Advanced Analytics for Proactive Cybersecurity Breach Prediction and Prevention
J Pathmanaban, S. Sangeetha, A Harini, S Pavithra, P Hemasri · 2024
As technology advances, so do the potential risks and security threats associated with it. It’s crucial to prioritize and invest in cybersecurity measures to safeguard these advancements. Balancing innovation with robust security measures is essential for staying proactive and implementing security protocols by this we can mitigate the risks and ensure a safer digital future. This project focuses on the development of a predictive model for cyber-attack detection on servers. Leveraging advanced analytics and machine learning techniques, the system aims to proactively identify potential security breaches before they manifest. This involves the analysis of server logs, network traffic, and anomaly behaviour to create a robust predictive model. Various types of Cyberattacks which include DDOS, PROBE, U2R, and R2L attacks. In this, we use the RNN algorithm for classification and training in order to improve the efficiency of IDS detection.