mlsanityData to 7 Oct 2026

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning

arXiv 2410.11463 ↗First posted 15 Oct 2024cs.CR cs.AI cs.LGcited by 6

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Abstract (arXiv)

The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could adapt and respond effectively to the dynamic nature of malware threats. Initially, the model struggled with low accuracy levels, but through persistent adjustments to its architecture and learning algorithms, accuracy improved dramatically from about 7 percent to over 73 percent in early iterations. By the end of the training, the model consistently reached accuracy levels near 98 percent, demonstrating its strong capability to accurately recognise and attribute malware activities. This upward trajectory in training accuracy is graphically represented in the Figure, which vividly illustrates the model maturation and increasing proficiency over time.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 57 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

11 of 57 references.

References · 11

  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  2. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  3. 2024A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directionscited by 33
  4. 2024AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environmentscited by 7
  5. 2024GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Modelscited by 7
  6. 2019End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malwarecited by 3
  7. 2023ESASCF: Expertise Extraction, Generalization and Reply Framework for an Optimized Automation of Network Security Compliancecited by 3
  8. 2023D2WFP: A Novel Protocol for Forensically Identifying, Extracting, and Analysing Deep and Dark Web Browsing Activitiescited by 3
  9. 2024A Comprehensive Survey of Advanced Persistent Threat Attribution: Taxonomy, Methods, Challenges and Open Research Problemscited by 3
  10. 2024Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Responsecited by 2
  11. 2024APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
  2. 2025Recent Advances in Transformer and Large Language Models for UAV Applicationscited by 3
  3. 2025A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Securitycited by 2
  4. 2025MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validationcited by 0
  5. 2025Hierarchical Graph Neural Network for Compressed Speech Steganalysiscited by 0
  6. 2025A Relay-Chain-Powered Ciphertext-Policy Attribute-Based Encryption in Intelligent Transportation Systemscited by 0