Command Disaggregation Attack and Mitigation in Industrial Internet of Things
A cyber-physical attack in the industrial Internet of Things can cause severe damage to physical system. In this paper, we focus on the command disaggregation attack, wherein attackers modify disaggregated commands by intruding command aggregators like programmable logic controllers, and then malici...
Main Authors: | , , , , |
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MDPI AG
2017-10-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/17/10/2408 |
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author | Peng Xun Pei-Dong Zhu Yi-Fan Hu Peng-Shuai Cui Yan Zhang |
author_facet | Peng Xun Pei-Dong Zhu Yi-Fan Hu Peng-Shuai Cui Yan Zhang |
author_sort | Peng Xun |
collection | DOAJ |
description | A cyber-physical attack in the industrial Internet of Things can cause severe damage to physical system. In this paper, we focus on the command disaggregation attack, wherein attackers modify disaggregated commands by intruding command aggregators like programmable logic controllers, and then maliciously manipulate the physical process. It is necessary to investigate these attacks, analyze their impact on the physical process, and seek effective detection mechanisms. We depict two different types of command disaggregation attack modes: (1) the command sequence is disordered and (2) disaggregated sub-commands are allocated to wrong actuators. We describe three attack models to implement these modes with going undetected by existing detection methods. A novel and effective framework is provided to detect command disaggregation attacks. The framework utilizes the correlations among two-tier command sequences, including commands from the output of central controller and sub-commands from the input of actuators, to detect attacks before disruptions occur. We have designed components of the framework and explain how to mine and use these correlations to detect attacks. We present two case studies to validate different levels of impact from various attack models and the effectiveness of the detection framework. Finally, we discuss how to enhance the detection framework. |
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id | doaj.art-0fc713bc5b1c46c99f0f91237469aee6 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-13T00:29:41Z |
publishDate | 2017-10-01 |
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spelling | doaj.art-0fc713bc5b1c46c99f0f91237469aee62022-12-22T03:10:31ZengMDPI AGSensors1424-82202017-10-011710240810.3390/s17102408s17102408Command Disaggregation Attack and Mitigation in Industrial Internet of ThingsPeng Xun0Pei-Dong Zhu1Yi-Fan Hu2Peng-Shuai Cui3Yan Zhang4College of Computer, National University of Defense Technology, Changsha 410073, ChinaDepartment of Electronic Information and Electrical Engineering, Changsha University, Changsha 410022, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer, National University of Defense Technology, Changsha 410073, ChinaDepartment of Informatics, University of Oslo, Oslo 0316, NorwayA cyber-physical attack in the industrial Internet of Things can cause severe damage to physical system. In this paper, we focus on the command disaggregation attack, wherein attackers modify disaggregated commands by intruding command aggregators like programmable logic controllers, and then maliciously manipulate the physical process. It is necessary to investigate these attacks, analyze their impact on the physical process, and seek effective detection mechanisms. We depict two different types of command disaggregation attack modes: (1) the command sequence is disordered and (2) disaggregated sub-commands are allocated to wrong actuators. We describe three attack models to implement these modes with going undetected by existing detection methods. A novel and effective framework is provided to detect command disaggregation attacks. The framework utilizes the correlations among two-tier command sequences, including commands from the output of central controller and sub-commands from the input of actuators, to detect attacks before disruptions occur. We have designed components of the framework and explain how to mine and use these correlations to detect attacks. We present two case studies to validate different levels of impact from various attack models and the effectiveness of the detection framework. Finally, we discuss how to enhance the detection framework.https://www.mdpi.com/1424-8220/17/10/2408cyber-physical attackindustrial Internet of Thingscommand disaggregationcommand correlationattack detection |
spellingShingle | Peng Xun Pei-Dong Zhu Yi-Fan Hu Peng-Shuai Cui Yan Zhang Command Disaggregation Attack and Mitigation in Industrial Internet of Things Sensors cyber-physical attack industrial Internet of Things command disaggregation command correlation attack detection |
title | Command Disaggregation Attack and Mitigation in Industrial Internet of Things |
title_full | Command Disaggregation Attack and Mitigation in Industrial Internet of Things |
title_fullStr | Command Disaggregation Attack and Mitigation in Industrial Internet of Things |
title_full_unstemmed | Command Disaggregation Attack and Mitigation in Industrial Internet of Things |
title_short | Command Disaggregation Attack and Mitigation in Industrial Internet of Things |
title_sort | command disaggregation attack and mitigation in industrial internet of things |
topic | cyber-physical attack industrial Internet of Things command disaggregation command correlation attack detection |
url | https://www.mdpi.com/1424-8220/17/10/2408 |
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