Hybrid fuzzy integrated convolutional neural network (HFICNN) for similarity feature recognition problem in abnormal netflow detection

Abstract

This paper presents a hybrid fuzzy integrated convolutional neural network (HFICNN) to deal with the similarity feature recognition problem in abnormal netflow detection, which is that different labels with the same netflow eigenvalues are not well disposed by traditional machine learning and deep learning methods. The HFICNN model can convert the same feature individuals with different labels to different feature individuals with different labels by constructing an integration method. Our method recognizes the actual environment and operational complexity, and converts the unique occurrence time feature performance as a new feature for feature recognition. HFICNN integrates time-dependent records into a new record to identify a feature, and converts the integrated records to the original label. The integration process plays a good role and effect in the entire process. HFICNN is realized with the aid of function mapping label redefinition (FMLR), adaptive feature integration convolutional neurons (AFICNs), fuzzy classification (FC), and inverse function mapping integrated reduction (IFMIR). The ICMPv6-based DDoS attacks dataset of a new-generation network is tested, and experimental results show that HFICNN performs better than 10 types of traditional machine learning and two types of deep learning methods on the similarity feature recognition problem, and the HFICNN model is reliable and effective.