Cyber Threat Intelligence #

• Thesis ID: 24-04
• Research Proposal: Enhancing National Cyber Threat Intelligence for Sweden Using AI-Based Technologies

Abstract #

The increasing complexity and frequency of cyber threats necessitate advanced, proactive defense mechanisms. This research proposes to enhance Sweden’s national cyber threat intelligence capabilities by integrating AI-based technologies. The study aims to explore, develop, and implement AI-driven solutions for real-time threat detection, analysis, and mitigation, thereby strengthening Sweden’s cyber resilience.

1. Introduction #

1.1 Background #

Cyber threats are evolving rapidly, with adversaries employing sophisticated tactics that traditional defense mechanisms often struggle to counter. National cybersecurity frameworks must therefore adopt cutting-edge technologies to stay ahead. Artificial Intelligence (AI) offers promising solutions for automating threat detection, improving accuracy, and enabling rapid response.

1.2 Problem Statement #

Sweden, like many nations, faces growing cyber threats targeting its critical infrastructure, government institutions, and private sector. Current cyber threat intelligence (CTI) frameworks are often reactive, lacking the capability to predict and mitigate threats in real-time. This research seeks to address these limitations by integrating AI-based technologies into Sweden’s national CTI framework.

1.3 Objectives #

1. To evaluate the current state of Sweden’s national cyber threat intelligence capabilities.
2. To explore AI-based technologies suitable for enhancing cyber threat intelligence.
3. To develop an AI-driven model for real-time threat detection and analysis.
4. To implement and test the proposed AI model in a simulated environment.
5. To provide recommendations for integrating AI-based CTI into Sweden’s national cybersecurity strategy.

2. Literature Review #

2.1 Cyber Threat Intelligence #

Overview of CTI, its importance, and current methodologies used in national cybersecurity frameworks.

2.2 AI in Cybersecurity #

Examination of AI technologies, including machine learning, natural language processing, and anomaly detection, and their applications in cybersecurity.

2.3 Case Studies #

Analysis of countries that have successfully integrated AI into their national CTI frameworks, highlighting best practices and lessons learned.

3. Research Methodology #

3.1 Phase 1: Evaluation of Current CTI Capabilities #

1. Stakeholder Interviews: Conduct interviews with key stakeholders in Sweden’s cybersecurity landscape to understand the current CTI framework and its limitations.
2. Literature Review: Review existing documentation, reports, and research papers on Sweden’s national CTI capabilities.

3.2 Phase 2: Exploration of AI Technologies #

1. Technology Review: Identify and evaluate AI technologies that can be applied to CTI, focusing on threat detection, data analysis, and incident response.
2. Feasibility Study: Assess the feasibility of integrating these technologies into Sweden’s CTI framework.

3.3 Phase 3: Development of AI-Driven CTI Model #

1. Model Design: Design an AI-driven model for real-time threat detection and analysis, incorporating machine learning algorithms and data analytics.
2. Data Collection: Gather relevant data from various sources, including network traffic, threat databases, and incident reports, to train and test the model.

3.4 Phase 4: Implementation and Testing #

1. Simulated Environment: Implement the AI model in a controlled, simulated environment to evaluate its effectiveness.
2. Testing and Validation: Conduct extensive testing to validate the model’s accuracy, reliability, and performance in detecting and mitigating threats.

3.5 Phase 5: Recommendations and Integration #

1. Analysis: Analyze the results of the testing phase to identify strengths, weaknesses, and areas for improvement.
2. Recommendations: Develop recommendations for integrating the AI-based CTI model into Sweden’s national cybersecurity strategy.
3. Documentation: Document the entire research process, findings, and recommendations in a comprehensive thesis report.

4. Expected Outcomes #

1. Enhanced CTI Capabilities: A robust AI-driven CTI model that significantly improves Sweden’s ability to detect, analyze, and respond to cyber threats in real-time.
2. Strategic Recommendations: Practical recommendations for integrating AI technologies into national CTI frameworks.
3. Academic Contributions: Publication of research findings in academic journals and conferences, contributing to the global knowledge base on AI in cybersecurity.

5. Timeline #

A tentative timeline.

6. Conclusion #

This research aims to enhance Sweden’s national cyber threat intelligence capabilities through the integration of AI-based technologies. By developing an AI-driven CTI model, this study will provide a proactive, real-time approach to cyber threat detection and mitigation. The findings and recommendations will contribute to the advancement of cybersecurity strategies, not only for Sweden but also for the global community.

7. References #

1. Literature on cyber threat intelligence and national cybersecurity frameworks.
2. Documentation on AI technologies and their applications in cybersecurity.
3. Research papers and case studies on the integration of AI into national CTI frameworks.
4. From Sands to Mansions: Enabling Automatic Full-Life-Cycle Cyberattack Construction with LLM
5. GPT-Powered MITRE ATT&CK Copilot