As software systems grow increasingly complex and interconnected, detecting vulnerabilities in source code has become a critical and challenging task. Traditional static analysis methods often fall short in capturing deep, context-dependent vulnerabi...
Vulnerability detection in software source code is crucial in ensuring software security. Existing models face challenges with dataset class imbalance and long training times. To address these issues, this paper introduces a multi-feature screening a...
With the widespread application of the Go language, the demand for vulnerability detection in Go programs is increasing. Existing detection models and methods have deficiencies in extracting source code features of Go programs and mainly focus on det...
Code vulnerability detection is a crucial approach to ensuring software security, aiming to automatically identify potential security vulnerabilities in source code. However, existing static vulnerability detection methods often face challenges such...
Vulnerability is one of the root causes of network intrusion. An effective way to mitigate security threats is to discover and patch vulnerabilities before an attack. Traditional vulnerability detection methods rely on manual participation and incur...
Software vulnerabilities in web applications are one of the sensitive points in data and application security. Although closing a vulnerability after it is detected in web applications seems to be a solution, detecting vulnerabilities in the source c...
As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant poten...
In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevat...
Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing eff...
The most severe problem in cross-programming languages is feature extraction due to different tokens in different programming languages. To solve this problem, we propose a cross-programming-language vulnerability detection method in this paper, IRC-...
Currently, enhancing the efficiency of vulnerability detection and assessment remains relevant. We investigate a new approach for the detection of vulnerabilities that can be used in cyber attacks and assess their severity for further effective respo...
Deep learning-based vulnerability detection models have received widespread attention; however, these models are susceptible to adversarial attack, and adversarial examples are a primary research direction to improve the robustness of the models. The...
Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of so...
Software developers represent the bastion of application security against the overwhelming cyber-attacks which target all organizations and affect their resilience. As security weaknesses which may be introduced during the process of code writing are...
Due to the proliferation of large language models (LLMs) and their widespread use in applications such as ChatGPT, there has been a significant increase in interest in AI over the past year. Multiple researchers have raised the question: how will AI...
With the increasing scale and complexity of software, the advantages of using neural networks for static vulnerability detection are becoming increasingly prominent. Before inputting into a neural network, the source code needs to undergo word embedd...
Following advances in machine learning and deep learning processing, cyber security experts are committed to creating deep intelligent approaches for automatically detecting software vulnerabilities. Nowadays, many practices are for C and C++ program...
With the growth of trusted computing demand for big data analysis, cloud computing platforms are reshaping trusted data infrastructure by integrating Blockchain as a Service (BaaS), which uses elastic resource scheduling and heterogeneous hardware ac...
A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role...
Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human interv...
Current static detection technology for web application vulnerabilities relies highly on specific vulnerability patterns, while dynamic analysis technology has the problem of low vulnerability coverage. In order to improve the ability to detect unkno...
The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular one...
This paper solves the problem of modeling the scheme for developing software systems, which can be used in building solutions for secure energy networks. A development scheme is proposed in a set of representations through which each program of the s...
Fuzz testing technology is an important approach to detecting SQL injection vulnerabilities. Among them, coverage-guided gray-box fuzz testing technology is the current research focus, and has been proved to be an effective method. However, for SQL i...
As is well known, smart contracts on the blockchain store plenty of digital assets, and these contracts deployed on the blockchain are difficult to be modified. For this reason, the analysis and detection of smart contract vulnerabilities have receiv...
With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs,...
With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the...
Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexp...
The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose...
The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis&mdas...
In the past decades, due to the popularity of cloning open-source software, 1-day vulnerabilities are prevalent among cyber-physical devices. Detection tools for 1-day vulnerabilities effectively protect users who fail to adopt 1-day vulnerability pa...
With the blooming of blockchain-based smart contracts in decentralized applications, the security problem of smart contracts has become a critical issue, as vulnerable contracts have resulted in severe financial losses. Existing research works have e...
Automated vulnerability detection is one of the critical issues in the realm of software security. Existing solutions to this problem are mostly based on features that are defined by human experts and directly lead to missed potential vulnerability....
Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and...
Automatic Exploit Generation (AEG) involves automatically discovering paths in a program that trigger vulnerabilities, thereby generating exploits. While there is considerable research on heap-related vulnerability detection, such as detecting Heap O...
Detecting vulnerabilities in programs is an important yet challenging problem in cybersecurity. The recent advancement in techniques of natural language understanding enables the data-driven research on automated code analysis to embrace Pre-trained...
In recent years, deep learning-based vulnerability detection has drawn wide attention for its data-driven ability to analyze code semantics and learn vulnerability patterns without predefined models. However, data distribution differences across proj...
Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerabili...
Deep-learning-based vulnerability detection has made substantial progress, but two limitations remain prominent. Sequence-based methods linearize source code and thus weaken the explicit modeling of control-flow and data-flow dependencies. Graph-base...
In the realm of software development, quality reigns supreme, but the ever-present danger of vulnerabilities threatens to undermine this fundamental principle. Insufficient early vulnerability identification is a key factor in releasing numerous apps...
Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adapta...
Smart contracts are at the core of blockchain technology, but the cost of fixing their security vulnerabilities is high, making pre-deployment vulnerability detection crucial. Existing methods rely on fixed rules, which have limitations in accuracy a...
The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current...
Vulnerabilities threaten the security of information systems. It is crucial to detect and patch vulnerabilities before attacks happen. However, existing vulnerability detection methods suffer from long-term dependency, out of vocabulary, bias towards...
Static analysis is one of the techniques used today to analyze source codes and minimize the issue of software vulnerability. Static analysis has the ability to observe all possible software paths in an application through the scrutiny of a web appli...
The widespread integration of third-party components (TPCs) in Internet of Things (IoT) firmware significantly increases the risk of software vulnerabilities, especially in resource-constrained devices deployed in sensitive environments. Binary Code...
The wide application of Ethereum smart contracts in the Internet of Things, finance, medical, and other fields is associated with security challenges. Traditional detection methods detect vulnerabilities by stacking hard rules, which are associated w...
Content Management Systems (CMS) have fundamentally transformed the digital landscape, powering a substantial majority of the modern web. However, the ubiquity of platforms such as WordPress, Joomla, and Drupal has made them primary targets for cyber...
Programmable Logic Controllers (PLCs), widely used in industrial automation, are often programmed in IEC 61131-3 Structured Text (ST), which is prone to subtle logic vulnerabilities. Traditional tools like static analysis and fuzzing struggle with th...
Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated their significance and practicability in detecting software defects. ASATs assist developers to identify potential vulnerabilities, errors, and security hazard...
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