Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches
–arXiv.org Artificial Intelligence
The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulnerability assessment provides important and multifaceted information to prevent and mitigate dangerous cyber-attacks in the wild. The key contributions include a systematisation of knowledge, along with a suite of novel data-driven techniques and practical recommendations for researchers and practitioners in the area. The thesis results help improve the understanding and inform the practice of assessing ever-increasing vulnerabilities in real-world software systems. This in turn enables more thorough and timely fixing prioritisation and planning of these critical security issues.
arXiv.org Artificial Intelligence
Jun-20-2023
- Country:
- Asia
- China (0.04)
- Middle East > Jordan (0.04)
- North America > United States
- Asia
- Genre:
- Overview (1.00)
- Research Report
- Experimental Study (1.00)
- New Finding (1.00)
- Promising Solution (0.67)
- Industry:
- Technology:
- Information Technology
- Artificial Intelligence
- Cognitive Science > Problem Solving (0.92)
- Machine Learning
- Ensemble Learning (0.68)
- Learning Graphical Models
- Directed Networks > Bayesian Learning (0.67)
- Undirected Networks > Markov Models (0.45)
- Neural Networks > Deep Learning (1.00)
- Performance Analysis > Accuracy (1.00)
- Statistical Learning (1.00)
- Natural Language
- Information Retrieval (1.00)
- Text Classification (1.00)
- Text Processing (1.00)
- Representation & Reasoning > Uncertainty (1.00)
- Communications
- Networks (1.00)
- Social Media (1.00)
- Data Science
- Data Mining (1.00)
- Data Quality (1.00)
- Information Management > Search (0.92)
- Security & Privacy (1.00)
- Software > Programming Languages (1.00)
- Artificial Intelligence
- Information Technology