Machine Learning Methods for Intrusion Detection: A Comprehensive Survey

Mobolaji Ogunbadejo; Oluwatobi Adedamola Ayilara-Adewale; Olanrewaju Alade

doi:10.18535/ijsrm/v13i07.ec07

Abstract

The exponential growth in connected networks, driven by the proliferation of the Internet of Things (IoT) and cloud computing, has resulted in surge in cyberattacks. Advanced and highly sophisticated threats have increased in prevalence, now encompassing advanced persistent threats, distributed denial-of-service attacks, and ransomware. Unfortunately, the signature- and rule-based detection mechanisms used in conventional Intrusion Detection Systems (IDSs) are failing to keep pace, especially with the increasing number of zero-day and newly discovered threats. Machine learning promises to be a futuristic technology due to its capability to identify patterns of activity, autonomously detect new attack designs, and instantly detect deviations in real-time. This survey comprehensively explores and examines the application of supervised, unsupervised, semi-supervised, hybrid, and deep learning methods in Intrusion Detection Systems (IDS), highlighting their unique contributions, strengths, and limitations.

Keywords

Intrusion detectionmachine learninganomaly detectiondeep learningcybersecurity

References

AFRIDI, S. 2024. Machine Learning Innovations in Intrusion Detection Systems (IDS): Emphasizing Ensemble Learning for Enhanced Security.Google Scholar ↗
AHMED, U., NAZIR, M., SARWAR, A., ALI, T., AGGOUNE, E.-H. M., SHAHZAD, T. & KHAN, M. A. 2025. Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering. Scientific Reports, 15, 1726.Google Scholar ↗
AKSHAYA, R. & SARAVANAN, C. A Novel Approach for Building Cyber Crime Prediction and Analysis Model using Random Forest. 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), 2024. IEEE, 1-6.Google Scholar ↗
AWAJAN, A. 2023. A novel deep learning-based intrusion detection system for IOT networks. Computers, 12, 34.Google Scholar ↗
DASH, N., CHAKRAVARTY, S., RATH, A. K., GIRI, N. C., ABORAS, K. M. & GOWTHAM, N. 2025. An optimized LSTM-based deep learning model for anomaly network intrusion detection. Scientific Reports, 15, 1554.Google Scholar ↗
DISHA, R. A. & WAHEED, S. A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset. 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 2021. IEEE, 1-5.Google Scholar ↗
DONG, H. & KOTENKO, I. 2025. Cybersecurity in the AI era: analyzing the impact of machine learning on intrusion detection. Knowledge and Information Systems, 1-52.Google Scholar ↗
ESHAK MAGDY, M., M MATTER, A., HUSSIN, S., HASSAN, D. & ELSAID, S. 2023. A Comparative study of intrusion detection systems applied to NSL-KDD Dataset. The Egyptian International Journal of Engineering Sciences and Technology, 43, 88-98.Google Scholar ↗
FENG, J. Improved Machine Learning-based System for Intrusion Detection. 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024), 2024. Atlantis Press, 130-136.Google Scholar ↗
KAVITHA, S. & UMA MAHESWARI, N. 2021. Network anomaly detection for NSL-KDD dataset using deep learning. Information Technology in Industry, 9, 821-827.Google Scholar ↗
KHAN, M. & GHAFOOR, L. 2024. Adversarial machine learning in the context of network security: Challenges and solutions. Journal of Computational Intelligence and Robotics, 4, 51-63.Google Scholar ↗
LIN, Q., LIU, Z., YANG, Y., WONG, K.-C., LU, Y. & LI, J. 2024. Multi-objective evolutionary neural architecture search for network intrusion detection. Swarm and Evolutionary Computation, 91, 101702.Google Scholar ↗
MASEER, Z. K., YUSOF, R., BAHAMAN, N., MOSTAFA, S. A. & FOOZY, C. F. M. 2021. Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE access, 9, 22351-22370.Google Scholar ↗
MOHAMMED, K. 2025. Enhancing Cybersecurity Through Artificial Intelligence: A Novel Approach to Intrusion Detection. International Journal of Advanced Computer Science and Applications, 16, 577 - 586.Google Scholar ↗
MOUSTAFA, N. 2021. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. Sustainable Cities and Society, 72, 102994.Google Scholar ↗
OGUNBADEJO, M. D., OLUWATOBI A AYILARA-ADEWALE & MOGHADDAM, A. 2025a. A State-of-the-Art Review of Ransomware Attacks on Internet of Things: Trends and Mitigation Strategies. Journal of Information Engineering and Applications, 15, 1-18.Google Scholar ↗
OGUNBADEJO, M. D., OLUWATOBI A. AYILARA-ADEWALE & ALADE, O. E. 2025b. Overview of Zero Trust Architecture Trend and Advancement in Information Security. Journal of Information Engineering and Applications, 15, 21-30.Google Scholar ↗
RAI, H. M., PAL, A., ERGASH O’G’LI, R. A., UGLI, B. A. K. & SHOKIROVICH, Y. S. 2025. Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection. Procedia Computer Science, 259, 140-149.Google Scholar ↗
SAHLI, Y. 2022. A comparison of the NSL-KDD dataset and its predecessor the KDD Cup’99 dataset. International Journal of Scientific Research and Management (IJSRM), 10, 832-839.Google Scholar ↗
SHARIF, M. H. U. & MOHAMMED, M. A. 2022. A literature review of financial losses statistics for cyber security and future trend. World Journal of Advanced Research and Reviews, 15, 138-156.Google Scholar ↗
TAYE, M. M. 2023. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12, 91.Google Scholar ↗
THAKKAR, A. & LOHIYA, R. 2022. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review, 55, 453-563.Google Scholar ↗
TRIPATHY, S. S. & BEHERA, B. 2024. A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges. IJISAE, 12, 3833-3857.Google Scholar ↗
WANG, Q., XIE, M., WU, Z. & YANG, D. Network Intrusion Detection and Dynamic Defense Method Based on Unsupervised Machine Learning. 2023 International Conference on Computer Simulation and Modeling, Information Security (CSMIS), 2023a. IEEE, 75-80.Google Scholar ↗
WANG, Y., ZHENG, W., LIU, Z., WANG, J., SHI, H., GU, M. & DI, Y. 2023b. A federated network intrusion detection system with multi-branch network and vertical blocking aggregation. Electronics, 12, 4049.Google Scholar ↗
YADAV, R., PATHAK, P. & SARASWAT, S. 2020. Comparative study of datasets used in cyber security intrusion detection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 6, 302-312.Google Scholar ↗
ZUKAIB, U., CUI, X., ZHENG, C., HASSAN, M. & SHEN, Z. 2024. Meta-IDS: meta-learning based smart intrusion detection system for internet of medical things (IoMT) network. IEEE Internet of Things Journal.Google Scholar ↗

[refR-1] AFRIDI, S. 2024. Machine Learning Innovations in Intrusion Detection Systems (IDS): Emphasizing Ensemble Learning for Enhanced Security.Google Scholar ↗

[refR-2] AHMED, U., NAZIR, M., SARWAR, A., ALI, T., AGGOUNE, E.-H. M., SHAHZAD, T. & KHAN, M. A. 2025. Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering. Scientific Reports, 15, 1726.Google Scholar ↗

[refR-3] AKSHAYA, R. & SARAVANAN, C. A Novel Approach for Building Cyber Crime Prediction and Analysis Model using Random Forest. 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), 2024. IEEE, 1-6.Google Scholar ↗

[refR-4] AWAJAN, A. 2023. A novel deep learning-based intrusion detection system for IOT networks. Computers, 12, 34.Google Scholar ↗

[refR-5] DASH, N., CHAKRAVARTY, S., RATH, A. K., GIRI, N. C., ABORAS, K. M. & GOWTHAM, N. 2025. An optimized LSTM-based deep learning model for anomaly network intrusion detection. Scientific Reports, 15, 1554.Google Scholar ↗

[refR-6] DISHA, R. A. & WAHEED, S. A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset. 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 2021. IEEE, 1-5.Google Scholar ↗

[refR-7] DONG, H. & KOTENKO, I. 2025. Cybersecurity in the AI era: analyzing the impact of machine learning on intrusion detection. Knowledge and Information Systems, 1-52.Google Scholar ↗

[refR-8] ESHAK MAGDY, M., M MATTER, A., HUSSIN, S., HASSAN, D. & ELSAID, S. 2023. A Comparative study of intrusion detection systems applied to NSL-KDD Dataset. The Egyptian International Journal of Engineering Sciences and Technology, 43, 88-98.Google Scholar ↗

[refR-9] FENG, J. Improved Machine Learning-based System for Intrusion Detection. 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024), 2024. Atlantis Press, 130-136.Google Scholar ↗

[refR-10] KAVITHA, S. & UMA MAHESWARI, N. 2021. Network anomaly detection for NSL-KDD dataset using deep learning. Information Technology in Industry, 9, 821-827.Google Scholar ↗

[refR-11] KHAN, M. & GHAFOOR, L. 2024. Adversarial machine learning in the context of network security: Challenges and solutions. Journal of Computational Intelligence and Robotics, 4, 51-63.Google Scholar ↗

[refR-12] LIN, Q., LIU, Z., YANG, Y., WONG, K.-C., LU, Y. & LI, J. 2024. Multi-objective evolutionary neural architecture search for network intrusion detection. Swarm and Evolutionary Computation, 91, 101702.Google Scholar ↗

[refR-13] MASEER, Z. K., YUSOF, R., BAHAMAN, N., MOSTAFA, S. A. & FOOZY, C. F. M. 2021. Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE access, 9, 22351-22370.Google Scholar ↗

[refR-14] MOHAMMED, K. 2025. Enhancing Cybersecurity Through Artificial Intelligence: A Novel Approach to Intrusion Detection. International Journal of Advanced Computer Science and Applications, 16, 577 - 586.Google Scholar ↗

[refR-15] MOUSTAFA, N. 2021. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. Sustainable Cities and Society, 72, 102994.Google Scholar ↗

[refR-16] OGUNBADEJO, M. D., OLUWATOBI A AYILARA-ADEWALE & MOGHADDAM, A. 2025a. A State-of-the-Art Review of Ransomware Attacks on Internet of Things: Trends and Mitigation Strategies. Journal of Information Engineering and Applications, 15, 1-18.Google Scholar ↗

[refR-17] OGUNBADEJO, M. D., OLUWATOBI A. AYILARA-ADEWALE & ALADE, O. E. 2025b. Overview of Zero Trust Architecture Trend and Advancement in Information Security. Journal of Information Engineering and Applications, 15, 21-30.Google Scholar ↗

[refR-18] RAI, H. M., PAL, A., ERGASH O’G’LI, R. A., UGLI, B. A. K. & SHOKIROVICH, Y. S. 2025. Advanced AI-Powered Intrusion Detection Systems in Cybersecurity Protocols for Network Protection. Procedia Computer Science, 259, 140-149.Google Scholar ↗

[refR-19] SAHLI, Y. 2022. A comparison of the NSL-KDD dataset and its predecessor the KDD Cup’99 dataset. International Journal of Scientific Research and Management (IJSRM), 10, 832-839.Google Scholar ↗

[refR-20] SHARIF, M. H. U. & MOHAMMED, M. A. 2022. A literature review of financial losses statistics for cyber security and future trend. World Journal of Advanced Research and Reviews, 15, 138-156.Google Scholar ↗

[refR-21] TAYE, M. M. 2023. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12, 91.Google Scholar ↗

[refR-22] THAKKAR, A. & LOHIYA, R. 2022. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review, 55, 453-563.Google Scholar ↗

[refR-23] TRIPATHY, S. S. & BEHERA, B. 2024. A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges. IJISAE, 12, 3833-3857.Google Scholar ↗

[refR-24] WANG, Q., XIE, M., WU, Z. & YANG, D. Network Intrusion Detection and Dynamic Defense Method Based on Unsupervised Machine Learning. 2023 International Conference on Computer Simulation and Modeling, Information Security (CSMIS), 2023a. IEEE, 75-80.Google Scholar ↗

[refR-25] WANG, Y., ZHENG, W., LIU, Z., WANG, J., SHI, H., GU, M. & DI, Y. 2023b. A federated network intrusion detection system with multi-branch network and vertical blocking aggregation. Electronics, 12, 4049.Google Scholar ↗

[refR-26] YADAV, R., PATHAK, P. & SARASWAT, S. 2020. Comparative study of datasets used in cyber security intrusion detection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 6, 302-312.Google Scholar ↗

[refR-27] ZUKAIB, U., CUI, X., ZHENG, C., HASSAN, M. & SHEN, Z. 2024. Meta-IDS: meta-learning based smart intrusion detection system for internet of medical things (IoMT) network. IEEE Internet of Things Journal.Google Scholar ↗