21. Evaluating Machine Learning Techniques for Cybersecurity Threat Detection

    Kh. Md. Nazmul Hossain, Mohammad Shoaib Rahman, Abdullah Al Prince, et al. · 2025 · 2025 28th International Conference on Computer and Information Technology (ICCIT)

    Cybersecurity is an ever-growing challenge as network infrastructures face increasingly sophisticated intrusion attempts. This study investigates the application of machine learning algorithms for network intrusion detection using the NSL-KDD dataset, a widely recognized benchmark in cybersecurity research. The methodology encompasses extensive data preprocessing steps such as label binarization to distinguish between normal and attack traffic, encoding of categorical features including protocol type, service, and flag, and normalization of numerical features to ensure uniform scale. Four machine learning classifiers-Random Forest, Support Vector Machine (SVM), Gradient Boosting, and XGBoost

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22. Artificial Intelligence in Cybersecurity: A Review of Machine Learning-Based Threat Detection Techniques, Challenges, and Future Directions

    Amrish Dhawan, Karmjit Kaur, Amninder Kaur · 2026 · Advanced International Journal of Multidisciplinary Research

    Artificial intelligence (AI), particularly machine learning (ML), has gained increasing importance in cybersecurity threat detection by enabling the analysis of network traffic and the identification of potentially malicious activities. However, the effectiveness of these techniques depends on data quality, detection performance, generalisation to unfamiliar attacks, and resilience to adversarial manipulation. This paper presents a qualitative, literature-based review of machine learning techniques used in cybersecurity threat detection, with particular emphasis on intrusion detection systems. It examines supervised learning, unsupervised learning, deep learning, and hybrid or ensemble appro

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23. Application of Machine Learning in Cybersecurity, Threat Detection and Prevention

    Dr.Sujata Pattnaik Dr.Sujata Pattnaik · 2026 · International Journal of Creative and Open Research in Engineering and Management

    The rapid growth of digital technologies and internet-based services has significantly increased the risk of cyber threats across organizations, educational institutions, financial systems, and government sectors. Traditional cybersecurity techniques often struggle to detect sophisticated and evolving attacks in real time. In this context, Machine Learning (ML) has emerged as a powerful approach for improving cybersecurity threat detection and prevention mechanisms. This research article explores the application of machine learning algorithms in identifying malicious activities, detecting anomalies, and preventing cyberattacks before they cause severe damage. Various supervised and unsupervi

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24. 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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