COMBINING MACHINE LEARNING AND NETWORK TRAFFIC ANALYSIS FOR CYBER ATTACK PREVENTION AND CYBERSECURITY ASSURANCE
Keywords
DOI:
https://doi.org/10.34238/tnu-jst.15266Abstract
With the rapid development of information technology and network environments, cyberattacks are becoming increasingly complex and sophisticated, posing major challenges to cybersecurity. Traditional signature-based defense methods are no longer adequate for detecting and preventing new threats. The combination of machine learning and network traffic analysis opens a new approach, allowing systems to automatically learn and identify abnormal behaviors in networks. This paper presents an integrated framework combining machine learning algorithms with network traffic analysis to enhance the capability of detecting and preventing cyberattacks. Experimental results on the CICIDS2017 dataset show that the Random Forest model achieves an accuracy of 99.28%, and deep learning models demonstrate advantages in detecting zero-day attacks. The proposed framework can be deployed in real-world IDS/IPS systems to proactively and effectively enhance cybersecurity, while also improving detection speed and system adaptability in dynamic network environments.Downloads
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