Protecting Water Infrastructure From Cyber and Physical Threats Using multimodal data fusion and adaptive deep learning to monitor critical systems

被引：54

作者：

Bakalos, Nikolaos ^{[1
]}

Voulodimos, Athanasios ^{[2
]}

Doulamis, Nikolaos ^{[1
]}

Doulamis, Anastasios ^{[1
,3
]}

Ostfeld, Avi ^{[4
,5
]}

Salomons, Elad

Caubet, Juan ^{[6
,7
]}

Jimenez, Victor

Li, Pau

机构：

[1] NTUA, Athens, Greece

[2] Univ West Att, Athens, Greece

[3] Tech Univ Crete, Khania, Greece

[4] Technion Israel Inst Technol, Haifa, Israel

[5] Technion Israel Inst Technol, Fac Civil & Environm Engn, Haifa, Israel

[6] Informat Technol Secur Unit Eurecat, Eurecat, Spain

[7] INRIA, Nancy Grand Est Res Ctr, Le Chesnay, France

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2019年 / 36卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

SCADA; MODEL;

D O I：

10.1109/MSP.2018.2885359

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility. © 1991-2012 IEEE.

引用

页码：36 / 48

页数：13

共 30 条

[1] Trajectory-Based Surveillance Analysis: A Survey [J].