Advanced Techniques for Malicious Activity Detection in Network Traffic Data

The fast evolution of cyber threats enforces the need for advanced techniques in the detection of malicious activities over network traffic data. Traditional methods are commonly inadequate against increasingly sophisticated attacks and huge fluxes of network traffic. In the present paper, different state-of-the-art approaches will be reviewed to put forward better detection and mitigation of malicious activities. We will further examine the potential of machine learning and deep learning algorithms in the real-time identification of anomalies and intrusions. An explanation of the key role feature engineering and feature selection play in improving detection accuracy will also be provided. Further, we will investigate how big data analytics can be applied to efficiently handle and analyze large data volumes from network traffic. It does a comparative analysis to find better solutions among different tools for network traffic analysis. This study will, therefore, help bring out advanced techniques essential in building a comprehensive framework that foresees a robust and scalable system for the detection of activities of malicious intent, to instill firmer cybersecurity measures for crucial network infrastructures.