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TIPS: Threat Actor Informed Prioritization of Applications using SecEncoder

arXiv 2411.07519 ↗First posted 12 Nov 2024cs.CR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 47 references that are in the map. First posted on arXiv 12 Nov 2024.

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References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  6. 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 419
  7. 2023A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verificationcited by 42
  8. 2023HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)cited by 25
  9. 2023LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testingcited by 16
  10. 2023An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approachcited by 12
  11. 2023PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention networkcited by 10
  12. 2022Vulnerability Prioritization: An Offensive Security Approachcited by 1

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