Deep learning for source code modeling and generation: Models, applications, and challenges
Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …
A survey on data-driven software vulnerability assessment and prioritization
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …
risks to many software systems. Given the limited resources in practice, SV assessment and …
Data quality for software vulnerability datasets
The use of learning-based techniques to achieve automated software vulnerability detection
has been of longstanding interest within the software security domain. These data-driven …
has been of longstanding interest within the software security domain. These data-driven …
Data preparation for software vulnerability prediction: A systematic literature review
Software Vulnerability Prediction (SVP) is a data-driven technique for software quality
assurance that has recently gained considerable attention in the Software Engineering …
assurance that has recently gained considerable attention in the Software Engineering …
Deepcva: Automated commit-level vulnerability assessment with deep multi-task learning
It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give
early warnings about potential security risks. However, there is a lack of effort to assess …
early warnings about potential security risks. However, there is a lack of effort to assess …
Apiro: A framework for automated security tools api recommendation
Security Orchestration, Automation, and Response (SOAR) platforms integrate and
orchestrate a wide variety of security tools to accelerate the operational activities of Security …
orchestrate a wide variety of security tools to accelerate the operational activities of Security …
Software vulnerability prediction in low-resource languages: An empirical study of codebert and chatgpt
Background: Software Vulnerability (SV) prediction in emerging languages is increasingly
important to ensure software security in modern systems. However, these languages usually …
important to ensure software security in modern systems. However, these languages usually …
A Survey on Software Vulnerability Exploitability Assessment
S Elder, MR Rahman, G Fringer, K Kapoor… - ACM Computing …, 2024 - dl.acm.org
Knowing the exploitability and severity of software vulnerabilities helps practitioners
prioritize vulnerability mitigation efforts. Researchers have proposed and evaluated many …
prioritize vulnerability mitigation efforts. Researchers have proposed and evaluated many …
SmartValidator: A framework for automatic identification and classification of cyber threat data
A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres
(SOCs) to perform validation of security incidents and alerts. Security experts manually …
(SOCs) to perform validation of security incidents and alerts. Security experts manually …
Mitigating data imbalance for software vulnerability assessment: Does data augmentation help?
Background: Software Vulnerability (SV) assessment is increasingly adopted to address the
ever-increasing volume and complexity of SVs. Data-driven approaches have been widely …
ever-increasing volume and complexity of SVs. Data-driven approaches have been widely …