IoT Security 265
node for all robots and responsible for connectivity between robots and mobile
networks. The first level of data monitoring and security analysis is done in LRC before
data is forwarded to mobile network. The IoT service provider can be either a mobile
network operator or a Mobile Virtual Network Operator (MVNO). The MVNO carries
out advanced analysis of collected data from various LRCs through data‐mining mechanisms in an IoT anomaly detection module. The IoT anomaly detection module includes
linear and learning algorithms. The linear data‐mining algorithm will extract and provide
proper attributes to the next process that uses a learning algorithm. This mechanism
would decrease the load of input data for the learning algorithm, which is the most
time‐consuming part (because of the algorithm complexity). So, the proposed algorithm could label new attacks and detect known ones (e.g. botnet, malware) faster than
the existing solutions. The analyzed data would be used for intrusion detection in the
same or a different MVNO. While a robot has been identified as malicious in an MVNO,
a notification will be sent to all MVNOs by an IoT orchestrator to block the malicious
robot and prevent the re‐analysis of the malicious robot in future, since the malicious
robot may try to attack the cloud platform using other MVNOs. Therefore, the distributed security platform reduces the computation processing for a malicious robot that
has been already identified as malicious in other MVNOs.
References
1 Nokia Networks (2016) 5G masterplan – five keys to create the new communications era.
White Paper, C401‐011949‐WP‐201601‐1‐EN.
2 Monshizadeh, M., Khatri, V., Kantola, R. and Yan, Z. (2017) An orchestrated security
platform for internet of robots. Proceedings of The 12th International Conference on
Green, Pervasive and Cloud Computing (GPC) [accepted].
3 Ian, C. et al. (2016) Automated Drone Systems. Patent US2016266579, September 15, 2016.
4 Daniel, O. (2016) Drone Docking Station and Delivery System. Patent WO2016094067,
June 16, 2016.
Robot
Local Robot
Controller
Source eNB
IoT Anomaly
Detection
Source MME
Target MME
Target eNB
Handover Request
Handover Request
Relocation Request
Handover Response
eNB Status Transfer
Handover Response
Relocation Response
MME Status Transfer
Handover Confirm
Figure 11.13 Robots roaming.
node for all robots and responsible for connectivity between robots and mobile
networks. The first level of data monitoring and security analysis is done in LRC before
data is forwarded to mobile network. The IoT service provider can be either a mobile
network operator or a Mobile Virtual Network Operator (MVNO). The MVNO carries
out advanced analysis of collected data from various LRCs through data‐mining mechanisms in an IoT anomaly detection module. The IoT anomaly detection module includes
linear and learning algorithms. The linear data‐mining algorithm will extract and provide
proper attributes to the next process that uses a learning algorithm. This mechanism
would decrease the load of input data for the learning algorithm, which is the most
time‐consuming part (because of the algorithm complexity). So, the proposed algorithm could label new attacks and detect known ones (e.g. botnet, malware) faster than
the existing solutions. The analyzed data would be used for intrusion detection in the
same or a different MVNO. While a robot has been identified as malicious in an MVNO,
a notification will be sent to all MVNOs by an IoT orchestrator to block the malicious
robot and prevent the re‐analysis of the malicious robot in future, since the malicious
robot may try to attack the cloud platform using other MVNOs. Therefore, the distributed security platform reduces the computation processing for a malicious robot that
has been already identified as malicious in other MVNOs.
References
1 Nokia Networks (2016) 5G masterplan – five keys to create the new communications era.
White Paper, C401‐011949‐WP‐201601‐1‐EN.
2 Monshizadeh, M., Khatri, V., Kantola, R. and Yan, Z. (2017) An orchestrated security
platform for internet of robots. Proceedings of The 12th International Conference on
Green, Pervasive and Cloud Computing (GPC) [accepted].
3 Ian, C. et al. (2016) Automated Drone Systems. Patent US2016266579, September 15, 2016.
4 Daniel, O. (2016) Drone Docking Station and Delivery System. Patent WO2016094067,
June 16, 2016.
Robot
Local Robot
Controller
Source eNB
IoT Anomaly
Detection
Source MME
Target MME
Target eNB
Handover Request
Handover Request
Relocation Request
Handover Response
eNB Status Transfer
Handover Response
Relocation Response
MME Status Transfer
Handover Confirm
Figure 11.13 Robots roaming.