QQ客服 官方微博 反馈留言

	Detecting Time Synchronization Attacks in Cyber-Physical Systems with Machine Learning Techniques
	Wang, Jingxuan1; Tu, Wenting2; Hui, Lucas C. K.1; Yiu, S. M.1; Wang, Eric Ke3
	2017
DOI	10.1109/ICDCS.2017.25
页码	2246-2251
英文摘要	Recently, researchers found a new type of attacks, called time synchronization attack (TS attack), in cyber-physical systems. Instead of modifying the measurements from the system, this attack only changes the time stamps of the measurements. Studies show that these attacks are realistic and practical. However, existing detection techniques, e.g. bad data detection (BDD) and machine learning methods, may not be able to catch these attacks. In this paper, we develop a "first difference aware" machine learning (FDML) classifier to detect this attack. The key concept behind our classifier is to use the feature of "first difference", borrowed from economics and statistics. Simulations on IEEE 14-bus system with real data from NYISO have shown that our FDML classifier can effectively detect both TS attacks and other cyber attacks.
会议录出版者	IEEE COMPUTER SOC
会议录出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000412759500228
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3357]
专题	上海财经大学
作者单位	1.Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China; 2.Shanghai Univ Finance & Econ, Dept Comp Sci, Shanghai, Peoples R China; 3.Shenzhen Grad Sch, Harbin Inst Technol, Shenzhen, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jingxuan,Tu, Wenting,Hui, Lucas C. K.,et al. Detecting Time Synchronization Attacks in Cyber-Physical Systems with Machine Learning Techniques[C]. 见:.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。