Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Abstract: Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek for developing proactive, adaptive MTD mechanisms.

• The paper presents a taxonomy that categorizes MTD techniques by what, how, and when to move system attributes.
• The paper leverages methodologies such as game theory and machine learning to model attacker–defender dynamics in adaptive defenses.
• The paper addresses challenges in scalability and integration, advocating combined strategies to balance performance and robust security.

Overview of "Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense"

The paper "Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense" provides a comprehensive exploration of Moving Target Defense (MTD), a cybersecurity strategy that aims to improve network and system resilience by dynamically changing attack surfaces. This proactive approach counters the limitations of traditional static defense mechanisms, which typically react to intrusions post-detection.

Key Elements and Classifications of MTD

The paper outlines MTD's fundamental principles, distinguishing MTD techniques based on vital questions: what to move, how to move, and when to move. It presents a taxonomy of MTD approaches:

• Shuffling: Rearranges system attributes such as IP addresses and network paths to confuse adversaries.
• Diversity: Involves using different software or system configurations to prevent attackers from easily predicting system weaknesses.
• Redundancy: Implements multiple system components to ensure service availability even when some components are under attack.

Application Domains and Methodologies

MTD strategies have been applied across various domains, including Cloud computing, Internet-of-Things (IoT), Cyber-Physical Systems (CPS), and Software-Defined Networks (SDNs). The survey highlights that each domain presents unique challenges and opportunities for MTD deployment. For instance, in Cloud environments, MTD can mitigate risks by employing VM migrations and diverse software stacks, while IoT requires lightweight approaches due to resource constraints.

The paper categorizes existing MTD approaches based on theoretical foundations such as game theory, machine learning, and genetic algorithms. These methodologies facilitate the modeling of interactions between attackers and defenders, allowing for the development of dynamic and adaptive defense mechanisms.

Effectiveness and Evaluation of MTD

The effectiveness of MTD strategies is typically measured through:

• Attack Success Probability (ASP): The likelihood that an attack successfully compromises a target;
• Defense Success Probability (DSP): Measures a defender's ability to thwart adversarial activities.

The paper discusses evaluation methodologies including analytical models, simulations, emulations, and real testbeds, each offering varying levels of abstraction and empirical validation.

Challenges and Future Directions

Despite the promise of MTD, the paper recognizes several challenges:

• Scalability: Managing the overhead associated with frequent system reconfigurations;
• Complexity: Integrating MTD with existing static defenses and ensuring minimal impact on legitimate users;
• Metrics: Developing comprehensive metrics that capture the trade-offs between security effectiveness, system performance, and defense costs.

The authors advocate for research into more adaptive MTD mechanisms that can intelligently balance these trade-offs, emphasizing the potential benefits of combining multiple MTD strategies to enhance system security and resilience.

Conclusion

This survey serves as an extensive resource on the state of MTD research, presenting insights into technological trends, strategic methodologies, and practical challenges. It underscores the critical role of MTD in revolutionizing cybersecurity by shifting from reactive to proactive defense, ultimately aiming for a more secure and resilient cyber environment.