Asanka Sayakkara

Ruwanga Konara, Kasun De Zoysa, Anuradha Mahasinghe et al.

With rapid advancements in quantum computing, it is widely anticipated that scalable quantum hardware may threaten classical cryptography and hence, the internet and the current information security infrastructure in the coming decade. This is mainly due to the operational realizations of quantum algorithms such as Grover and Shor, to which the current classical encryption protocols are vulnerable. Blockchains, i.e., blockchain data structures and their data, rely heavily on classical cryptography. One approach to secure blockchains is to attempt to achieve conceptual information-theoretic security under certain assumptions by defining blockchains on quantum technologies. There have been two major conceptualizations of blockchains data structures on quantum registers: the time-entangled Greenberger-Horne-Zeilinger (GHZ) state blockchain and the quantum hypergraph blockchain. We conceptualize a new quantum blockchain framework combining features of both these schemes to achieve the conceptual information-theoretic protection against undetected measurement attack (physics-based disturbance detectability) of the time-entangled GHZ blockchain and the scalability and efficiency of the quantum hypergraph blockchain in the proposed quantum blockchain data structure and framework. In this work, we propose a novel quantum blockchain architecture that integrates temporal GHZ entanglement with phase encoding inspired by the quantum hypergraph blockchain. The proposed design combines the conceptual information-theoretic tamper sensitivity/resistance of temporal entanglement with improved encoding efficiency, offering a unified conceptual framework for scalable and secure quantum blockchains.

Asanka Sayakkara, Nhien-An Le-Khac, Mark Scanlon

Internet of Things (IoT) devices have expanded the horizon of digital forensic investigations by providing a rich set of new evidence sources. IoT devices includes health implants, sports wearables, smart burglary alarms, smart thermostats, smart electrical appliances, and many more. Digital evidence from these IoT devices is often extracted from third party sources, e.g., paired smartphone applications or the devices' back-end cloud services. However vital digital evidence can still reside solely on the IoT device itself. The specifics of the IoT device's hardware is a black-box in many cases due to the lack of proven, established techniques to inspect IoT devices. This paper presents a novel methodology to inspect the internal software activities of IoT devices through their electromagnetic radiation emissions during live device investigation. When a running IoT device is identified at a crime scene, forensically important software activities can be revealed through an electromagnetic side-channel analysis (EM-SCA) attack. By using two representative IoT hardware platforms, this work demonstrates that cryptographic algorithms running on high-end IoT devices can be detected with over 82% accuracy, while minor software code differences in low-end IoT devices could be detected over 90% accuracy using a neural network-based classifier. Furthermore, it was experimentally demonstrated that malicious modification of the stock firmware of an IoT device can be detected through machine learning-assisted EM-SCA techniques. These techniques provide a new investigative vector for digital forensic investigators to inspect IoT devices.