Mu Zhu
College of Engineering
Works (5)
Updated: April 5th, 2024 14:43
2023 article
Deception in Drone Surveillance Missions: Strategic vs. Learning Approaches
PROCEEDINGS OF THE 2023 INTERNATIONAL SYMPOSIUM ON THEORY, ALGORITHMIC FOUNDATIONS, AND PROTOCOL DESIGN FOR MOBILE NETWORKS AND MOBILE COMPUTING, MOBIHOC 2023, pp. 382–387.
author keywords: Honey drone; defensive deception; unmanned aerial vehicle; mission effectiveness; game theory; deep reinforcement learning
TL;DR:
This work proposes a unique proactive defense using honey drones (HD) for UAVs during surveillance operations to identify the optimal setting using deep reinforcement learning (DRL) or game theory and compares their performance with that of non-HD-based methods.
(via Semantic Scholar)
dl.acm.org (publisher PDF)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 11, 2023
2023 journal article
Resisting Multiple Advanced Persistent Threats via Hypergame-Theoretic Defensive Deception
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 20(3), 3816–3830.
author keywords: Defensive deception; hypergame theory; expected utility; beliefs; advanced persistent threat; Hyper Nash Equilibrium
TL;DR:
An attack-defense hypergame where multiple advanced persistent threat attackers and a single defender play a repeated game with different perceptions, and the DD strategies showed their highest advantages when the hypergame and machine learning are considered in terms of reduced false positives and negatives of the NIDS, system lifetime, and players’ perceived uncertainties and utilities.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 8, 2023
2022 journal article
Foureye: Defensive Deception Against Advanced Persistent Threats via Hypergame Theory
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 19(1), 112–129.
author keywords: Games; Uncertainty; Nash equilibrium; Analytical models; Stochastic processes; Reconnaissance; Predictive models; Defensive deception; hypergame theory; uncertainty; attacker; defender; advanced persistent threat
TL;DR:
This work forms a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs, which gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker's decision-making.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions
(OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 11, 2022
2022 article
Honeypot-Based Cyber Deception Against Malicious Reconnaissance via Hypergame Theory
2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), pp. 3393–3398.
TL;DR:
A two-player hypergame model that characterizes how a defender should deploy low and high-interaction honeypots to defend the network against malicious reconnaissance activities is presented and the numerical results validate the effectiveness of the proposed honeypot system.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: May 1, 2023
2021 journal article
A Survey of Defensive Deception: Approaches Using Game Theory and Machine Learning
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 23(4), 2460–2493.
author keywords: Games; Tutorials; Taxonomy; Computer security; Planning; Monitoring; Measurement; Defensive deception; cybersecurity; game theory; machine learning
TL;DR:
This survey focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception.
(via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 20, 2021
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