Advanced anomaly detection system for network traffic using unsupervised learning and real-time monitoring capabilities.
Objectives
The primary objective of the Network Anomaly Detection project is to develop and deploy an intelligent system capable of identifying unusual and potentially malicious activities within network traffic. By leveraging unsupervised learning, the system aims to detect novel threats and zero-day attacks that traditional signature-based methods might miss, ensuring robust network security.
Key Objective 1: Unsupervised Anomaly Detection
Develop and implement unsupervised machine learning models that can learn normal network behavior patterns and accurately flag deviations as anomalies without requiring pre-labeled attack data.
Key Objective 2: Real-time Monitoring & Alerting
Design a system capable of continuous, real-time monitoring of network traffic, providing immediate alerts upon detection of anomalous activities to facilitate rapid response.
Key Objective 3: Scalability & Low False Positives
Ensure the anomaly detection system is scalable to handle large volumes of network data and is optimized to minimize false positives, preventing alert fatigue for security analysts.
Methodology
Our methodology focuses on applying advanced unsupervised learning techniques, such as autoencoders, isolation forests, and clustering algorithms, to network flow data. The process involves continuous data ingestion, feature extraction, model training, and real-time inference.
Phase 1: Data Acquisition & Preprocessing
Collect raw network flow data (e.g., NetFlow, IPFIX) from various network segments. Preprocess and anonymize the data, and engineer relevant features for unsupervised learning models.
Phase 2: Unsupervised Model Development
Experiment with and develop various unsupervised learning models to establish baselines of normal network behavior. This involves iterative training and hyperparameter tuning to optimize anomaly detection capabilities.
Phase 3: Deployment, Integration & Refinement
Deploy the trained anomaly detection models into a live network environment. Integrate with existing security information and event management (SIEM) systems for alerting. Continuously refine models based on feedback and evolving network characteristics.
Expected Results & Impact
The Network Anomaly Detection project is expected to significantly enhance an organization's capability to detect sophisticated and unknown cyber threats. By providing a proactive layer of defense, it will help prevent data breaches, reduce network downtime, and improve the overall resilience of critical IT infrastructure. This project will contribute to a more secure and reliable network environment, allowing organizations to operate with greater confidence.
Project Team
- Dr. Emmanuel Ahene (Principal Investigator)
- Hoke Ishmael (MPhil Candidate, Network Security Analyst)