Abstract
Finding a method to quickly and efficiently identify online hazards is one of the most crucial issues in the world of cybersecurity. This study demonstrates a novel approach to identifying internet threats using artificial intelligence and artificial neural networks. The suggested method significantly increases the ability to identify cyber threats by breaking down a huge number of security events into individual event profiles and using a deep learning-based detection mechanism. AI-SIEM (Artificial Intelligence Security Information and Event Management) has been developed as a means of achieving this. This system integrates multiple artificial neural network methods, such as Fully Connected Neural Networks (FCNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, with event profiling for data preparation.
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