Bitcoin, being the most successful cryptocurrency, has been repeatedly attacked with many users losing their funds. The industry's response to securing the user's assets is to offer tamper-resistant hardware wallets. Although such wallets are considered to be the most secure means for managing an account, no formal attempt has been previously done to identify, model and formally verify their properties. This paper provides the first formal model of the Bitcoin hardware wallet operations. We identify the properties and security parameters of a Bitcoin wallet and formally define them in the Universal Composition (UC) Framework. We present a modular treatment of a hardware wallet ecosystem, by realizing the wallet functionality in a hybrid setting defined by a set of protocols. This approach allows us to capture in detail the wallet's components, their interaction and the potential threats. We deduce the wallet's security by proving that it is secure under common cryptographic assumptions, provided that there is no deviation in the protocol execution. Finally, we define the attacks that are successful under a protocol deviation, and analyze the security of commercially available wallets.
With over 80% of goods transportation in volume carried by sea, ports are key infrastructures within the logistics value chain. To address the challenges of the globalized and competitive economy, ports are digitizing at a fast pace, evolving into smart ports. Consequently, the cyber-resilience of ports is essential to prevent possible disruptions to the economic supply chain. Over the last few years, there has been a significant increase in the number of disclosed cyber-attacks on ports. In this paper, we present the capabilities of a high-end hybrid cyber range for port cyber risks awareness and training. By describing a specific port use-case and the first results achieved, we draw perspectives for the use of cyber ranges for the training of port actors in cyber crisis management.
Abstract. As with every financially oriented protocol, there has been a great interest in studying, verifying, attacking, identifying problems, and proposing solutions for Bitcoin. Within that scope, it is highly recommended that the keys of user accounts are stored offline. To that end, companies provide solutions that range from paper wallets to tamper-resistant smart-cards, offering different level of security. While incorporating expensive hardware for the wallet purposes is though to bring guarantees, it is often that the low-level implementations introduce exploitable back-doors. This paper aims to bring to attention how the overlooked low-level protocols that implement the hardware wallets can be exploited to mount Bitcoin attacks. To demonstrate that, we analyse the general protocol behind LEDGER Wallets, the only EAL5+ certified against side channel analysis attacks hardware. In this work we conduct a throughout analysis on the Ledger Wallet communication protocol and show how to successfully attack it in practice. We address the lack of well-defined security properties that Bitcoin wallets should conform by articulating a minimal threat model against which any hardware wallet should defend. We further use that threat model to propose a lightweight fix that can be adopted by different technologies.
The Open Information Extraction Project 1 is one of the most ambitious attempts in the area of automatically constructing ontologies by harvesting information from the web. What we will call their KnowItAll Ontology contains about 6 billion items, consisting of triples and rules. The downside of such automatically constructed ontologies is that they contain a vast number of errors: some arising from errors in the original web data and some from errors in extracting the data. In this project we explore whether techniques we have developed in the domain of ontology repair can be used to detect and correct some of these errors. In particular, we explore whether the errors in their ontology can be automatically detected by using a theorem prover. We also present a manual classification of the errors as a preliminary feasibility exploration, and discuss our future work towards automatically correcting the ontology based on the error classification.
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