CERIAS Weekly Security Seminar - Purdue University

The rapid adoption of mobile applications for managing consumer whole-house battery and energy systems has introduced new questions about software supply chain security. While these applications are not currently integrated with critical infrastructure, their growing role in connected energy environments highlights the importance of understanding the dependencies,permissions, and external services that support their operation. Many of these applications rely on shared third-party libraries, analytics frameworks, and messaging services, creating overlapping software ecosystems across vendors.In this talk, I will present an analysis of several battery-management mobile applications using static and dynamic analysis techniques. The study examines third-party dependencies, Android permission usage, and outbound network activity to identify common software components and shared external infrastructure. The results reveal significant overlap in libraries and permissions across applications, suggesting that vulnerabilities in widely used components could introduce shared risk pathways across multiple vendors. This work highlights the need for stronger dependency governance,permission minimization, and ongoing monitoring as mobile energy applications continue to evolve. About the speaker: Jen Sims is a cybersecurity technical professional in the Cyber Resilience and Intelligence Division at Oak Ridge National Laboratory (ORNL). Her research focuses on resilient cyber-physical systems and vulnerability assessment of technologies used within the electric grid, with particular emphasis on supply chain risk. She also conducts research in cybersecurity for manufacturing and is actively involved in cyber education outreach, engaging students from grade school through graduate programs.Jen earned a Master of Software Engineering and a Bachelor of Computer Science with a concentration in Secure Cyber Systems from the University of Texas at El Paso (UTEP). During her time at UTEP, she founded the Women in Cybersecurity (WiCyS) student chapter and helped launch the university's summer cybersecurity camps.Outside of her research, Jen is passionate about workforce development and cybersecurity education, volunteering with Oak Ridge Computer Science Girls (ORCsGirls) and creating hands-on cybersecurity activities to inspire the next generation of students.

 Software signing is a foundational mechanism for improving software supply-chain security because it helps establish artifact provenance, integrity, and authenticity across organizational boundaries. Yet the security value of software signing depends not only on cryptographic design, but also on whether signing is adopted, integrated, and used correctly in practice. This research examines these questions across multiple empirical settings, from industry deployment to modern open-source signing tools (ecosystems).In this talk, I synthesize findings from a set of studies on software signing in practice. I first discuss how organizations adopt and operationalize signing, then turn to identity-based signing using Sigstore as a case study of next-generation signing usability. I next present longitudinal evidence across five identity-based signing ecosystems showing that newer designs reduce some historical burdens, especially around key management, but do not eliminate usability challenges. Instead, friction shifts toward verification workflows, policy and configuration surfaces, and deployment integration boundaries. These lessons point beyond artifact signing alone: building trustworthy software supply chains will require broader trust mechanisms, including actor-centred approaches such as ARMS as envisioned. About the speaker: Kelechi Kalu is a fourth-year Ph.D. student in Electrical and Computer Engineering at Purdue University and a member of the Duality Lab, where he is advised by Prof. James C. Davis. His research focuses on software and AI security, especially software supply-chain security, usability, and trust in open-source ecosystems. His recent work examines software signing adoption in practice, the usability of identity-based signing tools such as Sigstore, and broader actor-centered trust mechanisms for software ecosystems. His work has appeared at USENIX Security, IEEE S&P, and ESEC/FSE. He previously interned at Microsoft Research in 2024 and received the Best Poster Award at the 2025 CERIAS Annual Security Symposium.

Quantum threats to cryptography has been fueled by Shor's and Grover's quantum algorithms and their derivatives. That has led a decade-long development of post-quantum cryptography algorithms and standards. While there are question lies about when Cryptanalytically Relevant Quantum Computers (CRQC) will be realized practically, they're imminent threats. In the meantime, Quantum computers and networks are evolving faster to take us closer to their realization in a practical and scalable manner, which will also help advance the development of CRQCs. In this talk, I will present how evolution of cryptography is shaping up, how to achieve quantum resistant security in a holistic manner and the development of quantum secure networks. About the speaker: Ashish Kundu is currently Head of Cybersecurity Research at Cisco Research. Ashish is an IEEE Fellow. He has also worked as: Head of Cybersecurity at Nuro.ai. During 2011- 2019, he worked as Research Staff Member, and Master Inventor and at the IBM T J Watson Research Center, Yorktown Heights, New York. He has led the Research and Development efforts for Security and Compliance for IBM Watson Health Cloud, IBM Watson Education, and IBM Watson Genomic Analytics. Dr. Kundu served on the IBM Research AI Ethics committee during 2015 - 2016. Dr. Kundu received Ph.D. from Computer Science at Purdue University and then he joined IBM T J Watson Research Center after that. He has been affiliated with CERIAS. Ashish is an ACM Distinguished Member, an ex-ACM Distinguished Speaker, and a Master Inventor (at IBM Research).

As foundation models, including large language models and multimodal models, are increasingly deployed in complex and high-stakes settings, ensuring their safety has become more important than ever. In this talk, I present a probabilistic perspective on AI safety: safety risks are treated as structured distributions to be discovered and controlled, rather than isolated failures to be patched. I first introduce probabilistic red-teaming methods that characterize distributions of failures, revealing systematic safety risks that standard evaluation often misses. I then describe probabilistic defense methods that control model behavior during deployment by adaptively steering generation toward constraint-aligned distributions. By unifying failure discovery and behavior control under a probabilistic perspective, this talk highlights a distributional approach for understanding and managing safety risks in foundation models. About the speaker: Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Her research focuses on probabilistic machine learning, generative modeling, and trustworthy AI. Prior to joining Purdue, she was a postdoctoral researcher at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin. She received her Ph.D. from Cornell University. Dr. Zhang has been a key organizer of the Symposium on Probabilistic Machine Learning. She has served as an Area Chair and Editor for ML conferences and journals, including ICML, NeurIPS, ICLR, AISTATS, UAI, and TMLR. Her contributions have been recognized with several honors, including AAAI New Faculty Highlights, Amazon Research Award, Spotlight Rising Star in Data Science, Seed for Success Acorn Award, and Ross-Lynn Research Scholar.

Many organizations adopt security frameworks but struggle to turn them into day-to-day operations that reduce risk without slowing delivery. This talk presents a practical operating model that pairs ISO/IEC 27001 (as the certifiable management system that runs governance, risk management, internal audit, and continual improvement) with NIST Cybersecurity Framework 2.0 (as the outcome-focused "dashboard" for aligning security priorities to business objectives and communicating posture to leaders). Attendees will see how to translate business goals into CSF 2.0 current and target profiles, convert those profiles into ISO 27001 objectives and control ownership, and design "evidence by default" workflows that reduce audit fire drills. The session will include real-world design patterns (paved roads, tiered decision rights, exception handling with expiry, and control health metrics) and highlight where assurance programs often drift into "control theater." The goal is a repeatable approach that both practitioners and researchers can critique, improve, and apply. About the speaker: Danny Vukobratovich is a Sr. IT Security Analyst at Purdue University, where he manages Purdue IT's ISO program spanning ISO/IEC 27001 (information security), ISO 9001 (quality management), and ISO/IEC 20000-1 (IT service management). He also oversees Purdue IT's business continuity and disaster recovery planning, with an emphasis on building resilient, auditable operating models that support research and administrative missions. Danny's professional focus is translating risk and governance into practical mechanisms, including clear decision rights, "evidence by design," and metrics that measure control health rather than control presence. His background includes security risk assessments, incident response, monitoring and logging, identity and access management, and standards-based audits across diverse environments. Danny holds the CISSP, ISO/IEC 27001:2022 Lead Implementer, and ITIL 4 Strategic Leader certifications, and an M.S. in Cybersecurity Management.

Deepfake voice technology is rapidly advancing, but how well do current detection systems handle differences in language and writing style? Most existing work focuses on robustness to acoustic variations such as background noise or compression, while largely overlooking how linguistic variation shapes both deepfake generation and detection. Yet language matters: psycholinguistic features such as sentence structure, complexity, and word choice influence how models synthesize speech, which in turn affects how detectors score and flag audio. In this talk,  we will ask questions such as: "If we change the way a person writes, while keeping their voice the same, will a deepfake detector still reach the same decision?" and "Are some text-to-speech and voice cloning models more vulnerable to shifts in writing style than others?" We will then discuss implications for designing robust deepfake voice detectors and for advancing more trustworthy speech AI in an era of increasingly synthetic media. About the speaker:  Thai Le is an Assistant Professor of Computer Science at the Indiana University's Luddy School of Informatics, Computing, and Engineering. He obtained his doctoral degree from the college of Information Science and Technology at the Pennsylvania State University with an Excellent Research Award and a DAAD Fellowship. His research focuses on the trustworthiness of AI/ML models, with a mission to enhance the robustness, safety, and transparency of AI technology in various sociotechnical contexts. Le has published nearly 50 peer-reviewed research works with two best paper presentation awards. He is a pioneer in collecting and investigating so-called text perturbations in the wild, which has been utilized by users and researchers worldwide to study and understand effects of humans' adversarial behaviors on their daily usage with AI/ML models. His works have also been featured in ScienceDaily, DefenseOne, and Engineering and Technology Magazine.

The rise of Industry 4.0 has transformed manufacturing through the integration of cyber-physical systems, connectivity, and real-time data exchange into increasingly automated and intelligent platforms. While these advances improve productivity and efficiency, they also introduce vulnerabilities to cyber-physical attacks that can degrade product quality, damage equipment, and pose safety risks. Effective detection depends on understanding which data sources and levels of granularity provide sufficient visibility for accurate anomaly detection and attack identification. Replicated environments, such as digital twins (DTs), help address the challenges of collecting high-fidelity data and executing complex attack scenarios in live production systems.This talk presents an AI-assisted framework for detecting cyber-physical attacks in smart manufacturing using real machine experimentation complemented by DT–based replication. The framework evaluates multiple data sources, ranging from high-level operational data to low-level control and side-channel signals, to understand how data fidelity and context influence detection performance. A hardware-in-the-loop (HIL) DT is used to replicate machine behavior, safely execute attacks, and enable controlled experimentation that would be impractical in live production environments.Through experiments on a real CNC machining system and its corresponding HIL-based DT, multiple cyber-physical attack scenarios are evaluated using statistical, machine learning, and deep learning-based detection methods. Results demonstrate that detection effectiveness is highly dependent on attack type and data granularity, highlighting the need for domain-aware, multi-source monitoring strategies. The framework is further extended to additive manufacturing, illustrating how insights derived from CNC systems can guide attack detection in related manufacturing domains.Overall, this work demonstrates how combining AI-based detection with real-world experimentation and DT technologies enables more robust and practical security analysis for cyber-physical manufacturing systems. About the speaker: Dr. Bethanie Williams is an R&D, S&E Cybersecurity Engineer at Sandia National Laboratories, where she specializes in applying artificial intelligence (AI) to enhance the security and resilience of cyber-physical systems in critical infrastructure, including power grid systems, healthcare facilities, and advanced manufacturing. She is also actively involved in the Cybersecurity Manufacturing Innovation Institute (CyManII) through her work at Sandia. Bethanie earned her Bachelor of Arts degree as a triple major in Mathematics, Spanish, and Computer Science from Berea College in 2020. During her time at Berea, she was a Bonner Scholar and a member of the women's basketball team, earning All-American honors for her athletic achievements. She completed her Master of Science in Computer Science with a concentration in Cybersecurity at Tennessee Technological University in 2022, under the supervision of Dr. Ambareen Siraj, and earned her Ph.D. in Engineering with a major in Computer Science in 2025 under the guidance of Dr. Muhammad Ismail. Her dissertation, titled "Multi-Source Data Analysis and an Effective AI-Assisted Detection Framework for Cyber-Physical Attacks in Smart Manufacturing," focused on leveraging AI-driven approaches and analyzing various data sources to detect and mitigate cyber-physical attacks in manufacturing systems. Throughout her graduate studies, Bethanie received the College of Engineering Distinguished Fellowship and the National Science Foundation (NSF) Scholarship for Service (SFS). She was a year-round intern at Sandia National Laboratories as part of the Center for Cyber Defenders (CCD) program, where she contributed to national research initiatives under CyManII. Bethanie held several executive leadership roles at Tennessee Tech, including Vice President of Cyber Eagles and Graduate Student Club. She also served as a Ph.D. advisor for Women in Cybersecurity (WiCyS). Through these roles, she actively mentored students, organized outreach events, and fostered a supportive community for women in cybersecurity. Bethanie's current research interests include cyber-physical security, modeling and simulation of industrial control systems, and leveraging AI for advanced manufacturing. As an Early Career R&D, S&E Cybersecurity Engineer at Sandia, she is committed to bridging academic innovation and national security applications to protect critical infrastructure and ensure its resilience.

Parental sharing, sometimes termed "sharenting," refers to ways that parents share information about their children online and is a common mechanism through which young children are exposed to social media. Parental sharing is controversial due to its significant benefits and risks, with researchers highlighting broader concerns regarding long-term implications for children's developing privacy standards. Yet, many parents report a high degree of acceptance for parental sharing, and parents exposing their young children to social media the most are often modeling risky online behaviors. This presentation examines parental sharing in association with privacy and security concepts, research, and interventions toward supporting safe and responsible parental sharing. About the speaker: Mary Jean Amon is a quantitative psychologist focused on human-computer interaction and an Assistant Professor in Indiana University Bloomington's Department of Informatics. Her interdisciplinary research program leverages sensing technologies and advanced analytics to understand and improve dynamic decision-making and performance in the context of complex sociotechnological systems. This includes identifying near-real-time team coordinative patterns that enhance teaming performance, as well as human factors in privacy and security. Amon's quality of work is recognized through publications in top venues, best paper awards, diverse research funding sources, and general dissemination through media outlets like Forbes, New York Times, and Washington Post.

Hardware security is not a new problem, but it is rapidly expanding in both consumer and medical domains due to hyperconnectivity. Medical devices and counterfeit medicines represent a fundamental security challenge. In particular, although counterfeit medicines are not a new issue,the problem continues to worsen as counterfeiting practices become increasingly sophisticated. The counterfeiting of biomedical products poses a serious threat to patient safety, public health, and economic stability in both developed and developing countries, and many current countermeasures remain vulnerable because they provide limited security. In this talk, we will share our work on biomedical hardware security with a focus on pharmaceutical products. We present cyber-physical biomedical security technologies that encode dosage information and authentication into edible biomaterials, enabling serialization, track-and-trace, and authentication at the dosage level. This approach empowers patients to play an active role in combating counterfeit medicines. About the speaker: Young Kim is a professor in the Weldon School of Biomedical Engineering and holds the titles of University Faculty Scholar and Showalter Faculty Scholar at Purdue University. His research centers on co-creating hardware(devices) and software (models) for large-scale societal and healthcare applications. His lab develops hybrid machine learning by combining data analytics with models grounded in optical spectroscopy and light-matter interactions to move beyond big-data, compute-intensive AI and leverage engineers' domain expertise. His work spans optical imaging and spectroscopy, mesoscopic physics, meta materials, cancer research, hardware security, and global health,unified by machine learning and data analytics. His research has been funded by a diverse range of agencies, including NIH, CDC, VA, AFOSR, USAID and Gates Foundation. His primary applications are in global health and rural community health, which address large-scale societal and healthcare challenges in mutually reinforcing ways.

AI-driven machine vision systems are becoming essential in mechanical engineering applications such as fastener classification, yet their increasing connectivity exposes them to adversarial cyberattacks. Model evasion attacks like FGSM can subtly alter input images and cause misclassification, raising concerns about reliability in automated manufacturing.This talk focuses on the role of Explainable AI and human-in-the-loop strategies in detecting and mitigating such attacks. In the presented case study, an EfficientNet-B0 fastener classification model is examined using Grad-CAM visualizations to determine whether shifts inactivation patterns can reveal adversarial manipulation. The study evaluates how FGSM-generated images affect model accuracy and confidence while assessing the XAI system's ability to highlight abnormal regions of attention and the potential for human-in-the-loop approaches to be utilized with XAI techniques as a practical path to strengthening the resilience of AI-based machine vision systems in manufacturing. About the speaker: Dr. Vijayanth Tummala is a Researcher in Cybersecurity and Human-AI Interaction. His research spans artificial intelligence and cybersecurity across interdisciplinary areas, including AI and Cybersecurity leadership, AI literacy, and computer vision applications. He was one of only seven recipients to receive the Best Paper Award in the AI track at ASME's IMECE conference held in November 2024, which features over 2,400 submissions annually. Previously, he held key leadership roles, including leading the NSA CAE-CD designation, launching graduate programs as part of a $1.5 million EDA grant received by his previous employer, and partnering with the Allen County High-Tech Crimes Unit.