Kazakhstan is historically a livestock-producing country. For the first time in this study, we attempted to assess the vulnerability of nomadic pastoralists in Kazakhstan to climate change using the Livelihood Vulnerability Index (LVI). To collect da...
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-...
Deep learning has become an important approach for automated software vulnerability detection. However, due to domain shift, existing models often suffer from significant performance degradation when applied to unseen projects. To address this issue,...
Stateful network protocol fuzzing is one of the essential means for ensuring network communication security. However, the existing methods have problems, including frequent auxiliary message interaction, no in-depth state-space exploration, and high...
Java middleware platforms expose powerful management functions through HTTP-accessible interfaces such as Jolokia. This article discusses the analysis of CVE-2023-50780 in Apache ActiveMQ Artemis by framing the vulnerability as a management-plane sta...
Generative AI enables both software engineers and non-programmer domain experts to easily generate executable code. However, software engineers often lack domain-specific nuance to detect subtle AI logic errors, while domain experts typically lack th...
Firmware forms the persistent software layer controlling embedded and Internet-of-Things (IoT) devices, industrial controllers, automotive systems, and cyber-physical infrastructure. Vulnerabilities in firmware enable remote compromise, supply-chain...
Binary code similarity detection (BCSD) plays a crucial role in various computer security applications, including vulnerability detection, malware detection, and software component analysis. With the development of the Internet of Things (IoT), there...
Contemporary vulnerability management relies on the Common Vulnerability Scoring System (CVSS) and the Exploit Prediction Scoring System (EPSS), both of which evaluate Common Vulnerabilities and Exposures (CVE) entry in isolation, disregarding the ne...
With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and...
Java Management Extensions (JMX) are indispensable for managing and administrating Java software solutions, yet when exposed through HTTP bridges such as Jolokia they can radically enlarge an application’s attack surface. This paper presents th...
Introduction: Regional differences in socioeconomic status (SES) are well known, and we believe that the use of geocoding (zip code) can facilitate the introduction of targeted interventions for underserved populations. This is a single-center, retro...
Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent way...
As the world accelerates toward a sustainable future with electric vehicles (EVs), smartphone applications have become an indispensable tool for drivers. These applications, developed by both EV manufacturers and third-party developers, offer functio...
The European rural population has been in continuous decline since the 20th century. In the early decades, depopulation was primarily driven by rural-to-urban migration. Since 1960, demographic decline has intensified due to low birth rates and popul...
Software attack surfaces define the external boundaries—entry points, communication channels, and sensitive data stores through which adversaries may compromise a system. This paper introduces a scoring mechanism that produces a normalized atta...
Modern machine learning approaches for vulnerability detection achieve strong performance within specific datasets, yet their reliability degrades under domain shift, limiting their effectiveness for real-world secure software development lifecycle (...
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...
Recent years have witnessed a rapid growth of code-reuse attacks in advance persistent threats and cyberspace crimes. Carefully crafted code-reuse exploits circumvent modern protection mechanisms and hijack the execution flow of a program to perform...
Software vulnerability mining is important for improving software reliability and security. Compared with sequence-based models, graph neural networks (GNNs) can explicitly model program structures such as syntax, control flow, data flow, call relati...
Aiming at the problem of insufficient technology for detecting smart contract vulnerabilities and the lack of improvement of certain detection tools, a method for the expansion and improvement of the internal module of the Mythril method is proposed....
About half of all exploit codes will become available within about two weeks of the release date of its vulnerability. However, 80% of the released vulnerabilities are never exploited. Since putting the same effort to eliminate all vulnerabilities ca...
Large language models (LLMs) have shown remarkable potential for automatic code generation. Yet, these models share a weakness with their human counterparts: inadvertently generating code with security vulnerabilities that could allow unauthorized at...
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...
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...
Internet of Things Operating Systems (IoT OSs) run, manage and control IoT devices. Therefore, it is important to secure the source code for IoT OSs, especially if they are deployed on devices used for human care and safety. In this paper, we report...
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...
Broken access control vulnerabilities pose significant security risks to the protected web interfaces of IoT devices, enabling adversaries to gain unauthorized access to sensitive configurations and even use them as stepping stones for attacking the...
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...
Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detecti...
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...
Vulnerable-code auditing is critical within the Software Development Life Cycle (SDLC) as timely remediation reduces exploitation risks from sensitive-data exposure to full system compromise. Although Automatic Program Repair (APR) has advanced throu...
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...
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...
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,...
Automated Program Repair (APR) is increasingly used for vulnerability patching, yet many existing methods focus primarily on syntactic similarity between vulnerable and fixed code, with limited guarantees of semantic correctness and limited alignment...
Today, as PHP application technology is becoming increasingly mature, the functions of modern multi-layer web applications are becoming more and more complete, and the complexity is also gradually increasing. While providing developers with various b...
Potential software weakness, which can lead to exploitable security vulnerabilities, continues to pose a risk to computer systems. According to Common Vulnerability and Exposures, 14,714 vulnerabilities were reported in 2017, more than twice the numb...
With the rapid growth of IoT devices, ensuring the security of embedded firmware has become a critical concern. Despite advances in existing vulnerability discovery methods, previous research has been limited to vulnerabilities occurring in binary pr...
This work has developed a Deep Learning model that analyses the semantics of the Python code used when working with TensorFlow and detects vulnerabilities to improve data security and bug recognition. This research not only seeks to improve the secur...
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...
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...
Many correlates of poor mental health at university are known, but not why students in the same cohort fare so differently. This study aimed to derive a testable account of how mental health risk forms among university students. A grounded theory stu...
Advanced Driver Assistance Systems (ADASs) are crucial components of intelligent vehicles, equipped with a vast code base. To enhance the security of ADASs, it is essential to mine their vulnerabilities and corresponding exploitation methods. However...
Aim: Our aim was to examine the association between emergency dental care use and dental providers’ availability and ZIP code social vulnerability index. Methods: This cross-sectional observational study mapped variations in emergency dental ca...
In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of address...
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...
Background/Objectives: Functional vulnerability may identify older adults hospitalized with Crohn’s disease flares who are at increased risk for adverse outcomes, but its prognostic significance in this setting remains incompletely defined. We...
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....
With the evolution of software systems, their size and complexity are rising rapidly. Identifying vulnerabilities as early as possible is crucial for ensuring high software quality and security. Just-in-time (JIT) vulnerability prediction, which aims...
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