The complex optimizations supported by modern compilers allow for compiler provenance recovery at many levels. For instance, it is possible to identify the compiler family and optimization level used when building a binary, as well as the individual compiler passes applied to functions within the binary. Yet, many downstream applications of compiler provenance remain unexplored. To bridge that gap, we train and evaluate a multi-label compiler provenance model on data collected from over 27,000 programs built using LLVM 14, and apply the model to a number of security-related tasks. Our approach considers 68 distinct compiler passes and achieves an average F-1 score of 84.4%. We first use the model to examine the magnitude of compiler-induced vulnerabilities, identifying 53 information leak bugs in 10 popular projects. We also show that several compiler optimization passes introduce a substantial amount of functional code reuse gadgets that negatively impact security. Beyond vulnerability detection, we evaluate other security applications, including using recovered provenance information to verify the correctness of Rich header data in Windows binaries (e.g., forensic analysis), as well as for binary decomposition tasks (e.g., third party library detection).

Bug triaging entails a laborious process wherein triagers spend time examining new bug reports, localizing the bugs, and assigning them to the appropriate developer(s) to fix the bugs. In recent years, the adoption of automated software testing techniques (e.g., fuzzing) further complicates the process because bug hunters can submit an overwhelming number of reports in a short period. To lessen these pain points, we present an approach that extracts a fingerprint from crash information within a bug report, and returns a group of bugs with similar behaviors. Our approach uses symptoms of the crash to create a robust fingerprint, and leverages MinHashing and Locality Sensitive Hashing to match crashes, as well as a sequential pattern mining algorithm to find frequent closed sequences among bugs. Our evaluation shows that our approach outperforms contemporary approaches (e.g., finding previously unknown duplicates among 81 CVEs), and saves triagers time and effort.

With a large number of renewable powers taken into the distributed grid, frequency‐irregular inter‐harmonics would cause the mainlobe interferences (MLI) in the frequency domain, which damages detection accuracy sharply. To solve MLI, a second‐order Hann and sixth‐order Rectangular convolution window (H 2 R 6 window) are constructed. It contains low sidelobes and a narrow mainlobe, which properly solves traditional harmonic problem, e.g. spectral leakage and picket‐fence effect. Moreover, by using this window, judging whether MLI exists becomes easier, and all inter‐harmonics are distinguished into three types. Different estimation strategies are applied to aim at different types of inter‐harmonics for common harmonic and the first type inter‐harmonic, a two‐point interpolation equation is calculated directly. For the second and the third type, a zone affected by MLI slightly is partitioned, then parameter can be estimated based on two spectra in this zone. The relative frequency error of the first type is about 10 −11 , and the ones corresponding to the second and the third type are at a range from 10 −3 to 10 −10 , which depends on the frequency distance of two adjacent sinusoids. Such a precision fully meets the demand for precision instruments.

Cyber-physical systems (CPS) is a new generation of engineered systems that integrate physical systems with the capability of networked computing and control. Real-time video capture and communication is expected to be an important function in many cyber-physical systems that involve camera-equipped mobile phones. In this paper, we present AnySense, a network architecture that supports video communication between 3G phones and Internet hosts in cyber-physical systems. AnySense implements transcoding of video streams between the Internet and circuit-switched 3G cellular networks, and is transparent to 3G service providers. AnySense can support a class of ubiquitous cyber-physical systems that require video-based information collection and sharing. A prototype of AnySense has been built and a video demo is available at http://www.anyserver.org/.