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Arthur DANJOU
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- Created CLAUDE.md to provide development commands, architecture overview, and environment variables for the Nuxt 3 portfolio website. - Refactored project pages to remove unused color mappings and improve project filtering logic. - Updated content.config.ts to enforce stricter project type definitions and added short descriptions for projects. - Deleted outdated project files and added new projects related to hackathons and academic research. - Enhanced existing project descriptions with short summaries for better clarity.
56 lines
3.2 KiB
Markdown
56 lines
3.2 KiB
Markdown
---
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slug: hackathon-cnd
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title: "CND Hackathon: Defense-Grade Log Intelligence"
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type: Hackathon
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description: A high-stakes cybersecurity challenge organized by the French Ministry of Defense (CND). Representing Université Paris-Dauphine, our team spent 3 days in a high-security military fortress developing ML models to detect stealthy cyber threats in firewall logs.
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shortDescription: Cybersecurity threat detection within a high-security military environment.
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publishedAt: 2025-10-28
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readingTime: 4
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status: completed
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tags:
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- Python
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- Streamlit
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- Cybersecurity
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- Machine Learning
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- Scikit-learn
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icon: i-ph-shield-check-duotone
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---
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## The Setting: Fort de Mont-Valérien
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This wasn't your typical university hackathon. Organized by the **Commissariat au Numérique de Défense (CND)**, the event took place over three intense days within the walls of the **Fort de Mont-Valérien**—a highly secured military fortress.
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Working in this environment underscored the real-world stakes of the mission. Our **team of six**, representing **Université Paris-Dauphine**, competed against several elite engineering schools to solve critical defense-related data challenges.
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## The Mission: Classifying the "Invisible"
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The core task involved processing poorly labeled and noisy firewall logs. In a defense context, a "missing" log or a mislabeled entry can be the difference between a minor system bug and a coordinated intrusion.
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### 1. Tactical Log Translation
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Firewall logs are often cryptic and inconsistent. We developed a preprocessing pipeline to:
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* **Feature Extraction:** Parse raw logs into structured data (headers, flags, payloads).
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* **Contextual Labeling:** Distinguish between routine system "bugs" (non-malicious failures) and actual "attacks" (malicious intent).
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### 2. Strategic Goal: Recalling the Threat
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In military cybersecurity, the cost of a **False Negative** (an undetected attack) is catastrophic.
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* **Model Priority:** We optimized our classifiers specifically for **Recall**. We would rather investigate a few system bugs (False Positives) than let a single attack slip through the net.
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* **Techniques:** We used ensemble methods (XGBoost/Random Forest) combined with advanced resampling to handle the heavy class imbalance typical of network traffic.
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> **Key Achievement:** Our model significantly reduced the rate of undetected threats compared to standard baseline configurations provided at the start of the challenge.
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## Deployment & Interaction
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To make our findings operational, we built a **Streamlit-based command center**:
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* **On-the-Fly Analysis:** Security officers can paste a single log line to get an immediate "Bug vs. Attack" probability score.
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* **Bulk Audit:** The interface supports CSV uploads, allowing for the rapid analysis of entire daily log batches to highlight high-risk anomalies.
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## Technical Stack
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* **Language:** Python
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* **ML Library:** Scikit-learn, XGBoost
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* **Deployment:** Streamlit
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* **Environment:** High-security on-site military infrastructure
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---
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Representing Dauphine in such a specialized environment was a highlight of my academic year. Would you like me to elaborate on the specific feature engineering techniques we used to "clean" the raw military logs?
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