A Next-Generation Deep Learning-Powered Security Operations Center (SOC) for Online Learning Platforms

DOI

10.2991/978-94-6463-360-3_29How to use a DOI?

Keywords

Online learning platforms; Security Operations Center (SOC); Cybersecurity; Deep Learning

Abstract

The digital landscape is experiencing a concerning surge in online threats, with their associated risks growing exponentially, especially within the realm of online learning platforms. These threats span a broad spectrum, ranging from sophisticated cyberattacks to pervasive phishing campaigns and insidious malware. In an era dominated by digitalization, no entity operating within online learning environments is immune to the reach of these threats. Recognizing the urgency of the situation is crucial, as inaction in the face of this escalating danger could have severe consequences, impacting not only the economy but also the privacy and security of individuals engaged in online learning. Effectively addressing the mounting threat landscape requires a multifaceted approach. Solutions must encompass advanced cybersecurity measures, robust threat intelligence, proactive risk management, and comprehensive awareness and training initiatives tailored to online education platforms. This article introduces an innovative approach to enhance Security Operations Centers (SOCs) specifically within the context of online learning platforms, harnessing the power of deep learning. By integrating cutting-edge deep learning techniques into the SOC framework, the goal is to significantly enhance the capability to detect, analyze, and respond to complex cybersecurity threats within the unique challenges of online education. This groundbreaking model leverages artificial intelligence to provide real-time threat intelligence, predictive analysis, and anomaly detection, thereby reinforcing the security posture of online learning platforms and fortifying their defenses against a rapidly evolving threat landscape.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ali Radid
AU  - Mohamed Ghazouani
AU  - Benlahmar Habib
PY  - 2024
DA  - 2024/02/05
TI  - A Next-Generation Deep Learning-Powered Security Operations Center (SOC) for Online Learning Platforms
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023)
PB  - Atlantis Press
SP  - 282
EP  - 292
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-360-3_29
DO  - 10.2991/978-94-6463-360-3_29
ID  - Radid2024
ER  -