Dynamic Application Rotation Environment for Moving Target Defense

Thompson, Michael L.; Mendolla, Marilyne; Muggler, Michael; Ike, Moses

doi:10.1109/rweek.2016.7573301

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…It includes changing the software version or altering the program binary code. A textbook example is the Dynamic Application Rotation Environment (DARE) [19]. In this framework, a web interface can have two web servers (Nginx and Apache) that repeatedly take over each other, making the attacker confused about the current webserver.…”

Section: Choice Of the Mpmentioning

confidence: 99%

Reasoning about Moving Target Defense in Attack Modeling Formalisms

Ballot

Malvone

Leneutre

et al. 2022

Proceedings of the 9th ACM Workshop on Moving Target Defense

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Since 2009, Moving Target Defense (MTD) has become a new paradigm of defensive mechanism that frequently changes the state of the target system to confuse the attacker. This frequent change is costly and leads to a tradeoff between misleading the attacker and disrupting the quality of service. Optimizing the MTD activation frequency is necessary to develop this defense mechanism when facing realistic, multi-step attack scenarios. Attack modeling formalisms based on DAG are prominently used to specify these scenarios. It represents the attack goal in the root of a tree that is recursively refined into subgoals to show the different ways the attacker can compromise the system. According to some specific models, the tree is augmented with countermeasures, time, costs, or probabilities.Our contribution is a new DAG-based formalism for MTDs and its translation into a Price Timed Markov Decision Process to find the best activation frequencies against the attacker's time/cost-optimal strategies. For the first time, MTD activation frequencies are analyzed in a state-of-the-art DAG-based representation. Moreover, this is the first paper that considers the specificity of MTDs in the automatic analysis of attack modeling formalisms. Finally, we present some experimental results using UPPAAL STRATEGO to demonstrate its applicability and relevance.

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Section: Choice Of the Mpmentioning

confidence: 99%

Reasoning about Moving Target Defense in Attack Modeling Formalisms

Ballot

Malvone

Leneutre

et al. 2022

Proceedings of the 9th ACM Workshop on Moving Target Defense

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show abstract

“…A three-student team from UT Dallas, working together with Argonne National Laboratories, developed a Moving Target Defense (MTD) to protect against probing attacks on web servers [14]. At random bounded intervals between 15 and 60 seconds, the system switched the web server between Apache and Nginx.…”

Section: Example 1: Moving Target Defensementioning

confidence: 99%

“…In addition to the works presented at INSuRE-Con, INSuRE projects resulted in refereed conference publications (e.g., [1,2,4,9,10,14]), refereed posters (e.g., [12,13]), and published datasets (e.g., [6]).…”

Section: Outcomes and Lessons Learnedmentioning

confidence: 99%

The INSuRE Project: CAE-Rs Collaborate to Engage Students in Cybersecurity Research

Sherman,

Dark,

Chan

et al. 2017

Preprint

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Since fall 2012, several National Centers of Academic Excellence in Cyber Defense Research (CAE-Rs) fielded a collaborative course to engage students in solving applied cybersecurity research problems. We describe our experiences with this Information Security Research and Education (INSuRE) research collaborative. We explain how we conducted our project-based research course, give examples of student projects, and discuss the outcomes and lessons learned.

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“…The consequences of failure go beyond financial loss and could result in a breach of privacy or a denial of service, significantly harming the users. Moving Target Defense has established itself as a potential solution to combat threats observed in web applications [1,2], operating systems [3], cloud-based services [4,5], etc. Instead of maintaining a single configuration, analysts can maintain multiple configurations of the system and alternate between them.…”

Section: Introductionmentioning

confidence: 99%

Learning Effective Strategies for Moving Target Defense with Switching Costs

Viswanathan¹,

Bose²,

Paruchuri³

2023

Preprint

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Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker's motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.

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Dynamic Application Rotation Environment for Moving Target Defense

Cited by 15 publications

References 5 publications

Reasoning about Moving Target Defense in Attack Modeling Formalisms

Reasoning about Moving Target Defense in Attack Modeling Formalisms

The INSuRE Project: CAE-Rs Collaborate to Engage Students in Cybersecurity Research

Learning Effective Strategies for Moving Target Defense with Switching Costs

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