Detection and Prevention of Malicious Activities in Network Traffic using Machine Learning Techniques
Keywords:
Network Security, Machine Learning, Deep Learning, Intrusion Detection Systems, Malicious Traffic Detection, Convolutional Neural Networks, LSTM, Cybersecurity, Threat Intelligence, Network Traffic AnalysisAbstract
The exponential growth of network infrastructure and increasing sophistication of cyber threatshave made malicious traffic detection a critical priority for organizations worldwide. Traditionalsignature-based detection methods are increasingly inadequate against modern, evolving attackpatterns that leverage advanced techniques
References
Ajmal, M., Ahmadi, F., Ali, S., et al. (2024). Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning. PMC. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC11175201/
Apruzzese, G., Laskov, P., Schneider, J. (2024). Machine Learning-Based Methodologies for Cyber-Attacks and Network Traffic Monitoring: A Review and Insights. MDPI Information, 15(11), 741. Retrieved from https://www.mdpi.com/2078-2489/15/11/741
