Automatic Method to Classify Cyber Crime Incident Using Artifical Intelligence and Deep Learning Approaches

Understanding the landscape The analysis of cyber incident data is vital to understanding the changing threat landscape. In times of continuing cyber-ruthlessness and numerous hacking events, the proposed work aims to explore the versatility of cyber-attacks and to devise counter weapons. Rather than assume these attacks are placed at random, we suggest that it is reasonable to model the interarrival times between hacking attacks and the number of accounts breached using stochastic processes that reflect the autocorrelation properties of these events. In order to handle the complexity of the data, we utilize deep learning algorithms, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). These approaches support solid analysis, unlocking latent patterns and trends in cyber events. By using these insights, the project will help us improve our understanding of cybersecurity while building a foundation for a set of proactive responses to developing threats.