Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

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.

Keywords

• Internet of Things
• hardware security
• home area network
• hardware Trojan
• machine learning
• power profile