Artificial Intelligence based Security Information and Event Management

Finding an automated method for detecting cyber-attacks is one of the biggest problems in cybersecurity.In this study, we describe an artificial intelligence (AI) method based on artificial neural networks for detecting cyberthreats.The suggested solution uses a deep learning-based detection method to improve cyber-threat identification by breaking down a large volume of recorded security events into individual event profiles.For this project, we created an AI-SIEM system that combines event profiling for data pre-processing with various artificial neural network techniques, such as FCNN, CNN, and LSTM.The system has a strong emphasis on separating true positive warnings from false positive alerts, assisting security analysts in quickly responding to cyber threats.All experiments in this paper on two benchmark datasets (NSLKDD and CICIDS2017) as well as two datasets that were gathered in the real world.We conducted trials utilizing the five traditional machine-learning methods (SVM, k-NN, RF, NB, and DT) to assess the performance comparison with existing approaches.The experimental findings of this study confirm that our proposed methods can be used as learning-based models for network intrusion detection and demonstrate that, when applied in the real world, they outperform traditional machine-learning techniques.