AI-Powered Security Information and Event Management: A Review of Deep Learning Approaches for Modern Cybersecurity

• Author(s): Sufia Begum D; Chinmaye D M; Poorna shree P; Nandana C K; Nithish Kumar K S
• Paper ID: 1712992
• Page: 1313-1318
• Published Date: 17-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

Abstract

Cyber threats are increasing with rapidly emerging digital infrastructure. In that scenario, even a reasonable pace cannot be maintained by traditional SIEM systems. Deep learning technologies such as Variational Autoencoders and Graph Neural Networks have to be embedded into SIEMbased systems so that anomaly activity might be detected and zero-day threats also identified with accurate and timely generation of the alerts. We critically compare recent advances in deep learning-based SIEM with their more practical applications in detecting complex cyberattacks and focus on how AI improves the effectiveness of the efficacy of reports from SIEMs and technical challenges associated with those methodologies.

Keywords

Deep Learning, Variational Autoencoder (VAE), Graph Neural Network (GNN), Anomaly Detection, Cybersecurity and Security Information and Event Management (SIEM).

Citations

IRE Journals:
Sufia Begum D, Chinmaye D M, Poorna shree P, Nandana C K, Nithish Kumar K S "AI-Powered Security Information and Event Management: A Review of Deep Learning Approaches for Modern Cybersecurity" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1313-1318 https://doi.org/10.64388/IREV9I6-1712992

IEEE:
Sufia Begum D, Chinmaye D M, Poorna shree P, Nandana C K, Nithish Kumar K S "AI-Powered Security Information and Event Management: A Review of Deep Learning Approaches for Modern Cybersecurity" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712992