Ryan Sheatsley, Matthew Durbin, Azaree Lintereur, and Patrick McDaniel, Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations.Berkay Celik, Patrick McDaniel, and Selcuk Uluagac, A Survey on IoT Platforms: Communication, Security, and Privacy Perspectives. Mingli Yu, Tian Xie, Ting He, Patrick McDaniel, and Quinn Burke, Flow Table Security in SDN: Adversarial Reconnaissance and Intelligent Attacks.SAE International Journal of Connected and Automated Vehicles, SAE International, June, 2021. Alejandro Andrade Salazar, Ryan Sheatsley, Jonathan Petit, and Patrick McDaniel, Physics-based Misbehavior Detection System for V2X Communications.Journal of Cyber Security Technology, Taylor & Francis, 5(3-4), 2021.
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Krych and Patrick McDaniel, Exposing Android Social Applications: Linking Data Leakage to Privacy Policies. Mobile Networks and Applications, Springer, 2021. Quinn Burke, Patrick McDaniel, Thomas La Porta, Mingli Yu, and Ting He, Misreporting Attacks Against Load Balancers in Software-Defined Networking.Kiekintveld and Fei Fang and Quanyan Zhu. Game Theory and Machine Learning for Cyber Security, John Wiley & Sons.Įditor: Charles A Kamhoua and Christopher D. Bolor-Erdene Zolbayar, Ryan Sheatsley, and Patrick McDaniel.Įvading Machine Learning based Network Intrusion Detection Systems with GANs.Technical Report arXiv:2203.06694, arXiv preprint, March 2022. Krishnamurthy, Generating Practical Adversarial Network Traffic Flows using NIDSGAN. Weisman, Sencun Zhu, Shitong Zhu, and Srikanth V. Bolor-Erdene Zolbayarn, Ryan Sheatsley, Patrick McDaniel, Michael J.Measuring and Mitigating the Risk of IP Reuse on Public Clouds.Ģ022 IEEE Symposium on Security and Privacy (S&P), IEEE, May 2022. Eric Pauley, Ryan Sheatsley, Blaine Hoak, Quinn Burke, Yohan Beugin, and PatrickMcDaniel.Proceedings on Privacy Enhancing Technologies (PETS), July 2022.
Building a Privacy-Preserving Smart Camera System.
Of the modules highlighted 72% did contain errors. However in the STANFINS project, we had a better success rate of finding the error modules. Just by viewing the external complexity, the metric does a relatively good job of pointing out high error modules, with only viewing 10% of the modules we found 33% of the errors.Comparing the results of STANFINS-R and the results of the BSU projects, the BSU projects did better in finding the errors 33% verus 53%. Since the D(G) is comprised of an internal and external component it is necessary to evaluate Di to support this hypothesis on a large project.
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Wayne Zage and Professor Dolores Zage are working on a Design metrics project to develop a metrics approach for analyzing software design.The purpose of this thesis is to test the hypotheses of this metric by calculating the De external design component, and to show the correlation of errors and stress points in the design phase for a large Ada Software, professionally developed at Computer Sciences Corporation.From these studies we can relatively conclude that De does indicate the error-prone module.