Biblio

Hardware security is a critical concern in design and fabrication of integrated circuits (ICs). Contemporary hardware threats comprise tens of advance invasive and non-invasive attacks for compromising security of modern ICs. Numerous attack-specific countermeasures against the individual threats have been proposed, trading power, area, speed, and design complexity of a system for security. These typical overheads combined with strict performance requirements in advanced technology nodes and high complexity of modern ICs often make the codesign of multiple countermeasures impractical. In this paper, on-chip distribution networks are exploited for detecting those hardware security threats that require non-invasive, yet physical interaction with an operating device-under-attack (e.g., measuring equipment for collecting sensitive information in side-channel attacks). With the proposed approach, the effect of the malicious physical interference with the device-under-attack is captured in the form of on-chip voltage variations and utilized for detecting malicious activity in the compromised device. A machine learning (ML) security IC is trained to predict system security based on sensed variations of signals within on-chip distribution networks. The trained ML ICs are distributed on-chip, yielding a robust and high-confidence security network on-chip. To halt an active attack, a variety of desired counteractions can be executed in a cost-effective manner upon the attack detection. The applicability and effectiveness of these security networks is demonstrated in this paper with respect to power, timing, and electromagnetic analysis attacks.

Because major smartphone platforms are equipped with Bluetooth Low Energy (BLE) capabilities, more and more smart devices have adopted BLE technologies to communicate with smartphones. In order to support the mesh topology in BLE networks, several proposals have been designed. Among them, the Bluetooth Special Interest Group (SIG) recently released a specification for Bluetooth mesh networks based upon BLE technology. This paper focuses on this standard solution and analyses its security protocol with hardware security in mind. As it is expected that internet of things (IoT) devices will be deployed everywhere, the risk of physical attacks must be assessed. First, we provide a comprehensive survey of the security features involved in Bluetooth mesh. Then, we introduce some physical attacks identified as serious threats for the IoT and discuss their relevance in the case of Bluetooth mesh networks. Finally, we briefly discuss possible countermeasures to reach a secure implementation.