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. 2021 Feb 3;21(4):1026.
doi: 10.3390/s21041026.

Efficient Anomaly Detection for Smart Hospital IoT Systems

Abdel Mlak Said  1 , Aymen Yahyaoui  1   2 , Takoua Abdellatif  1
Affiliations

Efficient Anomaly Detection for Smart Hospital IoT Systems

Abdel Mlak Said et al. Sensors (Basel). .

Abstract

In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients' health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients' care and their environments' adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

Keywords: RPL; anomaly detection; event detection; internet of things; intrusion detection; machine learning; routing attacks; smart hospitals.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
CC debugger + USB dongle.
Figure A2
Figure A2
Patching the USB dongle through SmartRF Flash Programmer.
Figure A3
Figure A3
SmartRF06 evaluation board + TI CC2650 SensorTag.
Figure A4
Figure A4
Patching the SensorTag through SmartRF Flash Programmer v2.
Figure A5
Figure A5
BeagleBone Black: Home page.
Figure A6
Figure A6
BeagleBone Black: Sensors page.
Figure 1
Figure 1
Smart Hospital infrastructure.
Figure 2
Figure 2
SVM classification.
Figure 3
Figure 3
The rank in IoT network.
Figure 4
Figure 4
Rank attack.
Figure 5
Figure 5
The rank attack scenario.
Figure 6
Figure 6
Version number modification attack.
Figure 7
Figure 7
Flooding attack scenario.
Figure 8
Figure 8
Different environment sensor types.
Figure 9
Figure 9
Different body sensor types.
Figure 10
Figure 10
ADS architecture.
Figure 11
Figure 11
System architecture.
Figure 12
Figure 12
Simulation topology.
Figure 13
Figure 13
Power tracking per each mote for each attack.
See this image and copyright information in PMC

References

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    1. Attaluri P., Iqbal M., Lawrence C.D. Smart Hospital Care System. Application No. 13/445,299. U.S. Patent. 2013 Oct 13;

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