Su Zhang, Ph.D.

Su Zhang, Ph.D.

San Francisco Bay Area
15K followers 500+ connections

About

Su is a cybersecurity leader driving the future of autonomous vehicle security.

As…

Activity

15K followers

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Experience

  • Tensor Auto Graphic

    Tensor Auto

    San Francisco Bay Area

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    San Francisco Bay Area

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    San Francisco Bay Area

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    San Francisco Bay Area

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    San Francisco Bay Area

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    San Francisco Bay Area

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    Manhattan, Kansas

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    San Francisco Bay Area

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    Palo Alto, California

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    Palo Alto, California

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Education

  • Kansas State University Graphic

    Kansas State University

    3.9/4.0

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    Ph.D Dissertation: Quantitative Risk Assessment under Multi-Context Environment

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Publications

  • Assessing Attack Surface with Component-based Package Dependency

    in Proceedings of the 9th International Conference on Network and System Security(NSS 15)

    The very first work regarding quantitative risk assessment of software component dependency.

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  • After we knew it: empirical study and modeling of cost-effectiveness of exploiting prevalent known vulnerabilities across IaaS cloud

    in Proceedings of the 9th ACM symposium on Information, computer and communications security (ASIACCS 14)

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  • Investigating the application of moving target defenses to network security

    In 6th International Symposium on Resilient Control Systems (ISRCS)

    This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker’s asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network’s configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD…

    This paper presents a preliminary design for a moving-target defense (MTD) for computer networks to combat an attacker’s asymmetric advantage. The MTD system reasons over a set of abstract models that capture the network’s configuration and its operational and security goals to select adaptations that maintain the operational integrity of the network. The paper examines both a simple (purely random) MTD system as well as an intelligent MTD system that uses attack indicators to augment adaptation selection. A set of simulation-based experiments show that such an MTD system may in fact be able to reduce an attacker’s success likelihood. These results are a preliminary step towards understanding and quantifying the impact of MTDs on computer networks.

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  • An Empirical Study of a Vulnerability Metric Aggregation Method

    in Proceedings of the 2011 International Conference on Security and Management (SAM 11)

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  • Effective Network Vulnerability Assessment through Model Abstraction

    in Proceedings of the Eighth Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 11)

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  • Empirical study on using the National Vulnerability Database to predict software vulnerability

    in Proceedings of the 22nd International Conference on Database and Expert Systems Applications (DEXA 11) ,

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