Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach

Abstract

Cyber threat intelligence has become increasingly critical for organizations to defend against sophisticated attacks. This paper presents a novel deep transfer learning approach for creating proactive cyber threat intelligence using hacker exploit labels. We develop a comprehensive framework that leverages advanced machine learning techniques to analyze and classify hacker exploits for proactive threat detection.

Our research addresses the challenges of:

• Exploit Classification: Automated identification and categorization of hacker exploits
• Transfer Learning: Leveraging knowledge from related domains to improve detection
• Proactive Intelligence: Moving from reactive to proactive threat detection
• Scalability: Handling large-scale exploit datasets efficiently

The framework demonstrates significant improvements in threat detection accuracy and provides actionable intelligence for security operations.

Key Contributions

1. Deep Transfer Learning Framework: Novel approach for exploit classification
2. Proactive Intelligence: Shift from reactive to proactive threat detection
3. Scalable Architecture: Efficient processing of large-scale exploit datasets
4. Actionable Intelligence: Generation of practical threat intelligence outputs

Research Impact

This work advances the field of cyber threat intelligence and provides practical tools for organizations to proactively defend against cyber threats.