Automating Privilege Escalation with Deep Reinforcement Learning

Kalle Kujanpää, Willie Victor, Alexander Ilin

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine learning-based defenses is a significant practical challenge. An intelligent red teaming agent capable of performing realistic attacks can alleviate this problem. However, there is little scientific evidence demonstrating the feasibility of fully automated attacks using machine learning. In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our results show that the autonomous agent can escalate privileges in a Windows 7 environment using a wide variety of different techniques depending on the environment configuration it encounters. Hence, our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
AlkuperäiskieliEnglanti
OtsikkoAISec 2021 - Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2021
ToimittajatNicholas Carlini, Ambra Demontis, Yizheng Chen
JulkaisupaikkaNew York
KustantajaACM
Sivut157-168
Sivumäärä12
ISBN (elektroninen)9781450386579
DOI - pysyväislinkit
TilaJulkaistu - 15 marrask. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM Workshop on Artificial Intelligence and Security - Virtual, Online, Etelä-Korea
Kesto: 15 marrask. 202115 marrask. 2021

Workshop

WorkshopACM Workshop on Artificial Intelligence and Security
Maa/AlueEtelä-Korea
KaupunkiVirtual, Online
Ajanjakso15/11/202115/11/2021

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