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Events for March 17, 2025
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Spring 2025 Viterbi Trek Mon, Mar 17, 2025 @ 10:00 AM - 05:00 PM
Viterbi School of Engineering Career Connections
Receptions & Special Events
Viterbi Trek is an exclusive opportunity for a select group of Viterbi students to visit companies during the Fall Recess! Explore industries, career options, and cultures at partner organizations. Trek is a wonderful opportunity to engage directly with employers and gain insights into your field of interest while learning about potential career paths. Trek provides a unique opportunity for Viterbi students to: Tour company offices to experience workplace environments. Meet employers and talk about career paths, current projects, and industry trends. Learn about company culture and potential job/internship opportunities. If selected, you will be assigned to attend 1 site visit, and will have the opportunity to participate in a company visit! Matches are based on your major and availability, and each visit is in-person. Apply only if you are available on all dates listed. Trek is open to undergraduate and graduate students with priority given to sophomores, juniors, and first-year graduate students. Eligibility: Viterbi Engineering Student GPA of 3.0+ Updated resume with VMock score of 80%+ Must be available on Trek dates
Location: Off Campus
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Connections
Event Link: https://usc.joinhandshake.com/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EiS Communications Hub - Tutoring for Engineering Ph.D. Students
Mon, Mar 17, 2025 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks! All tutoring is one-on-one and conducted by Viterbi faculty.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
USC-ISI Networking and Cybersecurity Seminar Talk
Mon, Mar 17, 2025 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Dr. Bhavani Thuraisingham, University of Texas at Dallas
Talk Title: Trustworthy Artificial Intelligence for Securing Transportation Systems
Series: Networking and Cybersecurity
Abstract: Artificial Intelligence (AI) techniques are being applied to numerous applications from Healthcare to Cyber Security to Finance. For example, Machine Learning (ML) algorithms are being applied to solve security problems such as malware analysis and insider threat detection. However, there are many challenges in applying ML algorithms for various applications. For example, (i) the ML algorithms may violate the privacy of individuals. This is because we can gather massive amounts of data and apply ML algorithms on the data to extract highly sensitive information. (ii) ML algorithms may show bias and be unfair to various segments of the population. (iii) ML algorithms themselves may be attacked possibly resulting in catastrophic errors including in cyber physical systems such as transportation systems.
In this presentation, we discuss our research we are conducting as part of the USDOT National University Technology Center TraCR (Transportation Cybersecurity and Resiliency) led by Clemson University. In particular, we describe (i) the application of federated machine learning techniques for detecting attacks in transportation systems; (ii) publishing synthetic transportation data sets that preserves privacy, (iii) fairness algorithms for transportation systems, and (iv) examining how GenAI systems are being integrated with transportation systems to provide security. Finally, we discuss resiliency issues with respect to transportation systems where such systems and applications must continue to operate in the midst of attacks and failures.
Biography: Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Founding Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is an elected Fellow of the ACM, IEEE, the AAAS, and the NAI. Her research interests are on integrating cyber security and artificial intelligence/data science including as they relate to the cloud, social media, and Transportation Systems. She has received several technical, education and leadership awards including the IEEE CS 1997 Edward J. McCluskey Technical Achievement Award, the IEEE CS 2023 Taylor L. Booth Education Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009). Her 44+ year career includes industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 140+ journal articles, 300+ conference papers, 200+ keynote and featured addresses, seven US patents, sixteen books, and over 120 panel presentations including at Fortune Media, Lloyds of London Insurance, Dell Technologies World, United Nations, and the White House Office of Science and Technology Policy. She has also written opinion columns for popular venues such as the New York Times, Inc. Magazine, Womensday.com and the Legal 500. She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK. She also has a Certificate in Public Policy Analysis from the London School of Economics and Political Science. She has been featured in the book by the ACM in 2024 titled: “Rendering History: The Women of ACM-W” as one of the 30+ “Women that Changed the Face of World Wide Computing Forever.”
Host: David Balenson and Jelena Mirkovic
More Info: https://www.isi.edu/events/5645/trustworthy-artificial-intelligence-for-securing-transportation-systems/
Webcast: https://www.isi.edu/events/5645/trustworthy-artificial-intelligence-for-securing-transportation-systems/Location: Information Science Institute (ISI) - 1135/37
WebCast Link: https://www.isi.edu/events/5645/trustworthy-artificial-intelligence-for-securing-transportation-systems/
Audiences: Everyone Is Invited
Contact: Matt Binkley / Information Sciences Institute
Event Link: https://www.isi.edu/events/5645/trustworthy-artificial-intelligence-for-securing-transportation-systems/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
"Keys to Life" series at USC ORSL
Mon, Mar 17, 2025 @ 12:00 PM - 01:00 PM
USC Viterbi School of Engineering
University Calendar
"Keys to Life" with Prof. Weiss is a motivational discussion series designed to promote student success and well-being. This series is for students who want to develop their "keys" in a small group setting and a peaceful, reflective environment. Finding purpose is essential to living a meaningful life and key to personal fulfillment. This series will help students identify and articulate their purpose and provide group motivation to work towards it. A unique feature of the series will be its peripatetic "Purpose Walks" through campus.
More Information: Keys to Life with Prof. Weiss.jpg
Location: University Religious Center (URC) - courtyard
Audiences: Everyone Is Invited
Contact: Elisabeth Arnold Weiss
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
USC-ISI Networking and Cybersecurity Seminar Talk
Mon, Mar 17, 2025 @ 01:30 PM - 02:30 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Dr. Latifur Khan, University of Texas at Dallas
Talk Title: Generative AI including Large Language Models for Cyber-Security
Series: Networking and Cybersecurity
Abstract: In this presentation, I will explore three applications of generative AI, specifically Large Language Models (LLMs), in the domains of tabular security datasets, cyber-threat reports, and Federal and State legislation related to autonomous vehicles.
1. Learning for Tabular Security Datasets and its applications to Automotive Security.
Tabular datasets in cybersecurity present significant challenges for machine learning due to their heavily imbalanced nature—with a small number of labeled attack samples buried in a vast sea of mostly benign, unlabeled data. Semi-supervised learning leverages a small subset of labeled data alongside a large subset of unlabeled data to train a model. While semi-supervised methods have been extensively studied in image and language domains, they remain underutilized in security contexts—particularly for tabular security datasets, where challenges such as contextual information loss and class imbalance hinder machine learning performance. To address these issues, we propose MCoM (Mixup Contrastive Mixup), a novel semi-supervised learning methodology that introduces a triplet mixup data augmentation approach to mitigate the imbalanced data problem in tabular security datasets.
Many automotive security datasets are tabular in nature. We leverage these advantages to produce novel solutions for securing smart vehicles. Machine learning approaches are a natural choice for detecting such attacks based on the payload information. However, machine learning models typically require a large dataset for training. With manufacturers independently gathering this data based on their own cars, it is unlikely that all this data will be available in one place. To address this issue, we explore federated solutions that learn in a distributed manner for increased smart vehicle security. We explore challenging scenarios in which we do not assume an independent and identically distributed (IID) setting for the data. With a combination of techniques including triplet-mixup based augmentation and a data exchange scheme involving synthetically generated samples, we show that we can attain strong performance in the most challenging label distribution scenarios.
2. AI for Cybersecurity Intelligence and Policy
In this area, we will discuss several related research projects:
• Optimizing Cyber Threat Intelligence with Active Learning We propose a framework for efficiently identifying cyber-attacks, called ALERT. The ALERT framework addresses challenges in extracting actionable intelligence from complex Cyber Threat Intelligence (CTI) reports by automating the identification of attack techniques and mapping them to the MITRE ATT&CK framework. By combining active learning strategies with Large Language Models (LLMs), our approach selects only the most informative instances for annotation, achieving comparable performance with 77% less data. This significantly reduces the resource-intensive process of manual annotation by security professionals while maintaining effectiveness in threat technique extraction.
• Automating Cyber-Threat Intelligence with LLMs In collaboration with researchers from NIST, we focus on automating the extraction of attack techniques from Common Vulnerabilities and Exposures (CVE) and Cyber Threat Intelligence (CTI) reports. We then map these techniques to the standardized MITRE ATT&CK framework using a combination of LLMs and active learning. This talk will demonstrate how this curated knowledge enables security analysts to respond more effectively to cyber threats by streamlining intelligence gathering and threat attribution.
• Identifying Legislative Gaps in Autonomous Vehicle Regulations Leveraging LLMs and Retrieval-Augmented Generation (RAG), we have identified gaps in Federal and State legislation concerning data privacy and cybersecurity within the autonomous vehicle domain. This presentation will showcase how modifications or additions to existing legislative frameworks can proactively address emerging cybersecurity and privacy challenges in autonomous vehicle regulations. By integrating generative AI and LLMs into these domains, we aim to bridge critical gaps in cybersecurity, intelligence automation, and policy-making, demonstrating the transformative potential of AI in real-world applications.
3. Responsible Active Online Learning for Streaming Data
In many practical applications, machine learning systems face three interconnected challenges: processing continuous streams of incoming data, handling predominantly unlabeled datasets, and ensuring responsible and unbiased predictions across diverse demographic groups. Current approaches rarely address all three aspects effectively. We propose a framework called FACTION, which strategically identifies and selects the most valuable data points for annotation by balancing model uncertainty with ethical considerations for various subpopulations. The system additionally demonstrates exceptional capability in detecting anomalous data points within streaming contexts. Through comprehensive testing on real-world datasets and rigorous theoretical validation, FACTION shows promising results in maintaining both accuracy and responsible AI principles in evolving data landscapes. This approach could potentially be applied to transportation safety systems, particularly pedestrian detection in autonomous vehicles where cameras continuously capture diverse individuals in varying conditions. By intelligently selecting informative detection scenarios for annotation, such an application might help address potential disparities in detection accuracy while optimizing the labeling process for continuous video feeds.
*This work is funded by NSF, DOT, NIH, ONR, ARMY, and NSA.
Biography:
Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000.
Dr. Khan is a fellow of IEEE, IET, BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award, and IBM Faculty Award (research) 2016. Dr. Khan has published over 300 papers in premier journals and prestigious conferences. Currently, Dr. Khan’s research focuses on big data management and analytics, data mining and its application to cyber security, and complex data management including geospatial data and multimedia data. His research has been supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found at www.utdallas.edu/~lkhan.
Host: David Balenson and Jelena Mirkovic
More Info: https://www.isi.edu/events/5655/generative-ai-including-large-language-models-for-cyber-security/
Webcast: https://www.isi.edu/events/5655/generative-ai-including-large-language-models-for-cyber-security/Location: Information Science Institute (ISI) - 1135/37
WebCast Link: https://www.isi.edu/events/5655/generative-ai-including-large-language-models-for-cyber-security/
Audiences: Everyone Is Invited
Contact: Matt Binkley / Information Sciences Institute
Event Link: https://www.isi.edu/events/5655/generative-ai-including-large-language-models-for-cyber-security/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
