TY - GEN
T1 - (HIADIoT)
T2 - 62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019
AU - Mohammed, Hawzhin
AU - Odetola, Tolulope A.
AU - Hasan, Syed Rafay
AU - Stissi, Sari
AU - Garlin, Isaiah
AU - Awwad, Falah
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Internet of Things (IoT) device usage has grown and has been adopted in various daily use devices applied in healthcare, smart homes, smart grids, connected cars and the list goes on. IoT devices have security vulnerabilities and cannot completely guarantee data privacy. As is the case with any network, IoT devices are also prone to hacks and Hardware Intrinsic (HI) attacks such as Hardware Trojans (HT), Firmware Modification and Memory Manipulation. The manifestation of HI attack can lead to various types of security issues which includes data theft and denial of service. Traditional HT attack detection techniques are valid for integrated circuit level only, and considered to be very invasive for an IoT device. Therefore, in this paper we propose a non-invasive approach that investigates Hardware Intrinsic Attack Detection in IoT (HIADIoT) devices. This approach detects covert channel and power depletion attacks through the power profile of IoT devices in different modes of operation utilizing machine learning algorithm. The power profile behavior of different IoT devices was observed over a period of time and then preprocessed to serve as data points. These data points is then provided to Random Forest Algorithm which correctly classifies 95.5% of the data point and recognizes potential HI attacks.
AB - Internet of Things (IoT) device usage has grown and has been adopted in various daily use devices applied in healthcare, smart homes, smart grids, connected cars and the list goes on. IoT devices have security vulnerabilities and cannot completely guarantee data privacy. As is the case with any network, IoT devices are also prone to hacks and Hardware Intrinsic (HI) attacks such as Hardware Trojans (HT), Firmware Modification and Memory Manipulation. The manifestation of HI attack can lead to various types of security issues which includes data theft and denial of service. Traditional HT attack detection techniques are valid for integrated circuit level only, and considered to be very invasive for an IoT device. Therefore, in this paper we propose a non-invasive approach that investigates Hardware Intrinsic Attack Detection in IoT (HIADIoT) devices. This approach detects covert channel and power depletion attacks through the power profile of IoT devices in different modes of operation utilizing machine learning algorithm. The power profile behavior of different IoT devices was observed over a period of time and then preprocessed to serve as data points. These data points is then provided to Random Forest Algorithm which correctly classifies 95.5% of the data point and recognizes potential HI attacks.
UR - http://www.scopus.com/inward/record.url?scp=85075027398&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075027398&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2019.8885183
DO - 10.1109/MWSCAS.2019.8885183
M3 - Conference contribution
AN - SCOPUS:85075027398
T3 - Midwest Symposium on Circuits and Systems
SP - 852
EP - 855
BT - 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, MWSCAS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 August 2019 through 7 August 2019
ER -
