Zakaria EL Mrabet
Data Scientist
Grand Forks,ND
Summary
Data Scientist familiar with gathering, cleaning and organizing data for use by technical and non-technical personnel. Advanced understanding of machine learning models, statistical, and analytical techniques. Highly organized, motivated and diligent with significant background in programming.
Overview
3
3
Languages
8
8
years of post-secondary education
4
4
years of professional experience
Work History
Graduate Research Assistant
University Of North Dakota
Grand Forks, ND
07.2017 - Current
- Gathered and built complex cyber-security dataset from multiple data sources
- Analyzed, cleaned, and pre-processed the prepared dataset using Python and Excel.
- Developed online/offline Machine Learning models based on Neural Network, Random Forest, and Deep Learning, for classifying anomalies and cyber-attacks
- Implementing these Machine Learning Models using Python and TensorFlow on the Google Colab Research Platform.
- Training and extensively testing these models based on several performance metrics
- Communicating verbally and presenting the obtained results to my research team (including technical and non-technical members) through visual graphs using various visualization tools, including matplotlib python library and MATLAB.
- Publishing and presenting the findings in top-tier conferences, such as the IEEE BigDATA conference.
Research Assistant
Pacific Northwest National Laboratory (PNNL)
Richland, WA
09.2019 - 03.2020
- Collaborated with a cross-disciplinary team for simulating and generating a large power system dataset using the GridPACK platform.
- Cleansed, selected relevant features, and pre-processed the prepared dataset using Python and Excel.
- Developed a Radom Forest based model able to deal with classification and regression problem at the same time.
- Trained, tested, and fine-tuned the developed model using Python and TensorFlow
- Conducted a comparison analysis with other models including, Neural Network, Decision Tree, SVM, Naive Bayes, and Deep Neural Network.
- The proposed model report a detection accuracy of 70%
- These results have been presented through a poser session at the North American SynchroPhasor Initiative (NASPI) 2020. It is available at: https://www.naspi.org/node/794
- The source code of this project is publicly available on GitHub at: https://github.com/zakaria-grid/ML-GridPack/blob/master/ML_comparison_analysis.ipynb
Education
PhD. - Electrical Engineering and Computer Science
University Of North Dakota
Grand Forks, ND 01.2018 - Current
Master of Science - Information Systems Security
Ibn Tofail University
09.2012 - 07.2014
Bachelor of Science - Computer Science
University Mohammed V, Rabat
Morocco 09.2009 - 07.2012
Skills
Strong coding skills in Java and Python
undefinedAffiliations
- Institute of Electrical and Electronics Engineers
- Toastmasters
Awards
- A grant from the National Science Foundation (NSF) to perform an intern at PNNL for 6 months (09/2019-03/2020)
- ND EPSCoR Cyber-Infrastructure Assistantship (01/2021-08/2021)
Publications
- Z. Elmrabet, D. F. Selvaraj, P. Ranganathan, “Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets, ” 2019IEEE Bigdata 2019 conference.
- Z. Elmrabet , D. F. Selvaraj, P. Ranganathan, “Detection of the False Data Injection Attacks in the Home Area Networks using ANN,” IEEE EIT 2019 Conference.
- Z. Elmrabet , H. Elghazi, N. Kaabouch, “A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid,” IEEE EIT 2019 Conference.
- K. Ketelyn, R. Andrew, S. Debenjan, S. Daisy, Z. Elmrabet, D. Matt, R. Prakash, “Heat Loss Estimation using UAS Thermal Imagery”, IEEE EIT 2019 Conference.
- Z. Elmrabet, M. Ezzari, H. El Ghazi, B.A, El Majd, “Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure”, Proceedings of the 2nd International Conference on Networking, Information Systems & Security, 2019
- Z. Elmrabet , H. Elghazi, N. Kaabouch, H. Elghazi, “Cyber-Security in Smart Grid: Survey and Challenges,” Computer and Electrical Engineering, 2018.
- Z. Elmrabet , N. Kaabouch, “Detecting the Primary User Emulation Attack Using Logistic Regression and the MLE,” submitted to Computers & Security journal in May 2018.
- Y. Arjoune, Z. Elmrabet , N. Kaabouch, “Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks,” MDPI Journal of Applied Sciences, 8(4), 628, 2018.
- Z. Elmrabet , Y. Arjoune, H. Elghazi, B. El Majd, N. Kaabouch, “Primary User Emulation Attacks: A detection technique based on Kalman Filter,” Submitted to Journal of Sensor and Actuator Networks on April 18, 2018.
- Y. ARJOUNE, Z. Elmrabet , N. Kaabouch, ‘Spectrum sensing: Enhanced energy detection technique based on noise measurement', Computing and Communication Workshop and Conference (CCWC), 2018, pp. 828-834.
- Z. Elmrabet , H. Elghazi, T. Sadiki, and H. Elghazi, “A New Secure Network Architecture to Increase Security Among Virtual Machines in Cloud Computing,” in Advances in Ubiquitous Networking, 2016, pp. 105–116.
Timeline
Research Assistant
Pacific Northwest National Laboratory (PNNL)
09.2019 - 03.2020
PhD. - Electrical Engineering and Computer Science
University Of North Dakota
01.2018 - Current
Graduate Research Assistant
University Of North Dakota
07.2017 - Current
Master of Science - Information Systems Security
Ibn Tofail University
09.2012 - 07.2014
Bachelor of Science - Computer Science
University Mohammed V, Rabat
09.2009 - 07.2012
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