Anomaly detection in network security systems using machine learning
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Published:
Jun 30, 2024
Keywords:
Algoritma Naïve Bayes, Anomaly Detection, Cross-Validation, Machine Learning, Network Security
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Abstract
Anomaly Detection in Network Security Systems Using Machine Learning highlights the importance of developing effective models for data security. This research aims to develop an adaptive and automated anomaly detection model using the Naive Bayes algorithm and cross-validation. The methodology applied includes security log data collection, data preprocessing, implementation of Naive Bayes algorithms, and model evaluation using metrics such as accuracy, precision, recall, and F1-score. The results showed that the developed model was able to achieve high accuracy in detecting anomalies, with significant performance in identifying real threats without negative errors. The implication of this research is the improvement of network security through the application of machine learning, providing practical solutions for practitioners to deal with increasingly complex cybersecurity challenges
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How to Cite
Santoso, N. A., Lutfayza, R., Nughroho, B. I. ., & Gunawan, G. . (2024). Anomaly detection in network security systems using machine learning. Journal of Intelligent Decision Support System (IDSS), 7(2), 113-120. https://doi.org/10.35335/idss.v7i2.238
References
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Wang, C., & Zhu, H. (2022). Wrongdoing monitor: A graph-based behavioral anomaly detection in cyber security. IEEE Transactions on Information Forensics and Security, 17, 2703–2718.
Wu, H., Han, H., Wang, X., & Sun, S. (2020). Research on artificial intelligence enhancing internet of things security: A survey. Ieee Access, 8, 153826–153848. https://doi.org/https://doi.org/10.1109/ACCESS.2020.3018170Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020). A machine learning security framework for iot systems. IEEE Access, 8, 114066–114077. https://doi.org/https://doi.org/10.1109/ACCESS.2020.2996214
Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. https://doi.org/https://doi.org/10.1016/j.knosys.2019.105361
Evangelou, M., & Adams, N. M. (2020). An anomaly detection framework for cyber-security data. Computers & Security, 97, 101941. https://doi.org/https://doi.org/10.1016/j.cose.2020.101941
Gunduz, M. Z., & Das, R. (2020). Cyber-security on smart grid: Threats and potential solutions. Computer Networks, 169, 107094. https://doi.org/https://doi.org/10.1016/j.comnet.2019.107094
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/https://doi.org/10.1109/TDSC.2021.3050101
Tama, B. A., & Lim, S. (2021). Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation. Computer Science Review, 39, 100357. https://doi.org/https://doi.org/10.1016/j.cosrev.2020.100357
DeGraba, T. J., Williams, K., Koffman, R., Bell, J. L., Pettit, W., Kelly, J. P., Dittmer, T. A., Nussbaum, G., Grammer, G., & Bleiberg, J. (2021). Efficacy of an interdisciplinary intensive outpatient program in treating combat-related traumatic brain injury and psychological health conditions. Frontiers in Neurology, 11, 580182. https://doi.org/https://doi.org/10.3389/fneur.2020.580182
Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., & Liotta, A. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67, 64–79. https://doi.org/https://doi.org/10.1016/j.inffus.2020.10.001
Javaheri, D., Gorgin, S., Lee, J. A., & Masdari, M. (2023). Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: Classification, overview, and future perspectives. Information Sciences, 626, 315–338. https://doi.org/10.1016/j.ins.2023.01.067
Lee, M., Kwon, W., & Back, K.-J. (2021). Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management, 33(6), 2117–2136. https://doi.org/https://doi.org/10.1108/IJCHM-06-2020-0587
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., & Liu, H. (2020). A review of android malware detection approaches based on machine learning. IEEE Access, 8, 124579–124607. https://doi.org/https://doi.org/10.1109/ACCESS.2020.3006143
Martins, I., Resende, J. S., Sousa, P. R., Silva, S., Antunes, L., & Gama, J. (2022). Host-based IDS: A review and open issues of an anomaly detection system in IoT. Future Generation Computer Systems, 133, 95–113. https://doi.org/https://doi.org/10.1016/j.future.2022.03.001
Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023). Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions. IEEE Communications Surveys & Tutorials. https://doi.org/https://doi.org/10.1109/COMST.2023.3280465
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. Ieee Access, 9, 78658–78700. https://doi.org/https://doi.org/10.1109/ACCESS.2021.3083060
Pang, G., Shen, C., Cao, L., & Hengel, A. Van Den. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR), 54(2), 1–38. https://doi.org/https://doi.org/10.1145/3439950
Safitra, M. F., Lubis, M., & Fakhrurroja, H. (2023). Counterattacking cyber threats: A framework for the future of cybersecurity. Sustainability, 15(18), 13369. https://doi.org/https://doi.org/10.3390/su151813369
Sarker, I. H. (2021). CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things, 14, 100393. https://doi.org/https://doi.org/10.1016/j.iot.2021.100393
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterprise Information Systems, 16(10–11), 1694–1710.
Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310–222354. https://doi.org/https://doi.org/10.1109/ACCESS.2020.3041951
Trim, P. R. J., & Lee, Y. I. (2021). The global cyber security model: Counteracting cyber attacks through a resilient partnership arrangement. Big Data and Cognitive Computing, 5(3). https://doi.org/10.3390/bdcc5030032
Uzun, B., & Ballı, S. (2022). A novel method for intrusion detection in computer networks by identifying multivariate outliers and ReliefF feature selection. Neural Computing and Applications, 34(20), 17647–17662. https://doi.org/https://doi.org/10.1007/s00521-022-07402-2
Wang, C., & Zhu, H. (2022). Wrongdoing monitor: A graph-based behavioral anomaly detection in cyber security. IEEE Transactions on Information Forensics and Security, 17, 2703–2718.
Wu, H., Han, H., Wang, X., & Sun, S. (2020). Research on artificial intelligence enhancing internet of things security: A survey. Ieee Access, 8, 153826–153848. https://doi.org/https://doi.org/10.1109/ACCESS.2020.3018170Bagaa, M., Taleb, T., Bernabe, J. B., & Skarmeta, A. (2020). A machine learning security framework for iot systems. IEEE Access, 8, 114066–114077. https://doi.org/https://doi.org/10.1109/ACCESS.2020.2996214
Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. https://doi.org/https://doi.org/10.1016/j.knosys.2019.105361
Evangelou, M., & Adams, N. M. (2020). An anomaly detection framework for cyber-security data. Computers & Security, 97, 101941. https://doi.org/https://doi.org/10.1016/j.cose.2020.101941
Gunduz, M. Z., & Das, R. (2020). Cyber-security on smart grid: Threats and potential solutions. Computer Networks, 169, 107094. https://doi.org/https://doi.org/10.1016/j.comnet.2019.107094
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/https://doi.org/10.1109/TDSC.2021.3050101
Tama, B. A., & Lim, S. (2021). Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation. Computer Science Review, 39, 100357. https://doi.org/https://doi.org/10.1016/j.cosrev.2020.100357
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