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Anonymous traffic classification based on three-dimensional Markov image and deep learning

Tang Xin, Li Huanzhou, Zhang Jian, Tang Zhangguo, Wang Han, Cai Cheng

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 4

art. no. e145676

Illegal elements use the characteristics of an anonymous network hidden service mechanism to build a dark network and conduct various illegal activities, which brings a serious challenge to network security. The existing anonymous traffic classification methods suffer from cumbersome feature selection and difficult feature information extraction, resulting in low accuracy of classification. To solve this problem, a classification method based on three-dimensional Markov images and output self-attention convolutional neural network is proposed. This method first divides and cleans anonymous traffic data packets according to sessions, then converts the cleaned traffic data into three-dimensional Markov images according to the transition probability matrix of bytes, and finally inputs the images to the output self-attention convolution neural network to train the model and perform classification. The experimental results show that the classification accuracy and F1-score of the proposed method for Tor, I2P, Freenet, and ZeroNet can exceed 98.5%, and the average classification accuracy and F1-score for 8 kinds of user behaviors of each type of anonymous traffic can reach 93.7%. The proposed method significantly improves the classification effect of anonymous traffic compared with the existing methods.

Influence of accelerometer signal preprocessing and classification method on human activity recognition

Kupryjanow A., Kaszuba K., Czyżewski A.

Elektronika : konstrukcje, technologie, zastosowania

2010

Vol. 51, nr 3

18-23

A study of data preprocessing influence on accelerometer-based human activity recognition algorithms is presented. The frequency band used to filter-out the accelerometer signals and the number of accelerometers involved were considered in terms of their influence on the recognition accuracy. In the tests four methods of classification were used: support vector machine, decision trees, neural network, k-nearest neighbor.

W artykule przedstawiono wpływ przetwarzania wstępnego sygnału przyspieszenia na skuteczność rozpoznawania aktywności ruchowych. Przeanalizowano zależność filtracji sygnałów oraz ilości zastosowanych czujników na skuteczność klasyfikacji. W badaniach wykorzystano cztery różne klasyfikatory: maszynę wektorów wsparcia, drzewa decyzyjne, sztuczne sieci neuronowe oraz klasyfikator najbliższego sąsiada.