Machine Learning Based Rank Attack
Detection for Smart Hospital
Infrastructure
Abd Mlak Said
1(B) , Aymen Yahyaoui
1,2 , Faicel Yaakoubi
3 ,
and Takoua Abdellatif
2
1 Military Academy of Fondouk Jedid, Nabul, Tunisia
maliksaid@outlook.fr
2 SERCOM Lab, Polytechnic School of Tunisia, La Marsa, Tunisia
3 Defense Science and Technology Laboratory, Tunis, Tunisia
Abstract. In recent years, many technologies were racing to deliver
the best service for human being. Emerging Internet of Things (IoT)
technologies made birth to the notion of smart infrastructures such as
smart grid, smart factories or smart hospitals. These infrastructures rely
on interconnected smart devices collecting real-time data in order to
improve existing procedures and systems capabilities. A critical issue in
smart infrastructures is the information protection which may be more
valuable than physical assets. Therefore, it is extremely important to
detect and deter any attacks or breath to the network system for information theft. One of these attacks is the rank attack that is carried out
by an intruder node in order to attract legitimate traffic to it, then steal
personal data of different persons (both patients and staffs in hospitals).
In this paper, we propose an anomaly based rank attack detection system
against an IoT network using Support Vector Machines. As a use case,
we are interested in the healthcare sector and in particular in smart
hospitals which are multifaceted with many challenges such as service
resilience, assets interoperability and sensitive information protection.
The proposed intrusion detection system (IDS) is implemented and evaluated using Conticki Cooja simulator. Results show a high detection
accuracy and low false positive rates.
Keywords: Internet of Things · Smart hospitals · Intrusion
detection · Rank attack · Machine learning · RPL
1 Introduction
Nowadays, the deployment of the Internet of Things (IoT) where many objects
are connected to the Internet cloud services becomes highly recommended in
many applications in various sectors. A highly important concept in the IoT is
Wireless Sensor Networks or WSNs where end nodes rely on sensors that can collect data from the environment to ensure tasks such as surveillance or monitoring
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 28–40, 2020.
https://doi.org/10.1007/978-3-030-51517-1_3
Detection for Smart Hospital
Infrastructure
Abd Mlak Said
1(B) , Aymen Yahyaoui
1,2 , Faicel Yaakoubi
3 ,
and Takoua Abdellatif
2
1 Military Academy of Fondouk Jedid, Nabul, Tunisia
maliksaid@outlook.fr
2 SERCOM Lab, Polytechnic School of Tunisia, La Marsa, Tunisia
3 Defense Science and Technology Laboratory, Tunis, Tunisia
Abstract. In recent years, many technologies were racing to deliver
the best service for human being. Emerging Internet of Things (IoT)
technologies made birth to the notion of smart infrastructures such as
smart grid, smart factories or smart hospitals. These infrastructures rely
on interconnected smart devices collecting real-time data in order to
improve existing procedures and systems capabilities. A critical issue in
smart infrastructures is the information protection which may be more
valuable than physical assets. Therefore, it is extremely important to
detect and deter any attacks or breath to the network system for information theft. One of these attacks is the rank attack that is carried out
by an intruder node in order to attract legitimate traffic to it, then steal
personal data of different persons (both patients and staffs in hospitals).
In this paper, we propose an anomaly based rank attack detection system
against an IoT network using Support Vector Machines. As a use case,
we are interested in the healthcare sector and in particular in smart
hospitals which are multifaceted with many challenges such as service
resilience, assets interoperability and sensitive information protection.
The proposed intrusion detection system (IDS) is implemented and evaluated using Conticki Cooja simulator. Results show a high detection
accuracy and low false positive rates.
Keywords: Internet of Things · Smart hospitals · Intrusion
detection · Rank attack · Machine learning · RPL
1 Introduction
Nowadays, the deployment of the Internet of Things (IoT) where many objects
are connected to the Internet cloud services becomes highly recommended in
many applications in various sectors. A highly important concept in the IoT is
Wireless Sensor Networks or WSNs where end nodes rely on sensors that can collect data from the environment to ensure tasks such as surveillance or monitoring
c
The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 28–40, 2020.
https://doi.org/10.1007/978-3-030-51517-1_3