图书馆 | 所内网 | 所长信箱 | English | 中国科学院
站内搜索  
 
首 页 新闻 机构概况 机构设置 科研成果 研究队伍 研究生教育 国际交流 院地合作 学术期刊 创新文化 党群园地 科学传播
 
科研成果
概况介绍
论文
专著
专利
成果转化
研究所图库
园区一角南区办公楼实验楼(R楼)园区一角科研楼(A楼)自动化所鸟瞰自动化所正门
相关链接
ARP Email 所报
 您现在的位置:首页 > 科研成果 >论文
论文题目: Double Behavior Characteristics for One-Class Classification Anomaly Detection in Networked Control Systems
第一作者: Wan M(万明);Shang WL(尚文利);Zeng P(曾鹏)
参与作者:
联系作者:
发表刊物: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
发表年度: 2017
卷,期,页: 12,12,3011-3023
论文出处:
第一作者所在部门:
论文编号:
论文摘要: Due to the growing dependencies of information network technology, networked control systems are undergoing a severe blow of cyberattacks, and simply modeling cyberattacks is inadequate and impractical for the detection requirements, because of various vulnerabilities in these systems and the diversities of cyberattacks. Actually, a feasible viewpoint is to identify misbehaviors by constructing a normal model of industrial communication behaviors. However, one of the chief difficulties is how to completely and appropriately summarize industrial communication behaviors according to the specific communication characteristics. In view of process control and data acquisition, this paper associates industrial communication characteristics with the time sequence, and further extracts two distinct behaviors: function control behavior and process data behavior. Based on these double behavior characteristics, we introduce one-class classification to detect the corresponding anomalies, respectively. Besides, we also present the weighted mixed Kernel function and parameter optimization method to improve classification performance. Experimental results clearly demonstrate that the proposed approach has significant advantages of classification accuracy and detection efficiency.
论文全文:
其他备注:
附件下载:
 
中国科学院沈阳自动化研究所 版权所有 1996-2009 辽ICP备05000867 联系我们
地址:中国辽宁省沈阳市东陵区南塔街114号 邮编:110016 留言反馈 网站地图