期刊
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
卷 52, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jisa.2020.102465
关键词
Dynamic CNN; Fake profile; Online social network; Features classification
资金
- Institute of Research in Information Processing Laboratory, Harbin University of Science and Technology under CSC Scholarship
Online Social Networks (OSN) are popular applications for sharing various data, including text, photos, and videos. However, fake account problems are one of the obstacles in the current OSN systems. Attacker exploits fake accounts to distribute misleading information such as malware, virus, or malicious URLs. Inspired by the big successes of deep learning in computer vision, mainly in automatic feature extraction and representation, we propose DeepProfile, a deep neural network (DNN) algorithm to deal with fake account issues. Instead of using standard machine learning, we construct a dynamic CNN to train a learning model in fake profile classification. Notably, we propose a novel pooling layer to optimize the neural network performance in the training process. Demonstrated by the experiments, we harvest a promising result with better accuracy and small loss than common learning algorithms in a malicious account classification task. (C) 2020 Elsevier Ltd. All rights reserved.
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