标题：Combining Deep Learning with Rough Set Analysis: A Model of Cyberspace Situational Awareness
作者：Li, Xueyu; Li, Xiaocheng; Zhao, Zhenhua
作者机构：[Li, Xueyu; Li, Xiaocheng; Zhao, Zhenhua] Shandong Univ Sci & Technol, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China.
会议名称：IEEE 6th International Conference on Electronics Information and Emergency Communication (ICEIEC)
会议日期：JUN 17-19, 2016
来源：PROCEEDINGS 2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC)
关键词：cyberspace situational awareness; feature extraction; deep learning;; Gaussian-Bernoulli Deep Belief Network; situation assessment; rough set; analysis
摘要：A cyberspace situational awareness (CSA) model, DLRSA model, is proposed in this paper, with feature extraction based on deep learning (DL) and situation assessment based on rough set analysis (SARSA). We focus on network flow instead of server logs of IDS to extract features, transforming source data into information and establishing knowledge base. On account of Gaussian-Bernoulli deep belief network (GBDBN), accurate feature information is provided for assessment. While DLRSA model assesses situation in reference to pattern recognition, assessment strategy could be given by rough set analysis and pattern information abstracted deeply. Experiments indicate that DLRSA model has low extraction error and succinct assessment rule.