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Research papers on Cybersecurity threat detection

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  1. Survey of intrusion detection systems: techniques, datasets and challenges

    Ansam Khraisat, Iqbal Gondal, Peter Vamplew, et al. · 2019 · Cybersecurity · 1,873 citations

    Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes.

  2. Deep Learning Approach for Intelligent Intrusion Detection System

    R. Vinayakumar, Mamoun Alazab, K. P. Soman, et al. · 2019 · IEEE Access · 1,819 citations

    Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attackin

  3. Network intrusion detection system: A systematic study of machine learning and deep learning approaches

    Zeeshan Ahmad, Adnan Shahid Khan, Cheah Wai Shiang, et al. · 2020 · Transactions on Emerging Telecommunications Technologies · 1,220 citations

    Abstract The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accu

  4. Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

    Hongyu Liu, Bo Lang · 2019 · Applied Sciences · 1,078 citations

    Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine lea

  5. Cybersecurity data science: an overview from machine learning perspective

    Iqbal H. Sarker, A. S. M. Kayes, Shahriar Badsha, et al. · 2020 · Journal Of Big Data · 731 citations

    Abstract In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model , is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science , where the data is being gathered from relevant cybersecurity sources, and the analyti

  6. Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset

    Ziadoon Kamil Maseer, Robiah Yusof, Nazrulazhar Bahaman, et al. · 2021 · IEEE Access · 443 citations

    An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters

  7. A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

    Qiang Liu, Pan Li, Wentao Zhao, et al. · 2018 · IEEE Access · 426 citations

    Machine learning is one of the most prevailing techniques in computer science, and it has been widely applied in image processing, natural language processing, pattern recognition, cybersecurity, and other fields. Regardless of successful applications of machine learning algorithms in many scenarios, e.g., facial recognition, malware detection, automatic driving, and intrusion detection, these algorithms and corresponding training data are vulnerable to a variety of security threats, inducing a significant performance decrease. Hence, it is vital to call for further attention regarding security threats and corresponding defensive techniques of machine learning, which motivates a comprehensiv

  8. Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

    Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, et al. · 2018 · Applied Sciences · 318 citations

    The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the l

  9. Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

    Kamran Shaukat, Suhuai Luo, Vijay Varadharajan, et al. · 2020 · Energies · 301 citations

    Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments mad

  10. Machine learning in cybersecurity: a comprehensive survey

    Dipankar Dasgupta, Zahid Akhtar, Sajib Sen · 2020 · The Journal of Defense Modeling and Simulation Applications Methodology Technology · 249 citations

    Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user au

  11. Innovative Machine Learning Algorithms for Classification and Intrusion Detectionv By IJISRT

    Pankaj Malik, Parag Jhala, Vedanshi Sharma, et al. · 2024 · International Journal of Innovative Science and Research Technology (IJISRT) · 208 citations

    With the escalating sophistication of cyber threats, the need for robust intrusion detection systems has become paramount in safeguarding information systems. This research addresses the limitations of traditional methods by proposing and evaluating innovative machine learning algorithms for classification in intrusion detection. The study explores a diverse set of algorithms designed to enhance accuracy, efficiency, and adaptability in the dynamic landscape of cybersecurity. The introduction provides a context for the research, emphasizing the critical role of intrusion detection in contemporary cybersecurity. A comprehensive literature review underscores the shortcomings of existing method

  12. Machine learning in cybersecurity: A review

    Anand Handa, Ashu Sharma, Sandeep K. Shukla · 2019 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 194 citations

    Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat analysis, anomaly based intrusion detection of prevalent attacks on critical infrastructures, and many others. Due to the ineffectiveness of signature‐based methods in detecting zero day attacks or even slight variants of known attacks, machine learning‐based detection is being used by researchers in many cybersecurity products. In this review, we discuss several areas of cybersecurity where machine learning is used as a tool. We also provide a few glimpses

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

    Basharat Ali · 2025 · Journal of Cybersecurity · 14 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

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

    Adam Grabowski, Shengjie Xu · 2025 · Journal of Cybersecurity Education, Research and Practice · 1 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

  17. 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

  19. 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,

  20. 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

  22. 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

  23. AI-Powered Threat Detection: Enhancing Cybersecurity with Machine Learning

    Krishna Chiatanya Chaganti · 2023 · EPH - International Journal of Science And Engineering

    Cybersecurity is being transformed by artificial intelligence (AI), which also provides a great benefit in spotting and reducing cyber vulnerabilities. Conventional security solutions usually fail to change as cyberattacks become more complicated. Using machine learning to examine large volumes, identify patterns, and find anomalies suggestive of possible hazards, artificial intelligence-driven threat detection seeks out Unlike conventional systems based on set rules, artificial intelligence can grow and improve on its own over time, hence it is very successful against changing cyberthreats. The main contribution of AI in cybersecurity is investigated in this article along with its ability t

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