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AI-Powered Security Information and Event Management: A Review of Deep Learning Approaches for Modern Cybersecurity
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1712992
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).
How to cite this paper
@article{1712992,
author = {Sufia Begum D, Chinmaye D M, Poorna shree P, Nandana C K, Nithish Kumar K S},
title = {AI-Powered Security Information and Event Management: A Review of Deep Learning Approaches for Modern Cybersecurity},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {1313-1318},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712992.pdf},
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).},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712992}
}