MACHINE LEARNING TECHNIQUES FOR NETWORK ANOMALY DETECTION IN ENTERPRISE NETWORKS
Keywords:
machine learning, network anomalies, cybersecurity, artificial intelligence, network traffic, attack detection, enterprise networks, data analysis, anomaly detection, network security.Abstract
Today, the growing volume of data in enterprise networks and the increasing sophistication of cyberattacks make network security a critical challenge. This article explores the potential of machine learning techniques for detecting anomalies in enterprise networks. The limitations of traditional security solutions are discussed, and the advantages of artificial intelligence technologies in analyzing network traffic and identifying suspicious activities are highlighted. In addition, the paper examines the role of machine learning algorithms in the early detection of cyber threats and their contribution to improving overall network security.
References
Ahmed, M., Mahmood, A. N., & Hu, J. (2016). *A survey of network anomaly detection techniques*. Journal of Network and Computer Applications, 60, 19–31.
Chandola, V., Banerjee, A., & Kumar, V. (2009). *Anomaly detection: A survey*. ACM Computing Surveys, 41(3), 1–58.
Sommers, J., & Barford, P. (2004). *Anomaly detection in dynamic network traffic*. ACM SIGCOMM Internet Measurement Workshop.
Garcia-Teodoro, P., Diaz-Verdejo, J. E., Maciá-Fernández, G., & Vázquez, E. (2009). *Anomaly-based network intrusion detection: Techniques, systems and challenges*. Computers & Security, 28(1–2), 18–28.
Liao, H. J., Lin, C. H. R., Lin, Y. C., & Tung, K. Y. (2013). *Intrusion detection system: A comprehensive review*. Journal of Network and Computer Applications, 36(1), 16–24.
Moustafa, N., & Slay, J. (2015). *UNSW-NB15: A comprehensive data set for network intrusion detection systems*. Military Communications and Information Systems Conference (MilCIS).
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). *A detailed analysis of the KDD CUP 99 data set*. IEEE Symposium on Computational Intelligence for Security and Defense Applications.
Buczak, A. L., & Guven, E. (2016). *A survey of data mining and machine learning methods for cybersecurity intrusion detection*. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.
Somani, G., et al. (2017). *A survey on intrusion detection systems in cloud computing*. Journal of Network and Computer Applications.
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