# Smart Devices Security Enhancement via Power Supply Monitoring +
## 6 Conclusions  
In this work, the introduction of a solution for monitoring IoT devices exploiting characteristics of the side-channel attacks is presented. The idea is based on the fact that manufacturers do not provide security mechanisms or hacker countermeasures in their IoT products, mainly due to their low cost. However, this leads to a critical increase of the security surface that needs to be protected. Successful attacks to IoT devices are announced every day but the main actions to secure the IoT devices are mainly found in commercial Intrusion Detection Systems (IDS). Furthermore, attacks have become more sophisticated, and are able to mask themselves from IDS discovery.  
The concept follows the assumption that any additional data processing will lead to an increase in power consumption. Thus, a monitoring mechanism was developed using a simple circuit and a microcontroller able to perform simple calculations and be placed between the power plug and the IoT device. The continuous monitoring of the supply current for a device with explicitly defined operation may reveal significant information and expand data for the status of the IoT device. The latter is significant for the modern IDS, and it generally follows bio-inspired approaches for anomaly detection.  
Hence, the supply current was monitored by exploiting knowledge gained from side-channel attacks. The deviation of the monitored current was correlated to security and reliability issues of IoT devices and it was proven that an anomaly may be detected with non typical methods. The main contribution of this work is that it introduced a new technique to detect operational anomalies of smart devices in a symptomatic approach rather than by identifying the cause itself. This allows on-time anomaly detection, a decrease of security dependency from the targeted IoT device and an increase of the data that may be correlated to a new kind of botnet.  
The extension of monitored parameters for security issues confronted by IDS is expected to be integrated soon in major systems. The presented method is the basis for this type of parameter extension that will provides additional measurements for protecting critical IoT infrastructures. The handling of the collected data in an IDS and the exploitation of Machine Learning (ML) algorithms in order to identify promptly new kinds of attacks, will be challenges for future research. This work may be expanded by automating the process to define the normal operational thresholds and the calculation of the sufficient number of spikes to efficiently detect anomalies and finally, the dynamic definition of variables \(m\) and \(n\). Finally, although the technique has decent performance regarding the reliability of the equipment (in contrast to the network attack and the physical attack), it may be used as a means to detect hardware Trojans or IP counterfeits.  
Figure 8: IP camera with infected application code.  
**Author Contributions:** Conceptualization, A.K.; methodology, A.K., D.M. and G.S.; software, D.M.; validation, D.M.; investigation, D.M.; resources, D.S.; data curation, D.M.; writing-original draft preparation, A.K. and D.M.; writing-review and editing, G.S. and D.S.; visualization, D.M.; supervision, A.K.; project administration, A.K.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by NOKIA Bell Labs through global donation program for \"Research on IoT Security based on Bio-informatics\".  
**Acknowledgments:** The authors would like to acknowledge the contribution of [PERSON] through creative discussions during the last two years. We would also like to acknowledge the professional assistance of the MDPI Future Internet personnel throughout the review process and the reviewers for their numerous comments, who have helped us improve significantly the submitted manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.