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Shing-Chi Cheung

Publications and source records attributed to Shing-Chi Cheung.

76 records · Page 5Linked to original sources

ContractGuard: Defend Ethereum Smart Contracts with Embedded Intrusion Detection

Ethereum smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. Smart contracts handle and transfer assets of values, offering strong incentives for malicious attacks. Intrusion attacks are a popular type of malicious attacks. In this paper, we propose ContractGuard, the first intrusion detection system (IDS) to defend Ethereum smart contracts against such attacks. Like IDSs for conventional programs, ContractGuard detects intrusion attempts as abnormal control flow. However, existing IDS techniques/tools are inapplicable to Ethereum smart contracts due to Ethereum's decentralized nature and its highly restrictive execution environment. To address these issues, we design ContractGuard by embedding it in the contracts to profile context-tagged acyclic paths, and optimizing it under the Ethereum gas-oriented performance model. The main goal is to minimize the overheads, to which the users will be extremely sensitive since the cost needs to be paid upfront in digital concurrency. Empirical investigation using real-life contracts deployed in the Ethereum mainnet shows that on average, ContractGuard only adds to 36.14% of the deployment overhead and 28.27% of the runtime overhead. Furthermore, we conducted controlled experiments and show that ContractGuard successfully guard against attacks on all real-world vulnerabilities and 83% of the seeded vulnerabilities.

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Analyzing and Disentangling Interleaved Interrupt-driven IoT Programs

In the Internet of Things (IoT) community, Wireless Sensor Network (WSN) is a key technique to enable ubiquitous sensing of environments and provide reliable services to applications. WSN programs, typically interrupt-driven, implement the functionalities via the collaboration of Interrupt Procedure Instances (IPIs, namely executions of interrupt processing logic). However, due to the complicated concurrency model of WSN programs, the IPIs are interleaved intricately and the program behaviours are hard to predicate from the source codes. Thus, to improve the software quality of WSN programs, it is significant to disentangle the interleaved executions and develop various IPI-based program analysis techniques, including offline and online ones. As the common foundation of those techniques, a generic efficient and real-time algorithm to identify IPIs is urgently desired. However, the existing instance-identification approach cannot satisfy the desires. In this paper, we first formally define the concept of IPI. Next, we propose a generic IPI-identification algorithm, and prove its correctness, real-time and efficiency. We also conduct comparison experiments to illustrate that our algorithm is more efficient than the existing one in terms of both time and space. As the theoretical analyses and empirical studies exhibit, our algorithm provides the groundwork for IPI-based analyses of WSN programs in IoT environment.

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An Empirical Analysis of the Influence of Fault Space on Search-Based Automated Program Repair

Automated program repair (APR) has attracted great research attention, and various techniques have been proposed. Search-based APR is one of the most important categories among these techniques. Existing researches focus on the design of effective mutation operators and searching algorithms to better find the correct patch. Despite various efforts, the effectiveness of these techniques are still limited by the search space explosion problem. One of the key factors attribute to this problem is the quality of fault spaces as reported by existing studies. This motivates us to study the importance of the fault space to the success of finding a correct patch. Our empirical study aims to answer three questions. Does the fault space significantly correlate with the performance of search-based APR? If so, are there any indicative measurements to approximate the accuracy of the fault space before applying expensive APR techniques? Are there any automatic methods that can improve the accuracy of the fault space? We observe that the accuracy of the fault space affects the effectiveness and efficiency of search-based APR techniques, e.g., the failure rate of GenProg could be as high as $60\%$ when the real fix location is ranked lower than 10 even though the correct patch is in the search space. Besides, GenProg is able to find more correct patches and with fewer trials when given a fault space with a higher accuracy. We also find that the negative mutation coverage, which is designed in this study to measure the capability of a test suite to kill the mutants created on the statements executed by failing tests, is the most indicative measurement to estimate the efficiency of search-based APR. Finally, we confirm that automated generated test cases can help improve the accuracy of fault spaces, and further improve the performance of search-based APR techniques.

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DroidLeaks: Benchmarking Resource Leak Bugs for Android Applications

Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes. In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly managing system resources. Yet, there exist no common bug benchmarks for effectively and reliably comparing such techniques and quantitatively evaluating their strengths and weaknesses. This paper describes our initial contribution towards constructing such a benchmark. To locate real resource leak bugs, we mined 124,215 code revisions of 34 large-scale open-source Android apps. We successfully found 298 fixed resource leaks, which cover a diverse set of resource classes, from 32 out of the 34 apps. To understand the characteristics of these bugs, we conducted an empirical study, which revealed the root causes of frequent resource leaks in Android apps and common patterns of faults made by developers. With our findings, we further implemented a static checker to detect a common pattern of resource leaks in Android apps. Experiments showed that the checker can effectively locate real resource leaks in popular Android apps, confirming the usefulness of our work.

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