🔍 Neural Network Log Monitoring

🦾 A project by Jerry

Overview

This project aims to develop a neural network that learns from logs stored in a database. The model will help IT admins detect anomalies and gain insights through a dashboard.

🛡️ What the NN Learns

• Login anomalies – Detects accounts logging in from different locations within an impossible timeframe.
• Unusual request frequency – Frequent requests from a single IP could indicate a DoS attack.
• Distributed attacks – Multiple IPs sending similar requests in a short time (DDoS attack).
• Pattern recognition – Extracts insights from timestamps, IP addresses, and log descriptions.

🚀 Project Workflow

Each phase is crucial for developing an effective neural network:

1️⃣ Idea – Identify the problem and define objectives.
2️⃣ Data Collection – Gather logs from databases (timestamps, IPs, request types).
3️⃣ Preliminary Training – Test with a small dataset to understand trends.
4️⃣ Data Cleaning – Filter noise, handle missing values, and standardize log formats.
5️⃣ Algorithm Development – Select an NN architecture (e.g., LSTMs for sequence-based patterns).
6️⃣ Hyperparameter Tuning – Optimize layers, learning rates, and activation functions.
7️⃣ Full-scale Training – Train with extensive real-world logs.
8️⃣ Deployment – Integrate the trained model into a dashboard for real-time monitoring.

🏗️ Next Steps

✅ Implement a relational database for structured logs.
✅ Experiment with LSTMs and CNNs for anomaly detection.
✅ Develop an interactive dashboard to visualize anomalies.

💡 Stay tuned for updates! 🐐