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B. Biggio, g. fumera, P. Russu, L. Didaci, F. Roli.  2015.  Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective. IEEE Signal Processing Magazine. 32:31-41.

In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization of known and novel vulnerabilities of biometric recognition systems, along with the corresponding attacks, countermeasures, and defense mechanisms. We report two application examples, respectively showing how to fabricate a more effective face spoofing attack, and how to counter an attack that exploits an unknown vulnerability of an adaptive face-recognition system to compromise its face templates.

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C. Wang, Z. Lu.  2018.  Cyber Deception: Overview and the Road Ahead. IEEE Security Privacy. 16:80-85.

Since the concept of deception for cybersecurity was introduced decades ago, several primitive systems, such as honeypots, have been attempted. More recently, research on adaptive cyber defense techniques has gained momentum. The new research interests in this area motivate us to provide a high-level overview of cyber deception. We analyze potential strategies of cyber deception and its unique aspects. We discuss the research challenges of creating effective cyber deception-based techniques and identify future research directions.

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E. Peterson.  2016.  Dagger: Modeling and visualization for mission impact situation awareness. MILCOM 2016 - 2016 IEEE Military Communications Conference. :25-30.

Dagger is a modeling and visualization framework that addresses the challenge of representing knowledge and information for decision-makers, enabling them to better comprehend the operational context of network security data. It allows users to answer critical questions such as “Given that I care about mission X, is there any reason I should be worried about what is going on in cyberspace?” or “If this system fails, will I still be able to accomplish my mission?”.

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N. Soule, B. Simidchieva, F. Yaman, R. Watro, J. Loyall, M. Atighetchi, M. Carvalho, D. Last, D. Myers, B. Flatley.  2015.  Quantifying & minimizing attack surfaces containing moving target defenses. 2015 Resilience Week (RWS). :1-6.

The cyber security exposure of resilient systems is frequently described as an attack surface. A larger surface area indicates increased exposure to threats and a higher risk of compromise. Ad-hoc addition of dynamic proactive defenses to distributed systems may inadvertently increase the attack surface. This can lead to cyber friendly fire, a condition in which adding superfluous or incorrectly configured cyber defenses unintentionally reduces security and harms mission effectiveness. Examples of cyber friendly fire include defenses which themselves expose vulnerabilities (e.g., through an unsecured admin tool), unknown interaction effects between existing and new defenses causing brittleness or unavailability, and new defenses which may provide security benefits, but cause a significant performance impact leading to mission failure through timeliness violations. This paper describes a prototype service capability for creating semantic models of attack surfaces and using those models to (1) automatically quantify and compare cost and security metrics across multiple surfaces, covering both system and defense aspects, and (2) automatically identify opportunities for minimizing attack surfaces, e.g., by removing interactions that are not required for successful mission execution.