TY - GEN
T1 - MEGA-PT
T2 - 15th International Conference on Decision and Game Theory for Security, GameSec 2024
AU - Ge, Yunfei
AU - Zhu, Quanyan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Penetration testing is an essential means of proactive defense in the face of escalating cybersecurity incidents. Traditional manual penetration testing methods are time-consuming, resource-intensive, and prone to human errors. Current trends in automated penetration testing are also impractical, facing significant challenges such as the curse of dimensionality, scalability issues, and lack of adaptability to network changes. To address these issues, we propose MEGA-PT, a meta-game penetration testing framework, featuring micro tactic games for node-level local interactions and a macro strategy process for network-wide attack chains. The micro- and macro-level modeling enables distributed, adaptive, collaborative, and fast penetration testing. MEGA-PT offers agile solutions for various security schemes, including optimal local penetration plans, purple teaming solutions, and risk assessment, providing fundamental principles to guide future automated penetration testing. Our experiments demonstrate the effectiveness and agility of our model by providing improved defense strategies and adaptability to changes at both local and network levels.
AB - Penetration testing is an essential means of proactive defense in the face of escalating cybersecurity incidents. Traditional manual penetration testing methods are time-consuming, resource-intensive, and prone to human errors. Current trends in automated penetration testing are also impractical, facing significant challenges such as the curse of dimensionality, scalability issues, and lack of adaptability to network changes. To address these issues, we propose MEGA-PT, a meta-game penetration testing framework, featuring micro tactic games for node-level local interactions and a macro strategy process for network-wide attack chains. The micro- and macro-level modeling enables distributed, adaptive, collaborative, and fast penetration testing. MEGA-PT offers agile solutions for various security schemes, including optimal local penetration plans, purple teaming solutions, and risk assessment, providing fundamental principles to guide future automated penetration testing. Our experiments demonstrate the effectiveness and agility of our model by providing improved defense strategies and adaptability to changes at both local and network levels.
KW - Agile Defense
KW - Cyber Risk Assessment
KW - Cyber Security
KW - Meta-Game
KW - Penetration Testing
UR - http://www.scopus.com/inward/record.url?scp=85207651766&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85207651766&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-74835-6_2
DO - 10.1007/978-3-031-74835-6_2
M3 - Conference contribution
AN - SCOPUS:85207651766
SN - 9783031748349
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 24
EP - 44
BT - Decision and Game Theory for Security - 15th International Conference, GameSec 2024, Proceedings
A2 - Sinha, Arunesh
A2 - Fu, Jie
A2 - Zhu, Quanyan
A2 - Zhang, Tao
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 16 October 2024 through 18 October 2024
ER -