Fiden: Intelligent Fingerprint Learning for Attacker Identification in the Industrial Internet of Things
作者：
Chen, Yuanfang*;Hu, Weitong;Alam, Muhammad;Wu, Ting
通讯作者：
Chen, Yuanfang
作者机构：
School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
通讯机构：
Hangzhou Dianzi Univ, Sch Cyberspace, Hangzhou 310018, Peoples R China.
语种：
英文
关键词：
Security;Industries;Feature extraction;Protocols;Voltage measurement;SCADA systems;Attacker identification;fingerprinting devices;Industrial Internet of Things (IIoT);Internet of Things Security
期刊：
IEEE Transactions on Industrial Informatics
ISSN：
1551-3203
年：
2021
卷：
17
期：
2
页码：
882-890
基金类别：
10.13039/501100001809-National Natural Science Foundation of China;Project of Qianjiang Talent (Grant Number: 61802097 and QJD1802020)
摘要：
This article studies the attacker identification issue in the Industrial Internet of Things (IIoT). There have been already some work that uses device fingerprinting to identify attackers, and the transmission offset of the device internal clock signals is used as the device's fingerprint. However, the existing work to measure the offset relies on the periodic transmission of signals, but in many types of IIoT devices, the signal transmission is aperiodic. To eliminate the limitation on the periodicity, in this article, we design an algorithm, Fiden, to fingerprint heterogeneous IIoT devices without considering the periodicity. This algorithm extracts the patterns from th...
