A signaling game model for moving target defense

Feng, Xiaotao; Zheng, Zizhan; Cansever, Derya; Swami, Ananthram; Mohapatra, Prasant

doi:10.1109/infocom.2017.8057200

Cited by 43 publications

(32 citation statements)

References 20 publications

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“…Consequently, optimal defensive strategy is formulated. Feng et al[42] proposed Bayesian Stackelberg dynamic game model to select strategy in generalized network confrontation with MTD. By comparing hidden transformation and transformation with feedback signal, it verifies that MTD can increase defense benefit by transmitting wrong signal deliberately to mislead attackers.5.1.2.…”

mentioning

confidence: 99%

Moving Target Defense Techniques: A Survey

Cheng

Zhang

Jian-cheng

et al. 2018

Security and Communication Networks

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As an active defense technique to change asymmetry in cyberattack-defense confrontation, moving target defense research has become one of the hot spots. In order to gain better understanding of moving target defense, background knowledge and inspiration are expounded at first. Based on it, the concept of moving target defense is analyzed. Secondly, literature analysis method is adopted to explain the design principles and system architecture of moving target defense. In addition, some relevant key techniques are introduced from the aspects of strategy generation, shuffling implementation, and performance evaluation. After that, the applications of moving target defense in different network architectures are illustrated. Finally, existing problems and future trend in this field are elaborated so as to provide a basis for further study.

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mentioning

confidence: 99%

Moving Target Defense Techniques: A Survey

Cheng

Zhang

Jian-cheng

et al. 2018

Security and Communication Networks

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“…Through simulation, they showed that their game model can provide optimal defensive strategies for the VM which can effectively fail the co-resident attack. Feng et al [49] investigated how the strategies of information disclosure by defenders can improve the effectiveness of MTD techniques based on Bayesian Stackelberg game theory. They designed a signaling game based on the concept of the Bayesian Persuasion Model to consider how a defender signals an attacker and how the attacker responds to the signal in their decision making process.…”

Section: A Game Theory-based Mtdmentioning

confidence: 99%

“…The examples include platform migrations or system diversity [20,30,46,49], server location migrations [51,158], software stack diversity [161], proxy shuffling [83], or IP mutation [34]. Main Attacks: Most MTD approaches developed for enterprise networks countermeasured worm attacks [51], DDoS [51,83,158], abstracted attacks in an attack-defense game [49], scanning attacks [20,30,34], APT attacks [161], or more sophisticated, multi-stage attacks, including circumvention attacks, deputy attacks, entropy reducing attacks, probing attacks, and incremental attacks [46]. Key Methodologies: Since majority of existing MTD approaches have used game theoretic approaches, many MTD techniques for enterprise networks have used in an attackdefense game where the MTD techniques are used as defense strategies.…”

Section: A Enterprise Networkmentioning

confidence: 99%

“…Key Methodologies: Since majority of existing MTD approaches have used game theoretic approaches, many MTD techniques for enterprise networks have used in an attackdefense game where the MTD techniques are used as defense strategies. The examples include utility-concerned, incentive-compatible MTD [51], game theoretic approaches (Bayesian Stackelberg game [49], empirical game-theoretic analysis [158], moving target game [83]), or decision utility model [20]. Some MTD research focused on developing the assessment tools to measure the performance of MTD techniques, such as quantitative MTD assessment framework [161] or model-based probability models to estimate the effectiveness of MTD techniques [43,46].…”

Section: A Enterprise Networkmentioning

confidence: 99%

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

Cho

Sharma

Alavizadeh

et al. 2020

IEEE Commun. Surv. Tutorials

178

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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.

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“…Although they realized that security alerts play an important role in effective move selection, the cost of the moves was ignored. Feng et al [24] proposed a Bayesian Stackelberg game that models the joint migration and signaling strategies for the defender in the face of a strategic and rational attacker and demonstrated that MTD can be improved through strategic information disclosure. Markov Decision Process(MDP) based approach has been utilized to analyze and further select optimal policies by many researchers [25][26][27], while Lei et al [28] proposed a novel of incomplete information Markov game theoretic approach to strategy generation.…”

Section: Related Workmentioning

confidence: 99%

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

Zhou

Cheng

Jiang

et al. 2019

Proceedings of the 6th ACM Workshop on Moving Target Defense

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Moving Target Defense (MTD) has emerged as a newcomer into the asymmetric field of attack and defense, and shuffling-based MTD has been regarded as one of the most effective ways to mitigate DDoS attacks. However, previous work does not acknowledge that frequent shuffles would significantly intensify the overhead. MTD requires a quantitative measure to compare the cost and effectiveness of available adaptations and explore the best trade-off between them. In this paper, therefore, we propose a new costeffective shuffling method against DDoS attacks using MTD. By exploiting Multi-Objective Markov Decision Processes to model the interaction between the attacker and the defender, and designing a cost-effective shuffling algorithm, we study the best trade-off between the effectiveness and cost of shuffling in a given shuffling scenario. Finally, simulation and experimentation on an experimental software defined network (SDN) indicate that our approach imposes an acceptable shuffling overload and is effective in mitigating DDoS attacks. CCS CONCEPTS• Networks → Denial-of-service attacks; • Security and privacy → Formal security models.

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A signaling game model for moving target defense

Cited by 43 publications

References 20 publications

Moving Target Defense Techniques: A Survey

Moving Target Defense Techniques: A Survey

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

A Cost-effective Shuffling Method against DDoS Attacks using Moving Target Defense

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