Maksim E. Eren

Maksim E. Eren

Graduate Research Assistant

University of Maryland, Baltimore County

Biography

Maksim Eren is a graduate Computer Science student at the University of Maryland Baltimore County (UMBC) and a Scholarship for Service CyberCorps alumnus. He graduated Summa Cum Laude with a Computer Science Bachelors at UMBC in 2020. He has held multiple internships, which have ranged from incident response and software engineering at Montgomery County Government (MCGov), a research assistantship at CyberPacks, to a teaching assistantship at UMBC. At MCGov, Maksim investigated and responded to hundreds of cyber incidents, and has developed several dashboards and incident analysis tools for the security operations center. Currently, he is working as a graduate research assistant at Los Alamos National Laboratory (LANL) and at UMBC’s DREAM Lab.

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, and topic modeling and evolution of scientific articles. 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, and adversarial analysis of malware classifiers. 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, and contributed to the design of various other tensor decomposition libraries.

Interests

  • Machine Learning
  • Tensor Decompositions
  • Cybersecurity

Education

  • 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

Maksim E. Eren, Nick Solovyev, Chris Hamer, Renee McDonald, Boian S. Alexandrov, Charles Nicholas (2021). COVID-19 Multidimensional Kaggle Literature Organization. In ACM Symposium on Document Engineering 2021 (DocEng ’21), 2021.

DOI Preprint URL Preprint PDF Interactive Plot Details

John Boutsikas, Maksim E. Eren, Charles Varga, Edward Raff, Cynthia Matuszek, Charles Nicholas (2021). Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery. Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021.

Preprint URL Preprint PDF Abstract Poster Code Details

Maksim E. Eren, Charles Nicholas, Renee McDonald, Chris Hamer (2021). Random Forest of Tensors (RFoT). Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021.

Abstract Poster Details

Maksim E. Eren, Juston S. Moore, Boian S. Alexandrov (2020). Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization. In IEEE International Conference on Intelligence and Security Informatics (ISI), 2020.

DOI Preprint PDF Details

Maksim E. Eren, Nick Solovyev, Edward Raff, Charles Nicholas, Ben Johnson (2020). COVID-19 Kaggle Literature Organization. In ACM Symposium on Document Engineering 2020 (DocEng ’20), 2020.

DOI Preprint URL Preprint PDF Poster Code Interactive Plot Details

See all publications

Recent Posts

Maksim E. Eren
Last updated on Dec 20, 2021 5 min read

Anaconda and Jupyter Setup for Research

Using Anaconda and Jupyter for research has become a daily routine for me. Here I list some of the most frequent commands I utilize when setting up conda environments with Jupyter for my research proejcts.

Maksim E. Eren
Last updated on Dec 29, 2021 8 min read

Python Documentation with Sphinx

Documenting your code is an important part of any project from developing a library to research code. In this blog post, I will give a brief tutorial on how to utilize Sphinx for documenting Python code. Sphinx is a tool utilized by several popular libraries. It turns the in-code comments into a user-friendly and modern documentation website.

Maksim E. Eren
Last updated on Dec 20, 2021 3 min read

screen + Jupyter - A way to execute long running Jupyter notebooks headless mode

Jupyter notebooks is a great way to work on research code. But notebooks used to analyse large datasets take long time to execute. And if our local or remote terminal session dies, our notebook dies too, resulting in waste of time. Here I summarize how we can run a notebook in headless mode in a screen session which allows us to run Jupyter notebook indipendent from the terminal session.

Recent Talks

Anomalous Event Detection using Non-Negative Poisson Tensor Factorization

Network intrusion detection systems that are based on statistical User Behaviour Analytics play a fundamental role in the identification of anomalous agents such as malicious insiders, misused accounts, and users with compromised credentials. To this extent, there have been significant results in detecting anomalies from learned user behavior models via non-negative Poisson matrix factorization. We expand upon previous work in this project by exploiting the higher dimensional and sparse problems created by the user authentication data. An integrated multidimensional anomaly scoring method based on tensors and Poisson recommender systems is proposed. In our experiments, we build a higher-order model that can detect the accounts compromised by red-team during penetration testing activities at a large organization.
Aug 10, 2020 9:00 AM — 1:00 PM Virtual Event
Maksim E. Eren, Juston S. Moore, Boian S. Alexandrov
Presentation
Anomalous Event Detection using Non-Negative Poisson Tensor Factorization

Software

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. Tensor train (TT) and Hierarchical Tucker(HT) are state-of-the-art tensor network introduced for factorization of high-dimensional tensors. These methods transform the initial high-dimensional tensor in a network of low dimensional tensors that requires only a linear storage. Many real-world data,such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce the distributed non-negative Hierarchical tensor decomposition tools and demonstrate their scalability and the compression on synthetic and real world big datasets. pyDNTNK is developed as part of the R&D 100 award wining SmartTensors project.
Code Details

lanl/pyQBTNs

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers. The library includes five different boolean tensor decomposition methods making up three distinct types of tensor networks. pyQBTNs is developed as part of the R&D 100 award wining SmartTensors project.
Code Details

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. pyCP_APR uses the CANDECOMP/PARAFAC Alternating Poisson Regression (CP-APR) tensor factorization algorithm utilizing both Numpy and PyTorch backend. While the Numpy backend can be used for the analysis of both sparse and dense tensors, PyTorch backend provides faster decomposition of large and sparse tensors on the GPU. pyCP_APR’s Scikit-learn like API allows comfortable interaction with the library, and include the methods for anomaly detection via the p-values obtained from the CP-APR factorization.
Code Details

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). Additionally, the Custom Clustering algorithm allows for automated determination for the number of Latent features. pyDNMFk is developed as part of the R&D 100 award wining SmartTensors project.
Code Details

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