A5071697357- Network Security and Intrusion Detection
- Internet Traffic Analysis and Secure E-voting
- Advanced Malware Detection Techniques
- Software-Defined Networks and 5G
Purple Mountain Laboratories
2020-2022
Southeast University
2022
Many efforts have been devoted to the development of efficient Network Intrusion Detection System (NIDS) using machine learning approaches in Software-defined (SDN). Unfortunately, existing solutions failed detect real-time and zero-day attacks due their limited throughput prior knowledge-based detection. To this end, we propose Griffin, a NIDS that uses unsupervised expertise both known intrusion with high accuracy. Specifically, Griffin an feature extraction framework capture sequential...
The Network Intrusion Detection Systems (NIDS) with machine learning in SDN become increasingly popular solutions. NIDS uses abnormal traffic detection to identify unknown network attacks. Most of today's systems are supposed continuously update the recognition model time based on features from newly collected packets accurately attack behaviors. However, those existing solutions always require a large number train offline. That means it is impossible detect emergence new cyber-attacks...