Myridakis, Dimitrios; Spathoulas, Georgios; Kakarountas, Athanasios; Schinianakis, Dimitrios Smart Devices Security Enhancement via Power Supply Monitoring Journal Article In: Future Internet, vol. 12, no. 3, 2020, ISSN: 1999-5903. Abstract | Links | BibTeX | Tags: Anomaly Detection, Hardware, IoT, security, smart device; current monitoring; physical characteristics Papafotikas, Stefanos; Kakarountas, Athanasios A Machine-Learning Clustering Approach for Intrusion Detection to IoT Devices Proceedings Article In: 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1–6, IEEE 2019. BibTeX | Tags: Anomaly Detection, Intrusion Detection, IoT, Machine Learning, security, Smart Devices Myridakis, Dimitrios; Spathoulas, Georgios; Kakarountas, Athanasios; Schoinianakis, Dimitris; Lueken, Joachim Anomaly detection in iot devices via monitoring of supply current Proceedings Article In: 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin), pp. 1–4, IEEE 2018. BibTeX | Tags: Anomaly Detection, IoT, security Myridakis, Dimitrios; Spathoulas, Georgios; Kakarountas, Athanasios Supply current monitoring for anomaly detection on iot devices Proceedings Article In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, pp. 1–2, 2017. BibTeX | Tags: Anomaly Detection, Intrusion Detection, IoT, security "Dimitrios, Myridakis; Georgios, Spathoulas; Athanasios, Kakarountas; Schoinianakis, Dimitrios; Lueken, Joachim" "Mimicking Biometrics on Smart Devices and Its Application in IoT Security for Health Systems" Book Chapter In: "Gupta, Nishu; Paiva, Sara" (Ed.): "IoT and ICT for Healthcare Applications", pp. "175–189", "Springer International Publishing", "Cham", 0000, ISBN: "978-3-030-42934-8". Abstract | Links | BibTeX | Tags: Anomaly Detection, biometrics, Hardware Security, Health Applications, Intrusion Detection, IoT, Smart Device
2020
@article{fi12030048,
title = {Smart Devices Security Enhancement via Power Supply Monitoring},
author = {Dimitrios Myridakis and Georgios Spathoulas and Athanasios Kakarountas and Dimitrios Schinianakis},
url = {https://www.mdpi.com/1999-5903/12/3/48},
doi = {10.3390/fi12030048},
issn = {1999-5903},
year = {2020},
date = {2020-01-01},
journal = {Future Internet},
volume = {12},
number = {3},
abstract = {The continuous growth of the number of Internet of Things (IoT) devices and their inclusion to public and private infrastructures has introduced new applciations to the market and our day-to-day life. At the same time, these devices create a potential threat to personal and public security. This may be easily understood either due to the sensitivity of the collected data, or by our dependability to the devices' operation. Considering that most IoT devices are of low cost and are used for various tasks, such as monitoring people or controlling indoor environmental conditions, the security factor should be enhanced. This paper presents the exploitation of side-channel attack technique for protecting low-cost smart devices in an intuitive way. The work aims to extend the dataset provided to an Intrusion Detection Systems (IDS) in order to achieve a higher accuracy in anomaly detection. Thus, along with typical data provided to an IDS, such as network traffic, transmitted packets, CPU usage, etc., it is proposed to include information regarding the device’s physical state and behaviour such as its power consumption, the supply current, the emitted heat, etc. Awareness of the typical operation of a smart device in terms of operation and functionality may prove valuable, since any deviation may warn of an operational or functional anomaly. In this paper, the deviation (either increase or decrease) of the supply current is exploited for this reason. This work aimed to affect the intrusion detection process of IoT and proposes for consideration new inputs of interest with a collateral interest of study. In parallel, malfunction of the device is also detected, extending this work’s application to issues of reliability and maintainability. The results present 100% attack detection and this is the first time that a low-cost security solution suitable for every type of target devices is presented.},
keywords = {Anomaly Detection, Hardware, IoT, security, smart device; current monitoring; physical characteristics},
pubstate = {published},
tppubtype = {article}
}
2019
@inproceedings{papafotikas2019machine,
title = {A Machine-Learning Clustering Approach for Intrusion Detection to IoT Devices},
author = {Stefanos Papafotikas and Athanasios Kakarountas},
year = {2019},
date = {2019-01-01},
booktitle = {2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)},
pages = {1--6},
organization = {IEEE},
keywords = {Anomaly Detection, Intrusion Detection, IoT, Machine Learning, security, Smart Devices},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
@inproceedings{myridakis2018anomaly,
title = {Anomaly detection in iot devices via monitoring of supply current},
author = {Dimitrios Myridakis and Georgios Spathoulas and Athanasios Kakarountas and Dimitris Schoinianakis and Joachim Lueken},
year = {2018},
date = {2018-01-01},
booktitle = {2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin)},
pages = {1--4},
organization = {IEEE},
keywords = {Anomaly Detection, IoT, security},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
@inproceedings{myridakis2017supplyb,
title = {Supply current monitoring for anomaly detection on iot devices},
author = {Dimitrios Myridakis and Georgios Spathoulas and Athanasios Kakarountas},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 21st Pan-Hellenic Conference on Informatics},
pages = {1--2},
keywords = {Anomaly Detection, Intrusion Detection, IoT, security},
pubstate = {published},
tppubtype = {inproceedings}
}
0000
@inbook{Dimitrios2020b,
title = {"Mimicking Biometrics on Smart Devices and Its Application in IoT Security for Health Systems"},
author = {Myridakis "Dimitrios and Spathoulas Georgios and Kakarountas Athanasios and Dimitrios Schoinianakis and Joachim" Lueken},
editor = {Nishu "Gupta and Sara" Paiva},
url = {"https://doi.org/10.1007/978-3-030-42934-8_10"},
doi = {"10.1007/978-3-030-42934-8_10"},
isbn = {"978-3-030-42934-8"},
booktitle = {"IoT and ICT for Healthcare Applications"},
pages = {"175--189"},
publisher = {"Springer International Publishing"},
address = {"Cham"},
abstract = {"The Internet of Things (IoT) encompasses cyber and physical objects in multidisciplinary applications such as home automation, industrial process, environmental monitoring, and human health, to mention a few. Especially the latter systems have seen rapid growth in hospitals and health centers over the last decade. The wireless healthcare monitoring devices of various technologies are presenting global interest, since they offer valuable health metrics to users and physicians and also they are easy to use on a day-to-day basis. This chapter refers to security issues associated to IoT devices in general and how a technique of bio-mimicking on smart devices may reveal potential attacks or malfunctions. The case of systems for healthcare and health monitoring is considered, in order to highlight the techniques' benefits in this topic. The approach is bio-inspired by human biometrics and adopted to fit the cyber world, referring to a device's state; specifically the paradigm of side channel attack is exploited. This introduces the notion that to secure devices for healthcare, the designer has to consider the ``health status'' of the device itself. Taking into consideration that these devices are limited by their functionality and functional characteristics, it is expected that any deviation from the expected normal operation would result in a similar deviation in any operational parameter."},
keywords = {Anomaly Detection, biometrics, Hardware Security, Health Applications, Intrusion Detection, IoT, Smart Device},
pubstate = {published},
tppubtype = {inbook}
}
Smart Devices Security Enhancement via Power Supply Monitoring Journal Article In: Future Internet, vol. 12, no. 3, 2020, ISSN: 1999-5903. A Machine-Learning Clustering Approach for Intrusion Detection to IoT Devices Proceedings Article In: 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1–6, IEEE 2019. Anomaly detection in iot devices via monitoring of supply current Proceedings Article In: 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin), pp. 1–4, IEEE 2018. Supply current monitoring for anomaly detection on iot devices Proceedings Article In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, pp. 1–2, 2017. "Mimicking Biometrics on Smart Devices and Its Application in IoT Security for Health Systems" Book Chapter In: "Gupta, Nishu; Paiva, Sara" (Ed.): "IoT and ICT for Healthcare Applications", pp. "175–189", "Springer International Publishing", "Cham", 0000, ISBN: "978-3-030-42934-8".
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