Sepideh Niktabe

Sepideh Niktabe

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location of Sepideh NiktabeNorth York, Ontario, Canada

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  • Timeline

  • About me

    Cyber Security Analyst @Vosyn || Graduate Research Assistant@Behaviour-Centric Cybersecurity Center (BCCC)

  • Education

    • Amirkabir University of Technology - Tehran Polytechnic

      2015 - 2020
      Bachelor of Science - BS Computer Science
    • York University

      2022 - 2024
      Master of Science - MS Computer Science
  • Experience

    • Lassonde School of Engineering - York University

      May 2022 - May 2023

      Assisting professor Rehman in the introduction to database systems Assisting Professor Madani in the Introduction to Computer Security course.

      • Teaching Assistant

        May 2022 - May 2023
      • Teaching Assistant

        May 2022 - Aug 2022
    • Lassonde School of Engineering - York University

      Sept 2023 - Sept 2024

      •Developed NTLFlowLyzer, a Python open-source project, to extract network layer features from TCP-based traffic for anomaly profiling. It generates bidirectional flows, calculates statistical features separately for forward and backward directions, and includes functionalities for feature selection, addition, and flow timeout control. TCP flows terminate upon connection teardown, maximum duration, or inactivity timeout.• Developed an optimized IoT bot detection and identification model using XAI(SHAP), combining XGBoost with a novel feature selection method, and an LSTM neural network optimized via Bayesian Optimization. Achieved 0.9999 accuracy, precision, recall, and F1-score on the augmented ’BCCC-Aposemat-Bot-IoT-24’ dataset, outperforming established models and excelling in handling sequential data, imbalanced datasets, and providing explainable insights. Model evaluation utilized AWS SageMaker and S3 storage for streamlined building, training, and deployment.• Developed the ‘BCCC-CIRA-CIC-DoHBrw-2020’ dataset and innovative feature engineering method to profile DoH traffic, achieving over 93% accuracy using decision trees. This research addressed gaps in DoH traffic analysis,enhancing privacy and security profiling despite dataset limitations.(Article Link)• Developed statistical pattern recognition models using linear and logistic regression to profile benign and maliciousDoH traffic, achieving up to 95.35% accuracy and 97.61% F1-score on the CIRA-CIC-DoHBrw-2020 dataset. The models offeradvantages in computational efficiency, simplicity, and robustness, though performance may vary with different datasets. Show less

      • Teaching Assistant

        Mar 2024 - Sept 2024
      • Graduate Research Assistant

        May 2022 - Aug 2024
      • Teaching Assistant

        Jan 2024 - May 2024
      • Teaching Assistant

        Jan 2024 - May 2024
      • Teaching Assistant

        Sept 2023 - Dec 2023
    • Vosyn

      Sept 2024 - now
      Cyber Security Analyst
  • Licenses & Certifications

  • Honors & Awards

    • Awarded to Sepideh Niktabe
      WiCyS Security Training Scholarship - Tier 1 Women in CyberSecurity (WiCyS) Sep 2024 Accepted into Tier 1 of the WiCyS Security Training Scholarship, made possible by WiCyS Tier 1 Partners and Premiere Supporters. This program includes participating in the SANS BootUp Capture-the-Flag (CTF) event, designed to enhance cybersecurity skills through practical, interactive challenges. The CTF covers tasks such as identifying hidden code on web pages and verifying communication authenticity, fostering a hands-on learning experience in a browser-based environment.
    • Awarded to Sepideh Niktabe
      York University Fellowship - May 2022 York university Fellowship C$62,500 for my master studies