With the tremendous increase in the use of wireless devices, understanding the surrounding wireless/RF environment is becoming essential for many application areas. In this work, we develop the technical building blocks needed for a spectrum monitoring system that can incrementally learn about the signals present in a deployed environment. We achieve "incremental learning (IL)" by identifying and grouping the new/unknown signals and, automatically building new machine learning (ML) models for detecting them. A thorough evaluation of our approach demonstrates its adaptability and high accuracy with signal data from several over-the-air scenarios.
A Data-Driven Reflection on 36 Years of Security and Privacy Research
Meta-research—research about research—allows us, as a community, to examine trends in our research and make informed decisions regarding the course of our future research activities. Additionally, overviews of past research are particularly useful for researchers or conferences new to the field. In this work we use topic modeling to identify topics within the field of security and privacy research using the publications of the IEEE Symposium on Security & Privacy (1980-2015), the ACM Conference on Computer and Communications Security (1993-2015), the USENIX Security Symposium (1993-2015), and the Network and Distributed System Security Symposium (1997-2015). We analyze and present data via the perspective of topics trends and authorship. We believe our work serves to contextualize the academic field of computer security and privacy research via one of the first data-driven analyses. An interactive visualization of the topics and corresponding publications is available at https://secprivmeta.net.
IDE Plugins for Detecting Input-validation Vulnerabilities
Many vulnerabilities in products and systems could be avoided if better secure coding practices were in place. There exist a number of Integrated Development Environment (IDE) plugins which help developers check for security flaws while they code. In this work, we present a review of these plugins. We specifically focus on the plugins that detect input-validation-related vulnerabilities. We list salient features such as their supported IDEs, applicable languages and specific types of vulnerability checks. We believe this work synthesizes information useful for future research on IDE plugins for detecting input-validation-related vulnerabilities.
Idaho National Lab
May 2019 – Oct 2019, May 2018 - Aug 2018
Idaho Falls, ID
Nokia Bell Labs
May 2017 - Aug 2017
Murray Hill, NJ
Computer Science Department, University of South Carolina
Jan 2013 - Dec 2014
Mechanical and Biomedical Engineering Department, University of South Carolina