Somesh Jha

About

Somesh Jha is the Lubar Professor in the Computer Sciences Department at the University of Wisconsin, Madison. He holds a Ph.D. from the School of Computer Science at Carnegie Mellon University. His main research interests lie at the intersection of security and formal methods, with current active focus areas including adversarial machine learning and privacy. He has a peripheral interest in topics such as computational finance, although he has not been actively involved in that area for several years. Jha has contributed to the field through his research, and his work can be accessed via his DBLP and Google Scholar pages. He has been involved in various projects, including the Center for Trustworthy Machine Learning, and has mentored several Ph.D. students. In addition to his research, Jha teaches courses such as Introduction to Cryptography and Security and Privacy for Data Science. He has also provided expert consulting as an expert witness and has been involved with startups like Novashield and Tala Security, focusing on malware detection and web security, respectively.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Data Mining
• Machine Learning
• Operating system
• Distributed computing
• Mathematics
• Telecommunications
• Data science

Selected publications

• ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining

  Lecture notes in computer science · 2021 · 75 citations

  Senior authorCorresponding
  DOI

• Semantic Robustness of Models of Source Code

  2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · 2022 · 71 citations

  Deep neural networks are vulnerable to adversarial examples-small input perturbations that result in incorrect predictions. We study this problem for models of source code, where we want the neural network to be robust to source-code modifications that preserve code functionality. To facilitate training robust models, we define a powerful and generic adversary that can employ sequences of parametric, semantics-preserving program transformations. We then explore how, with such an adversary, one c…

  DOI

• OAT: Attesting Operation Integrity of Embedded Devices

  2022 IEEE Symposium on Security and Privacy (SP) · 2020 · 69 citations

  Senior authorCorresponding

  Due to the wide adoption of IoT/CPS systems, embedded devices (IoT frontends) become increasingly connected and mission-critical, which in turn has attracted advanced attacks (e.g., control-flow hijacks and data-only attacks). Unfortunately, IoT backends (e.g., remote controllers or in-cloud services) are unable to detect if such attacks have happened while receiving data, service requests, or operation status from IoT devices (remotely deployed embedded devices). As a result, currently, IoT bac…

  DOI

• TRACE: Enterprise-Wide Provenance Tracking for Real-Time APT Detection

  IEEE Transactions on Information Forensics and Security · 2021 · 61 citations

  We present TRACE, a comprehensive provenance tracking system for scalable, real-time, enterprise-wide APT detection. TRACE uses static analysis to identify program unit structures and inter-unit dependences, such that the provenance of an output event includes the input events within the same unit. Provenance collected from individual hosts are integrated to facilitate construction of a distributed enterprise-wide causal graph. We describe the evolution of TRACE over a four-year period, during w…

  DOI

• Identifying and Mitigating the Security Risks of Generative AI

  Foundations and Trends® in Privacy and Security · 2023-12-14 · 43 citations

  articleOpen access

  Every major technical invention resurfaces the dual-use dilemma—the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This…

  PublisherDOI

Recent grants

• SaTC: CORE: Frontier: Collaborative: End-to-End Trustworthiness of Machine-Learning Systems

  NSF · $696k · 2018–2025

• CPS: Small: Self-Improving Cyber-Physical Systems

  NSF · $500k · 2017–2022

• SaTC: CORE: Medium: Collaborative: User-Centered Deployment of Differential Privacy

  NSF · $77k · 2020–2021

Frequent coauthors

• Xi Wu

  Chengdu University of Information Technology

  34 shared

• Thomas Reps

  33 shared

• Jiefeng Chen

  Central South University

  31 shared

• Mihai Christodorescu

  24 shared

• Ninghui Li

  23 shared

• Tianhao Wang

  23 shared

• Matt Fredrikson

  21 shared

• Prasad Chalasani

  21 shared

Education

• Ph.D.

  School of Computer Science

Similar researchers at University of Wisconsin-Madison

• Yong Jae Leeh-90
• Michael Gleicherh-62
• AnHai Doanh-56