Maksim E. Eren

Maksim E. Eren

Scientist

Los Alamos National Laboratory

Biography

Maksim E. Eren is an early career scientist in A-4, Los Alamos National Laboratory (LANL) Advanced Research in Cyber Systems division. He is an alumnus of the Scholarship for Service CyberCorps program. Maksim graduated Summa Cum Laude with a Bachelor’s degree in Computer Science from the University of Maryland Baltimore County (UMBC) in 2020 and earned his Master’s degree from the same institution in 2022. In 2024, he received his Ph.D. from UMBC, focusing on tensor decomposition methods for malware characterization.

Eren’s interdisciplinary research interests lie at the intersection of machine learning and cybersecurity, with a focus on tensor decomposition. His tensor decomposition-based research projects encompass large-scale malware detection and characterization, cyber anomaly detection, data privacy, biology, text mining, large language models, knowledge graphs, and high-performance computing. Eren 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, including detecting malicious hidden code, adversarial analysis of malware classifiers, and federated learning. At LANL, Eren was a member of the 2021 R&D 100 winning project SmartTensors AI, 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.

Interests

  • Machine Learning
  • Tensor Decompositions
  • Cybersecurity

Education

  • PhD in Computer Science, 2024

    University of Maryland, Baltimore County (UMBC)

  • MS in Computer Science, 2022

    University of Maryland, Baltimore County (UMBC)

  • BS in Computer Science, 2020

    University of Maryland, Baltimore County (UMBC)

  • AA in Computer Science, 2018

    Montgomery College (MC)

Recent Publications

Quickly discover relevant content by filtering publications.
(2024). TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs. In ACM Symposium on Document Engineering 2024 (DocEng ’24), 2024.

Details

(2024). Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization. In IEEE 12th International Symposium on Digital Forensics and Security (ISDFS), 2024.

DOI Preprint PDF Details

(2024). Catch'em all: Classification of Rare, Prominent, and Novel Malware Families. In IEEE 12th International Symposium on Digital Forensics and Security (ISDFS), 2024.

DOI Preprint PDF Details

(2023). Electrical Grid Anomaly Detection via Tensor Decomposition. In IEEE Military Communications Conference, Articial Intelligence for Cyber Workshop (MILCOM), 2023.

DOI Preprint PDF Details

(2023). Interactive Distillation of Large Single-Topic Corpora of Scientific Papers. In IEEE Conference on Machine Learning and Applications (ICMLA 2023), 2023.

DOI Preprint PDF Details

(2023). Distributed out-of-memory NMF on CPU/GPU architectures. In The Journal of Supercomputing, 2023.

DOI Details

(2023). Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection. In ACM Transactions on Privacy and Security (TOPS) journal, 2023.

DOI Preprint PDF Details

(2023). MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware. In IEEE International Conference on Intelligence and Security Informatics (ISI), 2023.

DOI Preprint PDF Details

(2023). Malware-DNA: Machine Learning for Malware Analysis that Treats Malware as Mutations in the Software Genome. Presented at the Malware Technical Exchange Meeting (MTEM), Lawrence Livermore National Laboratory, California. July 25-27, 2023.

Poster Details

(2023). Malware-DNA: Machine Learning for Malware Analysis that Treats Malwares as Mutations in the Genome of the Software. Presented at the Conference on Data Analysis 2023 (CoDA 23), Santa Fe, New Mexico. March 7-9, 2023.

Poster Details

Software

*
lanl/T-ELF

lanl/T-ELF

Tensor Extraction of Latent Features (T-ELF) is one of the machine learning software packages developed as part of the R&D 100 winning SmartTensors AI project at Los Alamos National Laboratory (LANL). T-ELF presents an array of customizable software solutions crafted for analysis of datasets.

pyCP_ALS

pyCP_ALS

pyCP_ALS is the Python implementation of CP-ALS algorithm that was originally introduced in the MATLAB Tensor Toolbox.

RFoT

RFoT

Random Forest of Tensors (RFoT) is a novel ensemble semi-supervised classification algorithm based on tensor decomposition. We show the capabilities of RFoT when classifying Windows Portable Executable (PE) malware and benign-ware.

lanl/pyDNTNK

lanl/pyDNTNK

pyDNTNK is a software package for applying non-negative Hierarchical Tensor decompositions such as Tensor train and Hierarchical Tucker decompositons in a distributed fashion to large datasets. It is built on top of pyDNMFk.

lanl/pyQBTNs

lanl/pyQBTNs

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers.

lanl/pyCP_APR

lanl/pyCP_APR

pyCP_APR is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining SmartTensors project. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs.

lanl/pyDNMFk

lanl/pyDNMFk

pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence).

lanl/pyDRESCALk

lanl/pyDRESCALk

pyDRESCALk is a software package for applying non-negative RESCAL decomposition in a distributed fashion to large datasets. It can be utilized for decomposing relational datasets.

Recent Talks