Chiheb Chebbi
Cyber Defense Specialist at Intellisec Solutions
Canada
Summary
👤
Senior🎓
Top SchoolChiheb Chebbi is a Cyber Defense Specialist based in Canada with 11 years of experience blending hands-on security operations and applied machine learning for threat detection. At Intellisec Solutions he focuses on defensive strategies while drawing on a rare mix of InfoSec and data-science skills demonstrated by code contributions to Packt's "Mastering Machine Learning for Penetration Testing." A Microsoft MVP and repeat technical reviewer for Packt titles on machine learning, cybersecurity, and reverse engineering, he bridges practitioner instincts with research-backed techniques. His background includes early software engineering roles at Microsoft Innovation Center, giving him strong foundations in performance optimization and applied software development. Chiheb brings a curiosity-driven approach—self-described as a security "n00b" persona that belies deep practical contributions to ML-for-security tooling and educational publishing. He is particularly adept at turning ML prototypes into practical detection models for phishing, malware, and image-based analysis.
11 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at TEK-UP
Arabic, French, English
Github Skills (12)
scikit-learn10
machine-learning10
python10
scikit10
numpy9
tensorflow9
pandas9
keras9
theano8
matplotlib7
deep-learning7
penetration-testing6
Programming languages (3)
Github contributions (5)
PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing
Apr 2018 - Aug 2018
Mastering Machine Learning for Penetration Testing, published by Packt
Role in this project:
Data Scientist
Data ScientistContributions:90 commits, 81 pushes, 1 comment in 3 months
Contributions summary:Chiheb primarily contributed code examples demonstrating machine learning concepts and techniques within the context of penetration testing. They implemented Python scripts showcasing various models, including Logistic Regression and Decision Trees for phishing detection, as well as a Random Forest model for malware detection. Furthermore, the user explored deep learning with a Keras-based neural network for image recognition and demonstrated fundamental concepts in TensorFlow and Theano.
machine-learningpenetration-testing
chihebchebbi/docusaurus-starter
Jan 2023 - Mar 2025
Contributions:210 pushes, 1 branch in 2 years 1 month
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