🚀 GSoC 2026: Multi-Track Preparation Journey

Welcome! This repository documents my technical evolution as I prepare for GSoC 2026. I am currently focused on Cybersecurity, with parallel tracks in Big Data and AI.

🛤️ Strategy & Tracks

🛡️ Track A: Cyber Security (Active)

• Target: OWASP Python Honeypot
• Status: Analyzing core architecture, environment constraints, and log data for anomalies.

📊 Track B: Big Data & Infra (Active)

• Target: Distributed systems and high-throughput data processing
• Status: Explored Kafka message acknowledgments and log integrity concepts.

🤖 Track C: AI & LLM Integration (Active)

• Goal: AI-driven log anomaly detection and analysis using Isolation Forest.

📈 Consolidated Progress Dashboard

Date	Track	Focus Topic	Key Accomplishments	Detailed Log
Dec 22	🛠️ Foundation	Environment & Git	Mastered Linux CLI, Git Rebase, and PDB theory.	Day 1
Dec 23	🛡️ Security	Codebase Tracing	Verified ohp.py entry point; Traced startup flow via PDB; Identified dependency constraints.	Day 2
Jan 1	🛡️/📊/🤖 Multi-Track	Security, Big Data & AI	✅ Docker handled Windows environment limitations; ✅ Learned Kafka ACK importance for log reliability; ✅ Fetched honeypot logs and applied Isolation Forest; ✅ Handled KeyError and empty DataFrame issues; ✅ Generated screenshots for logs and anomalies.	Day 3

🛠️ Technical Insights (Key Milestones)

🛡️ Security Track

Jan 1, 2026

• Docker Advantage 🐳: Solved dependency/version issues that failed on Windows natively.
• Observation 👀: Running the honeypot without Docker triggered ModuleNotFoundError and Flask/Werkzeug conflicts.
• Resolution ✅: Recognized that missing external services (Elasticsearch) are environment constraints, not bugs.

Dec 23–24, 2025 (OWASP Python Honeypot – Reference Work)

• Dynamic Tracing 🕵️‍♀️: Moved from static code reading to live execution tracing using PDB.
• Logic Verification 🔍: Identified that elasticsearch connection failure is an intentional sys.exit(1) design, not a random crash.

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📊 Big Data Track

Jan 1, 2026

• Kafka ACK Mechanism 📬: Learned that ACK ensures log messages are reliably stored and not lost.
• Impact 💡: Understanding ACK guarantees helps maintain log integrity when feeding data into AI models.

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🤖 AI Track

Jan 1, 2026

• Data Pipeline 🗂️: Fetched honeypot logs from Elasticsearch into pandas DataFrame.

• Feature Engineering 🔧:

  • Converted event to numeric labels.
  • Converted timestamp to numeric for modeling.

• Anomaly Detection 🕵️‍♂️: Applied Isolation Forest on numeric features to flag anomalies.

• Bugs & Solutions 🐞:

  • KeyError: 'event_type' → column did not exist, switched to event.
  • Empty DataFrame initially → added a synthetic attack_log to test workflow.

• Visualization 📸: Screenshots captured:

  • day3_df_preview.png — preview of loaded logs.
  • day3_anomaly.png — rows flagged as anomalies.

📬 Contact & Open Source Profile

• Current Status: Multi-track progress on OWASP Honeypot logs, Kafka concepts, and AI anomaly detection.
• GitHub: chenjingen-jane

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"Reading code is like reading a map; debugging is like walking the path."