Evidence map›Paper›PMID 29065461›Full record

ArticleSensors (Basel, Switzerland)2017

Command Disaggregation Attack and Mitigation in Industrial Internet of Things.

Peng Xun, Pei-Dong Zhu, Yi-Fan Hu, Peng-Shuai Cui, Yan Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Peng XunCollege of Computer, National University of Defense Technology, Changsha 410073, China. xunpeng12@nudt.edu.cn.ORCID 0000-0002-5225-6118
Pei-Dong ZhuDepartment of Electronic Information and Electrical Engineering, Changsha University, Changsha 410022, China. peidong_nudt@163.com.
Yi-Fan HuCollege of Computer, National University of Defense Technology, Changsha 410073, China. ouchyf@foxmail.com.
Peng-Shuai CuiCollege of Computer, National University of Defense Technology, Changsha 410073, China. cuipengshuai@nudt.edu.cn.
Yan ZhangDepartment of Informatics, University of Oslo, Oslo 0316, Norway. yanzhang@ieee.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

attack detectioncommand correlationcommand disaggregationcyber-physical attackindustrial Internet of Things

Identifiers

PMID29065461
PMCPMC5677416

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.