Revolutionizing Security Information and Event Management (SIEM) Systems: Harnessing Deep Learning for Advanced Threat Detection

This chapter explores various deep learning methods for enhancing Security Information and Event Management (SIEM) systems. As cyber threats become increasingly sophisticated, traditional SIEM approaches often fall short in efficiently processing and analyzing vast amounts of security data. We investigate the application of deep learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, to improve threat detection, anomaly detection, and incident response capabilities. CNNs are leveraged for feature extraction from complex datasets, enabling the identification of intricate patterns in security events. RNNs are utilized for sequential data analysis, effectively capturing temporal dependencies in attack vectors.