Network-Aware SDN Load Balancer with Deep Active Learning based Intrusion Detection Model
Usman Iqbal Ahmed, Jerry Chun‐Wei Lin, Gautam Srivastava · 2021
In this paper, we propose a load balancing algorithm that optimizes the sensor usability by using sensor computing capability and source needs for intrusion detection, which is able to track the attack on the network. This paper combines the entropy-based active learning model to identify intrusion patterns efficiently. From the results, we can see that the designed model can help and improve the decision boundary by increasing the training instance through pooling strategy and entropy uncertainty measure.