Smart Detection of Cyber Threats: A Machine Learning Approach to Real-Time Network Traffic Analysis

In today’s digital age, cybersecurity has become a necessity as organizations increasingly rely on networks to transmit sensitive information. Protecting these networks from malicious attacks is critical, yet traditional rule-based intrusion detection systems (IDS) are insufficient for detecting new or sophisticated intrusions. These systems rely on predefined attack signatures, which limit their ability to recognize evolving threats. This project aims to overcome these limitations by utilizing machine learning to detect anomalies in network traffic. By analysing real-time network data, the proposed system identifies deviations from normal behaviour, allowing it to recognize both known and unknown cyber threats with a high level of accuracy. This approach enhances network security and provides an adaptable solution to modern cybersecurity challenges.