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Advancing Software Vulnerability Scoring: A Statistical Approach with Machine Learning Techniques and GridSearchCV Parameter Tuning
The growing complexity, diversity, and importance of software pose a significant threat to computer system security due to exploitable software...
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Comprehensive vulnerability aspect extraction
Extracting valuable information from unstructured vulnerability reports constitutes a fundamental task in numerous cybersecurity applications....
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Vulnerability management in Linux distributions
Vulnerabilities in software systems not only lead to loss of revenue, but also to loss of reputation and trust. To avoid this, software providers...
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Enriching Vulnerability Reports Through Automated and Augmented Description Summarization
Security incidents and data breaches are increasing rapidly, and only a fraction of them is being reported. Public vulnerability databases, e.g.,...
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On the coordination of vulnerability fixes
The Common Vulnerabilities and Exposures (CVE) program is dedicated to analyzing vulnerabilities, then to assigning a unique ID to them and...
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Analysis and implementation of semi-automatic model for vulnerability exploitations of threat agents in NIST databases
Proactive security plays a vital role in preventing the attack before entering active mode. In the modern information environment, it depends on the...
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Dynamic vulnerability severity calculator for industrial control systems
The convergence of information and communication technologies has introduced new and advanced capabilities to Industrial Control Systems. However,...
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Vulnerability Assessment and Penetration Testing
Vulnerability assessment is a comprehensive process aimed at identifying, quantifying, and prioritizing vulnerabilities within a system. This system...
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ProRLearn: boosting prompt tuning-based vulnerability detection by reinforcement learning
Software vulnerability detection is a critical step in ensuring system security and data protection. Recent research has demonstrated the...
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Entity Alignment Based on Multi-view Interaction Model in Vulnerability Knowledge Graphs
Entity alignment (EA) aims to match the same entities in different Knowledge Graphs (KGs), which is a critical task in KG fusion. EA has recently...
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XSS Vulnerability Test Enhancement for Progressive Web Applications
Progressive Web Applications produce false negative results when scanned with security vulnerability scanners. In this paper the authors investigate...
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Identify Vulnerability Types: A Cross-Project Multiclass Vulnerability Classification System Based on Deep Domain Adaptation
Software Vulnerability Detection(SVD) is a important means to ensure system security due to the ubiquity of software. Deep learning-based approaches...
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A Framework for TLS Implementation Vulnerability Testing in 5G
A 5G TLS implementation vulnerability testing framework is proposed. By constructing a TLS vulnerability database using the public TLS security...
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VulNet: Towards improving vulnerability management in the Maven ecosystem
Developers rely on software ecosystems such as Maven to manage and reuse external libraries (i.e., dependencies). Due to the complexity of the used...
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Estimating Time-To-Compromise for Industrial Control System Attack Techniques Through Vulnerability Data
When protecting the Industrial Control Systems against cyber attacks, it is important to have as much information as possible to allocate defensive...
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Generating ICS vulnerability playbooks with open standards
Organizations face attacks on industrial control systems (ICS) as vulnerabilities are pervasive. However, patching vulnerable systems by simply...
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Assessing Vulnerability from Its Description
This paper shows an end-to-end Artificial Intelligence (AI) system to estimate the severity level and the various Common Vulnerability Scoring System...
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A Software Vulnerability Prediction Model Using Traceable Code Patterns and Software Metrics
The goal of this research is to build a vulnerability prediction model to assist developers in evaluating the security of software systems during the...
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An empirical study of text-based machine learning models for vulnerability detection
With an increase in complexity and severity, it is becoming harder to identify and mitigate vulnerabilities. Although traditional tools remain...
