Bigsids: an efficient SDN-based network intrusion detection systems for big data environments

Bigsids: an efficient SDN-based network intrusion detection systems for big data environments

연구 분야: Databases

학회: Cluster Computing

Software-defined networking (SDN) offers promising network solutions in a big data environment, but existing network intrusion detection systems (NIDS) are limited in handling the high volume of network traffic data. To address this challenge, we propose an SDN-based architecture designed for efficient big data analysis and enhanced monitoring, seamlessly integrating NIDS. The attack detector of our approach is a hybrid model leveraging the advances of both machine and deep learning paradigms with big data processing technologies; thus, it ensures a high processing rate and accuracy in detecting and classifying cyber attacks. The evaluation results on four popular NIDS datasets show that our system could detect several attacks with an accuracy rate of 99% and maintain a minimal false alarm rate of 0.35%. In addition, in a simulated distributed environment, our proposal could process over 40,000 flows per second using just five worker nodes.