Enhancing Cybersecurity Threat Detection Using Machine Learning: A Comprehensive Review
DOI:
https://doi.org/10.37385/jaets.v7i1.8814Keywords:
Cyber security, threat detection, machine learning, Adaptive Boosting, XGBoost, SVMAbstract
Cybersecurity forms the backbone of digital infrastructure that protects overstretched payment systems, governmental operations, and business continuity today. With machine learning (ML) techniques, it can help analyze a large amount of data and improve cyber-security. It’s tough to quantify how effective the ML-based cybersecurity system is, especially when we theorize it. This review paper talks about the significant role of ML in security, threat detection and security measures. Using machine learning algorithms helps in cybersecurity as they make the system automatic and fast. We can implement a threat detection security model using widely used ML algorithms. For classification purposes, we have Support Vector Machines (SVM), Decision Trees (DT), Random forests (RF), and Adaptive and Extreme gradient boosting (XGBoost). This review paper proposes ML algorithms for the implementation of cybersecurity with some practical application demonstrations. Machine learning algorithms can provide valuable analytics to help bolster security and reduce threats. We assess the accuracy of threat detection in network security by utilizing a set of formulas based on confusion, recall, F1-score, time complexity, accuracy and precision. This review synthesizes algorithmic performance across benchmark datasets (CICIDS2017 NSL-KDD UNSW-NB15) to identify significant gaps in previous ML-based cybersecurity frameworks. The results demonstrate the superior precision (90. 8 percent) and scalability of XGBoost.
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Agarwal, A., Sharma, P., Alshehri, M., Mohamed, A. A., & Alfarraj, O. (2021). Classification model for accuracy and intrusion detection using machine learning approach. PeerJ Computer Science, 7, e437. https://doi.org/10.7717/peerj-cs.437
Ahn, B., Kim, T., Ahmad, S., Mazumder, S. K., Johnson, J., Mantooth, H. A., & Farnell, C. (2023). An overview of cyber-resilient smart inverters based on practical attack models. IEEE Transactions on Power Electronics, 39(4), 4657-4673. https://doi.org/10.1109/TPEL.2023.3342842
Ahsan, M., Nygard, K. E., Gomes, R., Chowdhury, M. M., Rifat, N., & Connolly, J. F. (2022). Cybersecurity threats and their mitigation approaches using Machine Learning—A Review. Journal of Cybersecurity and Privacy, 2(3), 527-555. https://doi.org/10.3390/jcp2030027
Ahsan, M., Gomes, R., Chowdhury, M. M., & Nygard, K. E. (2021). Enhancing machine learning prediction in cybersecurity using dynamic feature selector. Journal of Cybersecurity and Privacy, 1(1), 199–218. https://doi.org/10.3390/jcp1010011
Al Razib, M., Javeed, D., Khan, M. T., Alkanhel, R., & Muthanna, M. S. A. (2022). Cyber threats detection in smart environments using SDN-enabled DNN-LSTM hybrid framework. IEEe Access, 10, 53015-53026. https://doi.org/10.1109/ACCESS.2022.3172304
Alashhab, A. A., Zahid, M. S., Isyaku, B., Elnour, A. A., Nagmeldin, W., Abdelmaboud, A., Abdullah, T. A. A., & Maiwada, U. D. (2024). Enhancing DDoS attack detection and mitigation in SDN using an ensemble online machine learning model. IEEE Access, 12, 51630–51649. https://doi.org/10.1109/ACCESS.2024.3384398
Almotairi, A., Atawneh, S., Khashan, O. A., & Khafajah, N. M. (2024). Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models. Systems Science & Control Engineering, 12(1), 2321381. https://doi.org/10.1080/21642583.2024.2321381
Al-Shehari, T. A., Alshamrani, A., & Alsabaan, M. (2024). Enhancing insider threat detection in imbalanced cybersecurity settings using the density-based local outlier factor algorithm. IEEE Access, 12, 34820–34834. https://doi.org/10.1109/ACCESS.2024.3373694
Arshad, K., Khan, W., Khan, M. A., & Gumaei, A. (2022). Deep reinforcement learning for anomaly detection: A systematic review. IEEE Access, 10, 124017–124035. https://doi.org/10.1109/ACCESS.2022.3224023
Babagana, A., Adewale, A. A., & Idris, Y. A. (2024). The role of artificial intelligence in cybersecurity: A review of AI techniques and applications. Journal of Artificial Intelligence and Security, 12(2), 56–70.
Chukwunweike, J. N., Praise, A., & Bashirat, B. A. (2024). Harnessing Machine Learning for Cybersecurity: How Convolutional Neural Networks are Revolutionizing Threat Detection and Data Privacy. https://doi.org/10.55248/gengpi.5.0824.2402
Dhaiya, S., Pandey, B. K., Adusumilli, S. B. K., & Avacharmal, R. (2021). Optimizing API Security in FinTech Through Genetic Algorithm based Machine Learning Model. International Journal of Computer Network and Information Security, 13(3), 24.
Ejiofor, O. E. (2023). A comprehensive framework for strengthening USA financial cybersecurity: Integrating machine learning and AI in fraud detection systems. European Journal of Computer Science and Information Technology, 11(6), 62–83. https://doi.org/10.37745/ejcsit.2013/vol11n66283
Ekundayo, F., Atoyebi, I., Soyele, A., & Ogunwobi, E. (2024). Predictive analytics for cyber threat intelligence in fintech using big data and machine learning. International Journal of Research Publication and Reviews, 5(11), 1–15. https://doi.org/10.55248/gengpi.5.1124.3352
Ferrag, M. A., Maglaras, L. A., & Janicke, H. (2021). Cyber security intrusion detection for agriculture 4.0: Machine learning-based solutions, datasets, and future directions. IEEE/CAA Journal of Automatica Sinica, 9(3), 407–436. https://doi.org/10.1109/JAS.2021.1004344
Guo, H., Li, J., Liu, J., Tian, N., & Kato, N. (2021). A survey on space-air-ground-sea integrated network security in 6G. IEEE Communications Surveys & Tutorials, 24(1), 53-87. https://doi.org/10.1109/COMST.2021.3131332
Haider, A., Aslam, M., Shah, M. A., Khan, A., & Baig, M. A. (2021). A real-time sequential deep extreme learning machine cybersecurity intrusion detection system. Computers, Materials & Continua, 66(2), 1785–1798. https://doi.org/10.32604/cmc.2020.013910
Hossain, M. A., & Islam, M. S. (2023). Ensuring network security with a robust intrusion detection system using ensemble-based machine learning. Array, 19, 100306. https://doi.org/10.1016/j.array.2023.100306
Ige, A. B., Kupa, E., & Ilori, O. (2024). Analyzing defense strategies against cyber risks in the energy sector: Enhancing the security of renewable energy sources. International Journal of Science and Research Archive, 12(1), 2978–2995. https://doi.org/10.30574/ijsra.2024.12.1.1186
Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. J. Sci. Technol, 11, 001-024. https://doi.org/10.53022/oarjst.2024.11.1.0060
Injadat, M., Moubayed, A., Shami, A., & Lutfiyya, H. (2020). Multi-stage optimized machine learning framework for network intrusion detection. IEEE Transactions on Network and Service Management, 18(2), 1803–1816. https://doi.org/10.1109/TNSM.2020.3014929
Katiyar, N., Tripathi, M. S., Kumar, M. P., Verma, M. S., Sahu, A. K., & Saxena, S. (2024). AI and Cyber-Security: Enhancing threat detection and response with machine learning. Educational Administration: Theory and Practice, 30(4), 6273-6282. https://doi.org/10.53555/kuey.v30i4.2377
Kaur, S., Mahajan, S., Yousuf, M., & Koul, A. (2024). Security issues and challenges in cybersecurity: A comprehensive review. Journal of Information Technology and Software Engineering, 14(1), 1–6.
Keserwani, H., Ali, S., Bhatnagar, A., & Gupta, M. (2022). Security enhancement by identifying attacks using machine learning for 5G network. International Journal of Communication Networks and Information Security, 14(2), 124–141.
Khan, M., & Ghafoor, L. (2024). Adversarial machine learning in the context of network security: Challenges and solutions. Journal of Computational Intelligence and Robotics, 4(1), 51–63.
Kravchik, M., & Shabtai, A. (2021). Efficient cyber-attack detection in industrial control systems using lightweight neural networks and PCA. IEEE Transactions on Dependable and Secure Computing, 19(4), 2179–2197. https://doi.org/10.1109/TDSC.2021.30
Kuppa, A., & Le-Khac, N.-A. (2021). Adversarial XAI methods in cybersecurity. IEEE Transactions on Information Forensics and Security, 16, 4924–4938. https://doi.org/10.1109/TIFS.2021.3117075
Le, D. C., Zincir-Heywood, N., & Heywood, M. I. (2020). Analyzing data granularity levels for insider threat detection using machine learning. IEEE Transactions on Network and Service Management, 17(1), 30–44. https://doi.org/10.1109/TNSM.2020.2967721
Li, Y., & Yan, J. (2022). Cybersecurity of smart inverters in the smart grid: A survey. IEEE Transactions on Power Electronics, 38(2), 2364–2383. https://doi.org/10.1109/TPEL.2022.3206239
Mahmood, R. K., Mahameed, A. I., Lateef, N. Q., Jasim, H. M., Radhi, A. D., Ahmed, S. R., & Tupe-Waghmare, P. (2024). Optimizing network security with machine learning and multi-factor authentication for enhanced intrusion detection. Journal of Robotics and Control (JRC), 5(5), 1502–1524. https://doi.org/10.18196/jrc.v5i5.22508
Mamidi, S. R. (2024). The role of AI and machine learning in enhancing cloud security. Journal of Artificial Intelligence General Science (JAIGS), 3(1), 403–417. https://doi.org/10.60087/jaigs.v3i1.161
Mohammed, S. H., Shabut, A. M., Ali, M. H., & Al-Zubaidi, S. (2024). A review on the evaluation of feature selection using machine learning for cyber-attack detection in smart grid. IEEE Access, 12, 44023–44042. https://doi.org/10.1109/ACCESS.2024.3370911
Mukesh, V. (2025). A Comprehensive Review of Advanced Machine Learning Techniques for Enhancing Cybersecurity in Blockchain Networks. Journal ID, 8736, 2145.
Musa, M. A., Adeyemi, A. O., & Okon, I. E. (2024). AI-driven approaches for improving cybersecurity in healthcare systems. Journal of Medical Systems and Cybersecurity, 3(1), 17–30.
Nabi, F., & Zhou, X. (2024). Enhancing intrusion detection systems through dimensionality reduction: A comparative study of machine learning techniques for cyber security. Cyber Security and Applications, 2, 100033. https://doi.org/10.1016/j.csa.2023.100033
Nassif, A. B., Shahin, T. M., Talib, M. A., & Azad, M. A. K. (2021). Machine learning for cloud security: A systematic review. IEEE Access, 9, 20717–20735. https://doi.org/10.1109/ACCESS.2021.3054129
Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., ... & Pathan, M. S. (2024). Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration. Journal of King Saud University-Computer and Information Sciences, 36(2), 101939. https://doi.org/10.1016/j.jksuci.2024.101939
Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat detection and defense mechanisms. World Journal of Advanced Research and Reviews, 21(1), 2286–2295. https://doi.org/10.30574/wjarr.2024.21.1.0315
Olutimehin, A. T. (2025). The Synergistic Role of Machine Learning, Deep Learning, and Reinforcement Learning in Strengthening Cyber Security Measures for Crypto Currency Platforms. Deep Learning, and Reinforcement Learning in Strengthening Cyber Security Measures for Crypto Currency Platforms (February 11, 2025). https://dx.doi.org/10.2139/ssrn.5138889
Ozkan-Okay, M., Akin, E., Aslan, Ö., Kosunalp, S., Iliev, T., Stoyanov, I., & Beloev, I. (2024). A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions. IEEe Access, 12, 12229-12256. https://doi.org/10.1109/ACCESS.2024.3355547
Saheed, Y. K., & Arowolo, M. O. (2021). Efficient cyber-attack detection on the internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access, 9, 161546–161554. https://doi.org/10.1109/ACCESS.2021.3128837
Sarker, I. H. (2021). CyberLearning: Effectiveness analysis of machine learning security modelling to detect cyber-anomalies and multi-attacks. Internet of Things, 14, 100393. https://doi.org/10.1016/j.iot.2021.100393
Shaukat, K., Luo, S., Varadharajan, V., & Chen, S. (2020). Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies, 13(10), 2509. https://doi.org/10.3390/en13102509
Siddiqi, M. A., & Pak, W. (2021). An agile approach to identify single and hybrid normalization for enhancing machine learning-based network intrusion detection. IEEE Access, 9, 137494–137513. https://doi.org/10.1109/ACCESS.2021.3118361
Tendikov, N., Rzayeva, L., Saoud, B., Shayea, I., Hadri Azmi, M., Myrzatay, A., & Alnakhli, M. (2024). Security information event management data acquisition and analysis methods with machine learning principles. Results in Engineering, 22, 102254. https://doi.org/10.1016/j.rineng.2024.102254
Tulli, S. K. C. (2023). Enhancing Marketing, Sales, Innovation, and Financial Management Through Machine Learning. International Journal of Modern Computing, 6(1), 41-52.
Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development through AI, ML and IoT, 2(2), 1–8.
Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2024). Enhancing cloud security: The role of artificial intelligence and machine learning. In Improving security, privacy, and trust in cloud computing (pp. 85-112). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1431-9.ch004
Wang, L., Zhang, Y., Li, X., & Chen, W. (2024). A deep learning-based approach for network intrusion detection. Journal of Cybersecurity Research, 6(3), 112–126.
Wazid, M., Das, A. K., & Rodrigues, J. J. P. C. (2022). Uniting cybersecurity and machine learning: Advantages, challenges and future research. ICT Express, 8(3), 313–321. https://doi.org/10.1016/j.icte.2022.04.007
Wu, H., Ding, S., Wang, H., Yang, S., & Deng, Q. (2020). Research on artificial intelligence enhancing internet of things security: A survey. IEEE Access, 8, 153826–153848. https://doi.org/10.1109/ACCESS.2020.3018170
Yadav, A., Singh, R., Verma, R., & Sharma, D. (2024). Application of AI in cybersecurity: A survey. International Journal of Advanced Computer Science and Applications, 15(1), 89–99.
Yaseen, A. (2023). The role of machine learning in network anomaly detection for cybersecurity. SAGE Science Review of Applied Machine Learning, 6(8), 16–34.
Ye, J., Chen, B., Yang, Y., & Mu, Y. (2021). A review of cyber–physical security for photovoltaic systems. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(4), 4879–4901. https://doi.org/10.1109/JESTPE.2021.3111728
Yeboah-Ofori, A., Imamverdiyev, Y., & Epiphaniou, G. (2021). Cyber threat predictive analytics for improving cyber supply chain security. IEEE Access, 9, 94318–94337. https://doi.org/10.1109/ACCESS.2021.3087109
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