Mu Zhu

College of Engineering

Works (6)

Updated: December 13th, 2024 05:01

2024 journal article

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 24(4).

By: Z. Wan *, J. Cho *, M. Zhu n, A. Anwar, C. Kamhoua * & M. Singh n 

author keywords: Defensive deception; deep reinforcement learning; unmanned aerial; vehicle; mission effectiveness
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 25, 2024

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.

By: Z. Wan *, J. Cho *, M. Zhu n, A. Anwar*, C. Kamhoua * & M. Singh n 

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)
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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.

By: Z. Wan *, J. Cho *, M. Zhu n, A. Anwar*, C. Kamhoua * & M. Singh n 

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.

By: Z. Wan *, J. Cho *, M. Zhu n, A. Anwar*, C. Kamhoua * & M. Singh n 

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)
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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.

By: A. Anwar*, M. Zhu n, Z. Wan *, J. Cho *, C. Kamhoua * & M. Singh n 

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 Goals Color Wheel
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.

By: M. Zhu n, A. Anwar*, Z. Wan *, J. Cho *, C. Kamhoua * & M. Singh n 

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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