Jlassi Dorra

🔹 Implemented a real-time log centralization system aggregating data from servers, con- tainers, virtual machines, and cloud environments. 🔹Designed a hybrid anomaly detection approach combining deep learning models for be- havioral analysis with rule-based open-source tools for known threat patterns. 🔹 Developed a FastAPI-based interface to train models, visualize predictions, and manage alerts. 🔹Automated real-time notifications to enable proactive monitoring and rapid incident re- sponse. 🔹Deployed a Web Application Firewall (WAF) as a reverse proxy to identify and block suspicious traffic. Tech Stack: Python | ELK Stack | TensorFlow | Deep learning | BERT | WAF | Slack API

🔹Developing 4 CTI datasets from diverse sources (NVD/CVE, threat reports...) 🔹Creating benchmark tests assessing LLMs' abilities in: - Fundamental cybersecurity knowledge - Threat analysis & reasoning - Vulnerability criticality assessment - Attack attribution & hacker fingerprinting Models Under Evaluation: GPT-4 | Llama 3 | Mistral

🔹Built a Python network sniffer to monitor and analyze traffic 🔹Conducted security code reviews and suggested improvements 🔹Set up a Snort-based IDS with custom rules to identify network attacks

Explored Odoo ERP’s core functionalities (Sales, Inventory, CRM) through hands-on configuration and testing.

🔹Designed and developed an e-commerce mobile application with secure JWT (JSON Web Tokens) authentication. 🔹Built a real-time order tracking system using WebSocket, enabling encrypted communication between customers and delivery personnel. Tech Stack: Flutter (Dart), Node.js, REST API, WebSocket, JWT, OAuth