A Comprehensive Survey of Advanced Persistent Threat Attribution: Taxonomy, Methods, Challenges and Open Research Problems
<|reference_start|>A Comprehensive Survey of Advanced Persistent Threat Attribution: Taxonomy, Methods, Challenges and Open Research Problems: Advanced Persistent Threat (APT) attribution is a critical challenge in cybersecurity and implies the process of accurately identifying the perpetrators behind sophisticated cyber attacks. It can significantly enhance defense mechanisms and inform strategic responses. With the growing prominence of artificial intelligence (AI) and machine learning (ML) techniques, researchers are increasingly focused on developing automated solutions to link cyber threats to responsible actors, moving away from traditional manual methods. Previous literature on automated threat attribution lacks a systematic review of automated methods and relevant artifacts that can aid in the attribution process. To address these gaps and provide context on the current state of threat attribution, we present a comprehensive survey of automated APT attribution. The presented survey starts with understanding the dispersed artifacts and provides a comprehensive taxonomy of the artifacts that aid in attribution. We comprehensively review and present the classification of the available attribution datasets and current automated APT attribution methods. Further, we raise critical comments on current literature methods, discuss challenges in automated attribution, and direct toward open research problems. This survey reveals significant opportunities for future research in APT attribution to address current gaps and challenges. By identifying strengths and limitations in current practices, this survey provides a foundation for future research and development in automated, reliable, and actionable APT attribution methods.<|reference_end|>
@article{rani2024a,
    title={A Comprehensive Survey of Advanced Persistent Threat Attribution:
  Taxonomy, Methods, Challenges and Open Research Problems},
    author={Nanda Rani, Bikash Saha, Sandeep Kumar Shukla},
    journal={arXiv preprint arXiv:2409.11415},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11415},
    primaryClass={cs.CR}
}