相关论文: Neutaint: Efficient Dynamic Taint Analysis with Ne…

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over…

Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection. However, DTA is fundamentally…

Information flows are intrinsic properties of an multi-stage manufacturing systems (MMS). In computer security, a basic information flow tracking technique is dynamic taint analysis (DTA). DTA tracks taint propagation from one data variable…

Dynamic taint analysis (DTA) has been widely used in various security-relevant scenarios that need to track the runtime information flow of programs. Dynamic binary instrumentation (DBI) is a prevalent technique in achieving effective…

Artificial Neural Networks (ANNs) require significant amounts of data and computational resources to achieve high effectiveness in performing the tasks for which they are trained. To reduce resource demands, various techniques, such as…

Existing methods for measuring training sample influence on models only provide static, overall measurements, overlooking how sample influence changes during training. We propose Dynamic Influence Tracker (DIT), which captures the…

Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models…

This paper presents a fully automated static analysis approach and a tool, Taint-Things, for the identification of tainted flows in SmartThings IoT apps. Taint-Things accurately identifies all tainted flows reported by one of the…

Energy-harvesting technology provides a promising platform for future IoT applications. However, since communication is very expensive in these devices, applications will require inference "beyond the edge" to avoid wasting precious energy…

Over the years, static taint analysis emerged as the analysis of choice to detect some of the most common web application vulnerabilities, such as SQL injection (SQLi) and cross-site scripting (XSS)~\cite{OWASP}. Furthermore, from an…

We present RETA (Relative Timing Analysis), a differential timing analysis technique to verify the impact of an update on the execution time of embedded software. Timing analysis is computationally expensive and labor intensive. Software…

Real-time high-accuracy optical flow estimation is critical for a variety of real-world robotic applications. However, current learning-based methods often struggle to balance accuracy and computational efficiency: methods that achieve high…

Taint analysis using explicit whole-program data-dependence graphs is powerful for vulnerability discovery but faces two major challenges. First, accurately modeling taint propagation through calls to external library procedures requires…

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task…

The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known…

Diffusion tensor imaging (DTI) holds significant importance in clinical diagnosis and neuroscience research. However, conventional model-based fitting methods often suffer from sensitivity to noise, leading to decreased accuracy in…

Generic taint analysis is a pivotal technique in software security. However, it suffers from staggeringly high overhead. In this paper, we explore the hypothesis whether just-in-time (JIT) generation of fast paths for tracking taint can…

Mutation analysis is one of the most effective, but costly means of assessing the ability of software test suites to prevent bugs. Traditional mutation analysis involves producing and evaluating syntactic variants of the original to check…

Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging. To address these limitations, we have developed a…

Transformers (Vaswani et al., 2017) have brought a remarkable improvement in the performance of neural machine translation (NMT) systems but they could be surprisingly vulnerable to noise. In this work, we try to investigate how noise…