Detection of water-bed cyber-attacks using artificial neural networks

Cyber threat intelligence gathered from previous erattacks may provide light on the methods and tools employed by cybercriminals, which can aid in both the reconstruction of assaults and the prediction of their future trajectory. Consequently, to mitigate these consequences, cyber security analysts use threat intelligence, alert correlations, machine learning, and enhanced visualisations. The impact and development of machine learning algorithm are booming in the current scenario. These algorithm are working perfectly by identifying the pattern based on instance based or model based learning. Well the model based learning is more efficient then the instance based learning. Because the instance based learning identify the pattern by memorising its pattern where the model based training try to form a boundary between these and try to classify them. In addition to the utilization of machine learning algorithms, the integration of advanced analytics techniques such as anomaly detection and behavior analysis further fortifies cyber defense mechanisms. Anomaly detection algorithms sift through vast datasets to identify deviations from normal behavior, flagging potentially malicious activities that might evade traditional rule-based systems. Meanwhile, behavior analysis algorithms scrutinize user actions and system interactions in real-time, discerning subtle deviations indicative of suspicious or unauthorized behavior.