TIPS: Threat Actor Informed Prioritization of Applications using SecEncoder
Bulut, Muhammed Fatih, Tamersoy, Acar, Ahmad, Naveed, Liu, Yingqi, Greenwald, Lloyd
–arXiv.org Artificial Intelligence
This paper introduces TIPS: Threat Actor Informed Prioritization using SecEncoder, a specialized language model for security. TIPS combines the strengths of both encoder and decoder language models to detect and prioritize compromised applications. By integrating threat actor intelligence, TIPS enhances the accuracy and relevance of its detections. Extensive experiments with a real-world benchmark dataset of applications demonstrate TIPS's high efficacy, achieving an F-1 score of 0.90 in identifying malicious applications. Additionally, in real-world scenarios, TIPS significantly reduces the backlog of investigations for security analysts by 87%, thereby streamlining the threat response process and improving overall security posture.
arXiv.org Artificial Intelligence
Nov-11-2024
- Country:
- Europe (0.67)
- Genre:
- Research Report (0.82)
- Industry:
- Information Technology > Security & Privacy (1.00)
- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning
- Neural Networks > Deep Learning (0.47)
- Performance Analysis > Accuracy (0.69)
- Natural Language > Large Language Model (1.00)
- Machine Learning
- Communications > Networks (0.94)
- Data Science > Data Mining (0.93)
- Security & Privacy (1.00)
- Artificial Intelligence
- Information Technology