Overview
Developed an intelligent anomaly detection system that monitors network traffic patterns and identifies suspicious activities in real-time using unsupervised machine learning.
Key Features
- Real-time streaming analysis
- Unsupervised learning (no labeled data needed)
- Adaptive thresholds
- Multi-level anomaly scoring
- Automated incident response
Technical Stack
- ML Algorithms: Isolation Forest, Autoencoders, One-Class SVM
- Stream Processing: Apache Kafka, Apache Flink
- Deep Learning: TensorFlow, Keras
- Monitoring: Prometheus, Grafana
Results
- 96% detection rate for known attacks
- 78% detection rate for zero-day threats
- Less than 2% false positive rate
- Reduced mean time to detect (MTTD) by 65%
Implementation
Multi-layered detection approach:
Statistical Methods
- Z-score analysis for traffic volume
- Time series decomposition
- Moving average comparisons
Machine Learning
- Isolation Forest for outlier detection
- One-Class SVM for boundary learning
- DBSCAN for clustering analysis
Deep Learning
- LSTM Autoencoders for sequence anomalies
- Variational Autoencoders for reconstruction error
- Attention mechanisms for feature importance
Key capabilities:
- Learns normal behavior patterns automatically
- Adapts to network changes over time
- Provides explainable anomaly scores
- Integrates with SIEM systems
Monitors 100+ network devices and processes 1M+ events per minute with sub-second detection latency.