Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network

Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propose a MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, the packet is divided into the packet header and the packet body, together with the flow features of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. Taking advantage of the above characteristics, we propose an end-to-end network traffic classification method. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance.Comment: 15 pages,6 figure

Interface Physical Influence on Mechanic Properties of PP/AS Modified Polymer Caused by Maleic Anhydride

Polypropylene (PP) is a widely used polymer matter, which has many advantages, such as rich sources, simple synthesis process, small density, low cost, easy processing and molding. At present, there has been a lot of progress in the research of polypropylene modified blending, including PP/ABS system, PP/SBS system, etc. However, the research on PP/AS blend is relatively few, and the experimental formula range is large, so it is difficult to determine the optimal ratio. On the basis of previous experiments, this study further narrowed the proportion range. Through AS modifying PP, the interface physical effect of maleic anhydride addition on the mechanical properties of the blend was discussed, and the optimal proportion was found to obtain the modified PP resin with significantly improved mechanical properties.Comment: 8 page

Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection

Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. In this paper, we propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF). Most forgery technologies always bring in high-frequency aware cues, which make it easy to distinguish source authenticity but difficult to generalize to unseen artifact types. The masked frequency forgery representation module is designed to explore robust forgery cues by randomly discarding high-frequency information. In addition, we find that the forgery attention map inconsistency through the detection network could affect the generalizability. Thus, the forgery attention consistency is introduced to force detectors to focus on similar attention regions for better generalization ability. Experiment results on several public face forgery datasets (FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.Comment: The source code and models are publicly available at https://github.com/chenboluo/ACM