Distributed System Fuzzing

Grey-box fuzzing is the lightweight approach of choice for finding bugs in sequential programs. It provides a balance between efficiency and effectiveness by conducting a biased random search over the domain of program inputs using a feedback function from observed test executions. For distributed system testing, however, the state-of-practice is represented today by only black-box tools that do not attempt to infer and exploit any knowledge of the system's past behaviours to guide the search for bugs. In this work, we present Mallory: the first framework for grey-box fuzz-testing of distributed systems. Unlike popular black-box distributed system fuzzers, such as Jepsen, that search for bugs by randomly injecting network partitions and node faults or by following human-defined schedules, Mallory is adaptive. It exercises a novel metric to learn how to maximize the number of observed system behaviors by choosing different sequences of faults, thus increasing the likelihood of finding new bugs. The key enablers for our approach are the new ideas of timeline-driven testing and timeline abstraction that provide the feedback function guiding a biased random search for failures. Mallory dynamically constructs Lamport timelines of the system behaviour, abstracts these timelines into happens-before summaries, and introduces faults guided by its real-time observation of the summaries. We have evaluated Mallory on a diverse set of widely-used industrial distributed systems. Compared to the start-of-the-art black-box fuzzer Jepsen, Mallory explores more behaviours and takes less time to find bugs. Mallory discovered 22 zero-day bugs (of which 18 were confirmed by developers), including 10 new vulnerabilities, in rigorously-tested distributed systems such as Braft, Dqlite, and Redis. 6 new CVEs have been assigned

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.Comment: IEEE Transactions on Dependable and Secure Computing; 16 pages. arXiv admin note: text overlap with arXiv:1908.1164

Adonis: Practical and Efficient Control Flow Recovery through OS-Level Traces

Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment. However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with deployment and development costs. To tackle this problem, we propose a novel solution, Adonis, which harnesses OS-level traces, such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice. Adonis operates in two steps: it first identifies the call-sites of trace entries, then it executes a pair-wise symbolic execution to recover valid execution paths. This technique has several advantages. First, Adonis does not require the insertion of any probes into existing applications, thereby minimizing runtime cost. Second, given that OS-level traces are hardware-independent, Adonis can be implemented across various hardware configurations without the need for hardware-specific engineering efforts, thus reducing deployment cost. Third, as Adonis is fully automated and does not depend on manually created logs, it circumvents additional development cost. We conducted an evaluation of Adonis on representative desktop applications and real-world IoT applications. Adonis can faithfully recover the control flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach, Adonis can not only cover all the execution paths recovered, but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of Adonis is 18.3× lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of Adonis is 50× smaller and 443× smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this work, we have made the code and data publicly available

Privacy-aware Biometric Blockchain based e-Passport System for Automatic Border Control

In the middle of 1990s, World Wide Web technology initially steps into our life. Now, 30 years after that, widespread internet access and established computing technology bring embodied real life into Metaverse by digital twin. Internet is not only blurring the concept of physical distance, but also blurring the edge between the real and virtual world. Another breakthrough in computing is the blockchain, which shifts the root of trust attached to a system administrator to the computational power of the system. Furthermore, its favourable properties such as immutable time-stamped transaction history and atomic smart contracts trigger the development of decentralized autonomous organizations (DAOs). Combining above two, this thesis presents a privacy-aware biometric Blockchain based e-passport system for automatic border control(ABC), which aims for improving the efficiency of existing ABC system. Specifically, through constructing a border control Metaverse DAO, border control workload can be autonomously self-executed by atomic smart contracts as transaction and then immutably recorded on Blockchain. What is more, to digitize border crossing documentation, biometric Blockchain based e-passport system(BBCVID) is created to generate an immutable real-world identity digital twin in the border control Metaverse DAO through Blockchain and biometric identity authentication. That is to say, by digitizing border crossing documentation and automatizing both biometric identity authentication and border crossing documentation verification, our proposal is able to significantly improve existing border control efficiency. Through system simulation and performance evaluation by Hyperledger Caliper, the proposed system turns out to be able to improve existing border control efficiency by 3.5 times more on average, which is remarkable. What is more, the dynamic digital twin constructed by BBCVID enables computing techniques such as machine learning and big data analysis applicable to real-world entity, which has a huge potential to create more value by constructing smarter ABC systems