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AI-driven cybersecurity framework for software development based on the ANN-ISM paradigm
Habib Ullah Khan1, Rafiq Ahmad Khan2, Hathal S Alwageed3
1Department of Accounting and Information Systems, College of Bussiness and Economics, Qatar University, Doha, Qatar. Habib.khan@qu.edu.qa.
Scientific Reports
|April 18, 2025
Summary
This study introduces an AI-driven cybersecurity framework using Artificial Neural Networks (ANN) and Interpretive Structural Modeling (ISM) to enhance threat detection and risk assessment in software development. The AI model significantly improves security by identifying and mitigating coding vulnerabilities more effectively than traditional methods.
Area of Science:
- Computer Science
- Cybersecurity
- Artificial Intelligence
Background:
- Traditional cybersecurity measures are failing to keep pace with evolving threats in software development.
- Increasing reliance on software applications necessitates advanced security solutions.
- Artificial Intelligence (AI) offers a promising approach to adapt and improve cybersecurity defenses.
Purpose of the Study:
- To present an innovative AI-driven cybersecurity framework for secure software development.
- To improve threat detection, vulnerability assessment, and risk response within the Software Development Life Cycle (SDLC).
- To integrate AI tools with cybersecurity modeling for dynamic and intelligent security.
Main Methods:
- Systematic literature review (SLR) to evaluate existing cybersecurity risks and best practices.
- Empirical survey to validate SLR findings.
- Hybrid approach combining Artificial Neural Network (ANN) for real-time threat detection and Interpretive Structural Modeling (ISM) for analyzing risk interdependencies.
- Case study to evaluate the AI-driven Mitigation Model for Secure Software Coding.
- Multi-level categorization system (5 levels) to assess organizational maturity.
Main Results:
- Identification of 15 cybersecurity risks and vulnerabilities in software coding.
- Compilation of 158 AI-driven best practices for risk mitigation.
- Development of a scalable model addressing cybersecurity risks across different maturity levels.
- AI demonstrated superior performance over traditional systems in detecting and fixing security weaknesses.
- Organizations at higher maturity levels (4-5) require further adoption of AI-based protection tools.
Conclusions:
- The proposed ANN-ISM framework effectively integrates AI with cybersecurity modeling for secure software coding.
- The AI-driven system provides valuable insights for selecting tailored security enhancements based on development stages.
- The framework supports automated threat analysis, enhancing organizational vigilance against cybersecurity threats.
- Merging AI systems with secure coding principles improves the practical application of AI-generated cybersecurity insights.
Keywords:
AIANN-ISM modelingCase studyCybersecurity maturity levelsCybersecurity risks and practicesEmpirical surveySecure software codingSystematic literature reviewMore Related Videos
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