Distributed Transfer Network Learning Based Intrusion Detection

Abstract

In order to solve the problem that there exists unbalanced detection performance on different types of attacks in current large-scale network intrusion detection algorithms, Distributed Transfer Network Learning algorithm is proposed in this paper. The algorithm introduces transfer learning into Distributed Network Boosting algorithm for instructing the attacks learning with poor performance, in which the instances transfer learning is adopted for different domain adaptation. The experimental results on the Kdd Cup’99 Data Set show that the proposed algorithm has higher efficacy and better performance. Further, the detection accuracy of R2L attacks has been improved greatly while maintaining higher detection accuracy of other attack types.