The UMBC Cyber Defense Lab presents

Tensor decomposition is a powerful unsupervised machine learning method used to extract hidden patterns from large datasets. This presentation aims to illuminate the extensive applications and capabilities of tensors within the realm of cybersecurity. We offer a comprehensive overview by encapsulating a diverse array of capabilities, showcasing the cutting-edge employment of tensors in the detection of network and power grid anomalies, identification of SPAM e-mails, mitigation of credit card fraud, and detection of malware. Additionally, we delve into the utility of tensors for classifying malware families, pinpointing novel forms of malware, analyzing user behavior, and utilizing tensors for data privacy through federated learning techniques.

Maksim E. Eren is an early career scientist in A-4, Los Alamos National Laboratory (LANL) Advance Research in Cyber Systems division. He graduated Summa Cum Laude with a Computer Science Bachelor’s at University of Maryland Baltimore County (UMBC) in 2020 and Master’s in 2022. He is currently pursuing his Ph.D. in the UMBC DREAM Lab, and he is a Scholarship for Service CyberCorps alumnus. His interdisciplinary research interests lie at the intersection of machine learning and cybersecurity, with a concentration in tensor decomposition. His tensor decomposition-based research projects include large-scale malware detection and characterization, cyber anomaly detection, data privacy, text mining, knowledge graphs, and high-performance computing. Maksim has developed and published state-of-the-art solutions in anomaly detection and malware characterization. He has also worked on various other machine learning research projects such as detecting malicious hidden code, adversarial analysis of malware classifiers, and federated learning. At LANL, Maksim was a member of the 2021 R&D 100 winning project SmartTensors, where he has released a fast tensor decomposition and anomaly detection software, contributed to the design and development of various other tensor decomposition libraries, and developed state-of-the-art text mining tools. Maksim is currently the lead for the Cyber Science Research Program (CSRP), a cybersecurity research internship at LANL.

 

Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.

 

 

The theme of of the 2024 KIL workshop is on developing and using metrics, methods, and datasets for consistent, reliable, explainable and safe LLMs.

Professor Gaur was also an organizer of the three previous KIL workshops, held at KDD in 2023, the knowledge Graph conference in 2021, and at KDD 2020.

UMBC CSEE Professor Ramana Vinjamuri received a new NSF IUCRC Phase II award to support participation as a partner in BRAIN, the Building Reliable Advances and Innovations in Neurotechnologies Industry-University Cooperative Research Center.

BRAIN's overall goal is to develop and validate new technologies that address the needs of physically and neurologically impaired individuals and those of the growing aging population. 

Research in Dr. Vinjamuri's lab complements existing strengths in other BRAIN sites and expands research into new areas, including artificial intelligence, neural imaging and stimulation, cyber-human systems, human-centered computing, neural signal processing, and virtual/augmented/mixed reality.

UMBC's Vandana Janeja in the Information Systems Department was selected as a recipient of the 2024 University System of Maryland Regents Faculty Award for Excellence in Scholarship or Research, the highest honor that the Board of Regents bestows to recognize exemplary faculty achievement.

Dr. Janeja is a world-renowned expert in data science and artificial intelligence. As a multidisciplinary researcher, her research spans data science, information technology, geoscience, linguistics, and education. After serving as the department chair of Information Systems from 2019 to 2023, Dr. Janeja is currently leading the College of Engineering and Information Technology as the Associate Dean for Research and Faculty Development. 

In the last three years, Dr. Janeja was exceptionally productive with one scholarly book on data analytics for cybersecurity, many research articles in prestigious refereed journals, and more than 16.5 million research funding from NSF. Her extramurally funded research projects address a wide range of national priorities such as data science education, research capacity building for earth and environmental sciences, and climate change. She uses novel data and AI-driven methods to advance research and education in these areas. 

Dr. Janeja’s research is incredibly impactful through her multidisciplinary collaboration. One exemplary project is the NSF HDR Institute for Harnessing the data and model revolution in the polar regions (iHARP), which is funded by one of the most prestigious NSF awards. As the director of iHARP, Dr. Janeja leads a team of more than 80 faculty, scientists, postdoctoral researchers, and students from nine institutions, industry, and government agencies to address the most pressing challenges in the Polar region. What sets Dr. Jeneja apart from her peers is her ability to bridge information technology and social sciences to make big societal impacts. In NSF-funded deepfake discernment project, Dr. Jeneja collaborates with linguists to advance the detection of audio deepfakes. Her work exemplifies the need to address the issues of ethics and responsibility in data science and AI. It will greatly improve the effectiveness of deepfake discernment to enhance cybersecurity, prevent financial fraud, and inform legal practice.