PrivGuard: A Self-Learning Automated Adversarial Defense Framework for Intelligent Cybersecurity

DOI

10.2991/978-94-6239-789-7_43How to use a DOI?

Keywords

Artificial Intelligence; Cybersecurity; Reinforcement Learning; Machine Learning-Based Intrusion Detection; Automated Remediation; Anomaly Detection

Abstract

PrivGuard is an intelligent cybersecurity platform that is capable of identifying threats and is constantly on-the-fly to respond and upgrade itself. It consists of four main parts: a Red Team which is a creation of realistic attacks, a Blue Team monitoring and defending the systems from activity, an auto-remediation unit that will prevent dangerous activities and restore the system services and a learning loop which will use the registered data to improve the models. PrivGuard desires to create a more. Affordable, trustworthy and dependable defense platform using a mixture. of automation and machine learning. In the end, it is a more effective defense with less work. Auto remediation scripts instantly block malicius IPs and restore systums while the feedback loop keeps the models using stored data. This is indicating a shift towards autonomous, intelligent and self-learning web defense systems.

Copyright

© 2026 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  - Utkarsh Sharma
AU  - Umang Sharma
AU  - Vansh Sharma
AU  - Utkarsh Sharma
AU  - Mohd. Shahid
PY  - 2026
DA  - 2026/10/08
TI  - PrivGuard: A Self-Learning Automated Adversarial Defense Framework for Intelligent Cybersecurity
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 655
EP  - 665
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_43
DO  - 10.2991/978-94-6239-789-7_43
ID  - Sharma2026
ER  -