Reinforcement learning for efficient network penetration testing
Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-stand...
Main Authors: | Ghanem, Mohamed Chahine, Chen, Thomas |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI
2020
|
Subjects: | |
Online Access: | https://repository.londonmet.ac.uk/7958/1/information-11-00006-v2.pdf |
Similar Items
-
Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks
by: Ghanem, Mohamed Chahine, et al.
Published: (2022) -
SQL injection detection and exploitation framework for penetration testing
by: Kazmi, Muhammad Ali Naqi
Published: (2019) -
A novel reinforcement learning model for post-incident malware investigations
by: Dunsin, Dipo, et al.
Published: (2025) -
ESASCF: expertise extraction, generalization and reply framework for optimized automation of network security compliance
by: Ghanem, Mohamed Chahine, et al.
Published: (2023) -
Automation of digital crime investigation using Reinforcement Learning (RL)
by: Ghanem, Mohamed Chahine
Published: (2023)