Abstract
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.
Highlights
Internet of Things (IoT) is fundamentally a collection of smart devices inserted with remote correspondence capacity of wireless connection [9], [17]
IoT devices are prevalent in the smart city, smart grid, home area network (HAN), advanced manufacturing, health monitoring, and many other modern applications
Stated that the number of IoT devices are expected to grow by 27 percent annually and will reach 4.1 billion devices by 2024 [1]
Summary
Internet of Things (IoT) is fundamentally a collection of smart devices inserted with remote correspondence capacity of wireless connection [9], [17]. Xiao et al in [49] have recognized such IoT-ED attack models and learning-based security methods This involves IoT-ED authentication, access control, malware detection, and secure offloading, which are proved to be assuring protection for the IoT-EDs against attacks that are sowed at the hardware level and manifest in the network layer. The second method utilizes classification to provide information on covert channel communication using IoT-ED power consumption They claim to detect seven types of covert channel attacks in Android devices. The existing literature related to hardware Trojan attacks in an IoT system does not discuss the possibility of them affecting network traffic This is the second problem that has been addressed in this work by proposing a novel, on-field, run-time HT based attack detection in IoT-ED
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