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Project-Sonic

Real-Time VPN Anomaly Detection System

Technologies

Python Scikit-learn Apache Kafka Redis Isolation Forest LSTM Networks Stream Processing

Status Production Ready

Category Cybersecurity & Network Monitoring

Project Overview

Project-Sonic is a high-performance anomaly detection system designed for real-time VPN network traffic analysis. Using advanced machine learning algorithms and stream processing, it identifies sophisticated cyber threats including credential stuffing, data exfiltration, and zero-day exploits with minimal latency.

Logs/Hour

500ms

Detection Latency

75%

Fewer False Positives

Key Features

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Real-Time Processing

High-throughput stream processing with Apache Kafka handling millions of log entries per hour

🧠

Advanced ML Algorithms

Ensemble of Isolation Forest, One-Class SVM, and LSTM networks for comprehensive threat detection

🎯

Behavioral Profiling

Adaptive baseline establishment and anomaly scoring based on user behavior patterns

📊

Interactive Dashboard

Real-time visualization of threat landscape with Plotly charts and trend analysis

🔗

Automated Response

Integration with security policies and firewall rules for immediate threat mitigation

🔄

Self-Learning

Online learning capabilities improving detection accuracy over time through continuous adaptation

Technical Implementation

$ Developed high-fidelity simulation of real-time anomaly detection system for VPN network traffic analysis

$ Implemented unsupervised machine learning algorithms including Isolation Forest, One-Class SVM, and Local Outlier Factor for threat detection

$ Created stream processing pipeline using Apache Kafka handling high-volume log data with sub-100ms latency

$ Built comprehensive feature engineering system extracting 50+ relevant patterns from raw VPN connection logs

$ Engineered baseline profiling mechanism establishing normal behavior patterns for each user using statistical methods

$ Developed adaptive threshold system automatically adjusting sensitivity based on network conditions and time-of-day patterns

$ Implemented alert prioritization engine scoring anomalies by severity, confidence, and potential security impact

$ Created interactive visualization dashboard using Plotly displaying real-time threat landscape and anomaly trends

$ Built forensic analysis tools for investigating flagged connections and reconstructing attack chains with timeline visualization

$ Developed automated response integration triggering security policies and firewall rules for high-risk anomalies

$ Implemented LSTM neural networks for sequence-based anomaly detection identifying complex attack patterns

$ Created correlation engine linking related anomalies to identify coordinated attacks

$ Built data preprocessing pipeline handling missing values, outliers, and data normalization

$ Developed model retraining system using online learning to adapt to evolving network patterns

$ Implemented Redis caching layer for rapid feature lookup and anomaly scoring