Maksim Eren, Tensor Decomposition Methods for Cybersecurity

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. About the speaker: 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. at UMBC's 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, 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.

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. About the speaker: 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. at UMBC's 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, 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.