Abstract: For malicious behaviors existing in the Internet, especially online malicious user behavior detection in social network applications, clustering analysis algorithms based on multi-dimensional user characteristics are usually used for detection. This paper proposes a dynamic feature selection algorithm (DFSA), which uses a fuzzy C-means objective function with feature weighted entropy. Firstly, a learning mode is constructed for the parameters, and each feature weight is automatically calculated, and features whose weight is less than the threshold are eliminated. Important feature components are selected dynamically, and the membership function, cluster center and feature weights are updated iteratively until the optimization is achieved. Finally, malicious user behavior clusters with high accuracy is detect- ed. The simulation results show that the proposed algorithm outperforms the SDAFS algorithm, the ELAFC algorithm and the NADMB algorithm in terms of three main performance indicators such as Rand index, Jaccard index and normalized mutual information.    

Key words: feature selection, malicious user behavior, online social network, fuzzy clustering ,