Related papers: Optimizing seed inputs in fuzzing with machine lea…
Fuzzing is widely used for detecting bugs and vulnerabilities, with various techniques proposed to enhance its effectiveness. To combine the advantages of multiple technologies, researchers proposed ensemble fuzzing, which integrates…
Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific…
The advent of high-throughput sequencing technologies constituted a major advance in genomic studies, offering new prospects in a wide range of applications. We propose a rigorous and flexible algorithmic solution to mapping SOLiD…
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files. We assess performance based on the temporal rate at…
Greybox fuzzing is a lightweight testing approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is…
The aims of our research are to evaluate the prediction performance of the proposed neuro-fuzzy model with System Evaluation and Estimation of Resource Software Estimation Model (SEER-SEM) in software estimation practices and to apply the…
Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons. As the LHC enters an era of high-precision measurements, a robust PDF determination…
Concolic execution and fuzzing are two complementary coverage-based testing techniques. How to achieve the best of both remains an open challenge. To address this research problem, we propose and evaluate Legion. Legion re-engineers the…
Grey box fuzzing is one of the most successful methods for automatic vulnerability detection. However,conventional Grey box Fuzzers like AFL can open perform fuzzing against the whole input and spend more time on smaller seeds with lower…
Fuzzy systems are a way to allow machines, systems and frameworks to deal with uncertainty, which is not possible in binary systems that most computers use. These systems have already been deployed for certain use cases, and fuzzy systems…
Real-world programs expecting structured inputs often has a format-parsing stage gating the deeper program space. Neither a mutation-based approach nor a generative approach can provide a solution that is effective and scalable. Large…
Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strategies. However, directly applying grey-box fuzzing to…
Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge, due to a closed-set and static perspective of the real world, which would induce the intelligent agent to make bad decisions. To…
Paper documents are widely used as an irreplaceable channel of information in many fields, especially in financial industry, fostering a great amount of demand for systems which can convert document images into structured data…
In vulnerability detection, machine learning has been used as an effective static analysis technique, although it suffers from a significant rate of false positives. Contextually, in vulnerability discovery, fuzzing has been used as an…
We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective…
The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without…
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can…
Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the…
While AI-coding assistants accelerate software development, current testing frameworks struggle to keep pace with the resulting volume of AI-generated code. Traditional fuzzing techniques often allocate resources uniformly and lack semantic…