TY - JOUR
T1 - Fusion-on-field security and privacy preservation for IoT edge devices
T2 - Concurrent defense against multiple types of hardware trojan attacks
AU - Mohammed, Hawzhin
AU - Hasan, Syed Rafay
AU - Awwad, Falah
N1 - Funding Information:
Corresponding authors: Hawzhin Mohammed (hmohammed42@students.tntech.edu); Syed Rafay Hasan (shasan@tntech.edu); Falah Awwad (f_awwad@uaeu.ac.ae) This work was supported by Information and Communication Technologies (ICT) Fund UAE, fund No. 21N206 at UAE University, Al Ain, United Arab Emirates (UAE).
Funding Information:
This work was supported by Information and Communication Technologies (ICT) Fund UAE, fund No. 21N206 at UAE University, Al Ain, United Arab Emirates (UAE).
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Internet of Things (IoT) devices have connected millions of houses around the globe via the internet. In the recent past, threats due to hardware Trojan (HT) in the integrated circuits (IC) have become a serious concern, which affects IoT edge devices (IoT-ED). In this paper, the possibility of the IoT-ED with embedded HT that can cause serious security, privacy, and availability problems to the IoT based Home Area Network (HAN) has been discussed. Conventional network attack detection techniques work at the network protocol layers, whereas IoT-ED with HT can lead to the peculiar manifestation of attack at the physical and/or firmware level. On the other hand, in the IC design, most of the HT-based attack detection techniques require design time intervention, which is expensive for many of the IoT-ED and cannot guarantee 100% immunity. The argument in this paper is that the health of modern IoT-ED requires a final line of defense against possible HT-based attacks that goes undetected during IC design and test. The approach is to utilize power profiling (PP) and network traffic (NT) data without intervening into the IC design to detect malicious activity in HAN. The proposed technique is to effectively identify multiple attacks concurrently and to differentiate between different types of attacks. The IoT-ED behaviors for five different types of random attacks have been studied, including covert channel, DoS, ARQ, power depletion, and impersonation attacks. Data fusion has been leveraged by combining the PP and NT data and is able to detect, without design time intervention, each of the five attacks individually with up to 99% accuracy. Moreover, the proposed technique can also detect all the attacks concurrently with 92% accuracy. To the best of authors' knowledge, this is the first work where multiple HT based attacks are concurrently detected in IoT-ED without requiring any design time intervention.
AB - Internet of Things (IoT) devices have connected millions of houses around the globe via the internet. In the recent past, threats due to hardware Trojan (HT) in the integrated circuits (IC) have become a serious concern, which affects IoT edge devices (IoT-ED). In this paper, the possibility of the IoT-ED with embedded HT that can cause serious security, privacy, and availability problems to the IoT based Home Area Network (HAN) has been discussed. Conventional network attack detection techniques work at the network protocol layers, whereas IoT-ED with HT can lead to the peculiar manifestation of attack at the physical and/or firmware level. On the other hand, in the IC design, most of the HT-based attack detection techniques require design time intervention, which is expensive for many of the IoT-ED and cannot guarantee 100% immunity. The argument in this paper is that the health of modern IoT-ED requires a final line of defense against possible HT-based attacks that goes undetected during IC design and test. The approach is to utilize power profiling (PP) and network traffic (NT) data without intervening into the IC design to detect malicious activity in HAN. The proposed technique is to effectively identify multiple attacks concurrently and to differentiate between different types of attacks. The IoT-ED behaviors for five different types of random attacks have been studied, including covert channel, DoS, ARQ, power depletion, and impersonation attacks. Data fusion has been leveraged by combining the PP and NT data and is able to detect, without design time intervention, each of the five attacks individually with up to 99% accuracy. Moreover, the proposed technique can also detect all the attacks concurrently with 92% accuracy. To the best of authors' knowledge, this is the first work where multiple HT based attacks are concurrently detected in IoT-ED without requiring any design time intervention.
KW - ARQ attack
KW - DoS attack
KW - Internet of Things
KW - hardware Trojan
KW - hardware security
KW - home area network
KW - machine learning
KW - power profile
UR - http://www.scopus.com/inward/record.url?scp=85081137773&partnerID=8YFLogxK
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U2 - 10.1109/ACCESS.2020.2975016
DO - 10.1109/ACCESS.2020.2975016
M3 - Article
AN - SCOPUS:85081137773
SN - 2169-3536
VL - 8
SP - 36847
EP - 36862
JO - IEEE Access
JF - IEEE Access
M1 - 9003413
ER -