Moving Target Defense

Moving Target Defense (MTD) is a cybersecurity strategy that dynamically alters a system's attack surface to hinder attackers. Current research emphasizes the use of reinforcement learning (RL) and Markov Decision Processes (MDPs) to optimize MTD strategies, often incorporating elements like federated learning for distributed systems and dynamic threat modeling to adapt to evolving attacks. This approach is particularly relevant for securing resource-constrained devices like those in the Internet of Things (IoT) and for mitigating adversarial attacks against machine learning models used in security applications. The effectiveness of MTDs, however, remains a subject of ongoing investigation, with research focusing on improving their resilience against sophisticated attack techniques and minimizing the performance overhead they introduce.

Papers

August 16, 2024

A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency
Megha Bose, Praveen Paruchuri, Akshat Kumar
Markov Decision Process Threat Model Cost Effective Moving Target Defense Space Attack

August 11, 2023

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez, Jan Kreischer, Jan von der Assen, Gerome Bovet, Gregorio Martinez Perez, Burkhard Stiller
Reinforcement Learning Federated Reinforcement Learning Zero Day Moving Target Defense Heterogeneous Zero Day Attack

July 21, 2023

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense
Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Decentralized Federated Learning Model Aggregation Threat Model Attack Method Security Challenge Moving Target Defense Network Security

February 1, 2023

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Attack Strategy Adversarial Machine Learning Based Malware Detection Moving Target Defense

December 30, 2022

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT
Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez, Jan von der Assen, Timo Schenk, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
Reinforcement Learning Human Like RL Zero Day Fingerprinting Lemma Attack Surface Moving Target Defense Heterogeneous Zero Day Attack

October 14, 2022

A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices
Jan von der Assen, Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez, Jordan Cedeño, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
Internet of Thing Internet of Thing Device Moving Target Defense Multi Purpose Malware

June 28, 2022

Reasoning about Moving Target Defense in Attack Modeling Formalisms
Gabriel Ballot, Vadim Malvone, Jean Leneutre, Etienne Borde
Step Attack DEfense Mechanism Attack Model DAG Learning Moving Target Defense

June 15, 2022

Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete
Adversarial Example Distribution Detection Adversarial Input Transferable Attack Evasion Attack Moving Target Defense

May 16, 2022

Moving Target Interception Considering Dynamic Environment
Chendi Qu, Jianping He, Jialun Li, Chongrong Fang, Yilin Mo
Dynamic Obstacle Wheeled Robot Simultaneous Interception Moving Target Defense

Moving Target Defense

Papers

A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT

A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices

Reasoning about Moving Target Defense in Attack Modeling Formalisms

Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection

Moving Target Interception Considering Dynamic Environment