AI-Driven Detection of Network Traffic Anomalies: A Case Study with OMNeT++
This research presents an AI-driven approach to detecting network traffic anomalies using machine learning techniques implemented and validated through OMNeT++ network simulation framework. The study focuses on developing intelligent systems capable of identifying unusual patterns in network traffic that may indicate security threats, performance issues, or system malfunctions. The proposed methodology demonstrates effective anomaly detection capabilities in simulated network environments, contributing to cybersecurity and network management applications.
AI-Driven Detection of Network Traffic Anomalies: A Case Study with OMNeT++
This research presents an AI-driven approach to detecting network traffic anomalies using machine learning techniques implemented and validated through OMNeT++ network simulation framework. The study focuses on developing intelligent systems capable of identifying unusual patterns in network traffic that may indicate security threats, performance issues, or system malfunctions. The proposed methodology demonstrates effective anomaly detection capabilities in simulated network environments, contributing to cybersecurity and network management applications.
Key Highlights
- AI-driven approach to detecting network traffic anomalies using ML techniques
- Implemented and validated through OMNeT++ network simulation framework
- Intelligent systems capable of identifying unusual patterns in network traffic
- Effective anomaly detection capabilities contributing to cybersecurity applications
Technologies Used
- Network Traffic Anomaly Detection - Unusual pattern identification in network data
- Artificial Intelligence - AI-based detection systems
- OMNeT++ - Network simulation framework
- Network Security - Cybersecurity threat detection
- Machine Learning - Pattern recognition algorithms
- Cybersecurity - Information security applications
- Network Simulation - Simulated network environment testing
- Intrusion Detection - Security threat identification systems
Read the full research paper on ResearchGate!