No Arabic abstract
Knowledge flow analysis offers a simple and flexible way to find flaws in security protocols. A protocol is described by a collection of rules constraining the propagation of knowledge amongst principals. Because this characterization corresponds closely to informal descriptions of protocols, it allows a succinct and natural formalization; because it abstracts away message ordering, and handles communications between principals and applications of cryptographic primitives uniformly, it is readily represented in a standard logic. A generic framework in the Alloy modelling language is presented, and instantiated for two standard protocols, and a new key management scheme.
We propose a method for engineering security protocols that are aware of timing aspects. We study a simplified version of the well-known Needham Schroeder protocol and the complete Yahalom protocol, where timing information allows the study of different attack scenarios. We model check the protocols using UPPAAL. Further, a taxonomy is obtained by studying and categorising protocols from the well known Clark Jacob library and the Security Protocol Open Repository (SPORE) library. Finally, we present some new challenges and threats that arise when considering time in the analysis, by providing a novel protocol that uses time challenges and exposing a timing attack over an implementation of an existing security protocol.
Mobile application security has been one of the major areas of security research in the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and performance, and are hence soundy. Unfortunately, the specific unsound choices or flaws in the design of these tools are often not known or well-documented, leading to a misplaced confidence among researchers, developers, and users. This paper proposes the Mutation-based soundness evaluation ($mu$SE) framework, which systematically evaluates Android static analysis tools to discover, document, and fix, flaws, by leveraging the well-founded practice of mutation analysis. We implement $mu$SE as a semi-automated framework, and apply it to a set of prominent Android static analysis tools that detect private data leaks in apps. As the result of an in-depth analysis of one of the major tools, we discover 13 undocumented flaws. More importantly, we discover that all 13 flaws propagate to tools that inherit the flawed tool. We successfully fix one of the flaws in cooperation with the tool developers. Our results motivate the urgent need for systematic discovery and documentation of unsound choices in soundy tools, and demonstrate the opportunities in leveraging mutation testing in achieving this goal.
Mobile health applications (mHealth apps for short) are being increasingly adopted in the healthcare sector, enabling stakeholders such as governments, health units, medics, and patients, to utilize health services in a pervasive manner. Despite having several known benefits, mHealth apps entail significant security and privacy challenges that can lead to data breaches with serious social, legal, and financial consequences. This research presents an empirical investigation about security awareness of end-users of mHealth apps that are available on major mobile platforms, including Android and iOS. We collaborated with two mHealth providers in Saudi Arabia to survey 101 end-users, investigating their security awareness about (i) existing and desired security features, (ii) security related issues, and (iii) methods to improve security knowledge. Findings indicate that majority of the end-users are aware of the existing security features provided by the apps (e.g., restricted app permissions); however, they desire usable security (e.g., biometric authentication) and are concerned about privacy of their health information (e.g., data anonymization). End-users suggested that protocols such as session timeout or Two-factor authentication (2FA) positively impact security but compromise usability of the app. Security-awareness via social media, peer guidance, or training from app providers can increase end-users trust in mHealth apps. This research investigates human-centric knowledge based on empirical evidence and provides a set of guidelines to develop secure and usable mHealth apps.
Off-chain protocols constitute one of the most promising approaches to solve the inherent scalability issue of blockchain technologies. The core idea is to let parties transact on-chain only once to establish a channel between them, leveraging later on the resulting channel paths to perform arbitrarily many peer-to-peer transactions off-chain. While significant progress has been made in terms of proof techniques for off-chain protocols, existing approaches do not capture the game-theoretic incentives at the core of their design, which led to overlooking significant attack vectors like the Wormhole attack in the past. This work introduces the first game-theoretic model that is expressive enough to reason about the security of off-chain protocols. We advocate the use of Extensive Form Games - EFGs and introduce two instances of EFGs to capture security properties of the closing and the routing of the Lightning Network. Specifically, we model the closing protocol, which relies on punishment mechanisms to disincentivize the uploading on-chain of old channel states, as well as the routing protocol, thereby formally characterizing the Wormhole attack, a vulnerability that undermines the fee-based incentive mechanism underlying the Lightning Network.
With the increasing usage of open-source software (OSS) components, vulnerabilities embedded within them are propagated to a huge number of underlying applications. In practice, the timely application of security patches in downstream software is challenging. The main reason is that such patches do not explicitly indicate their security impacts in the documentation, which would be difficult to recognize for software maintainers and users. However, attackers can still identify these secret security patches by analyzing the source code and generate corresponding exploits to compromise not only unpatch