A Comprehensive Survey of Advanced Persistent Threat Attribution: Taxonomy, Methods, Challenges and Open Research Problems

• URL: http://arxiv.org/abs/2409.11415v3
• Date: Sun, 6 Oct 2024 15:48:05 GMT
• Title: A Comprehensive Survey of Advanced Persistent Threat Attribution: Taxonomy, Methods, Challenges and Open Research Problems
• Authors: Nanda Rani, Bikash Saha, Sandeep Kumar Shukla,
• Abstract summary: Advanced Persistent Threat attribution is a critical challenge in cybersecurity. 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. Previous literature on automated threat attribution lacks a systematic review of automated methods and relevant artifacts that can aid in the attribution process.
• Score: 3.410195565199523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI. We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts. We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning [6.144680854063938]
  Advanced Persistent Threats (APTs) pose a significant security risk to organizations and industries. Mitigating these sophisticated attacks is highly challenging due to the stealthy and persistent nature of APTs. This paper aims to analyze measurements considered when recording network traffic and conclude which features contribute more to detecting APT samples.
  arXiv  Detail & Related papers  (2025-02-11T03:06:03Z)
• SoK: Towards Effective Automated Vulnerability Repair [11.028015952491991]
  The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques. This Systematization of Knowledge (SoK) provides a comprehensive overview of the landscape, encompassing both synthetic and real-world vulnerabilities.
  arXiv  Detail & Related papers  (2025-01-31T00:35:55Z)
• Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering [0.0]
  The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete. Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems. This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy.
  arXiv  Detail & Related papers  (2025-01-09T11:38:58Z)
• SoK: Watermarking for AI-Generated Content [112.9218881276487]
  Watermarking schemes embed hidden signals within AI-generated content to enable reliable detection. Watermarks can play a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception. This work aims to guide researchers in advancing watermarking methods and applications, and support policymakers in addressing the broader implications of GenAI.
  arXiv  Detail & Related papers  (2024-11-27T16:22:33Z)
• AutoSurvey: Large Language Models Can Automatically Write Surveys [77.0458309675818]
  This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys. Traditional survey paper creation faces challenges due to the vast volume and complexity of information. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
  arXiv  Detail & Related papers  (2024-06-10T12:56:06Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey [97.33926242130732]
  Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
  arXiv  Detail & Related papers  (2023-10-23T18:11:32Z)
• Adversarial attacks and defenses in explainable artificial intelligence: A survey [11.541601343587917]
  Recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models.
  arXiv  Detail & Related papers  (2023-06-06T09:53:39Z)
• Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques [77.34726150561087]
  We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research. Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
  arXiv  Detail & Related papers  (2022-07-18T09:59:21Z)
• Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports [5.789368942487406]
  We evaluate several classification approaches to automatically retrieve Tactics, Techniques and Procedures from unstructured text. We present rcATT, a tool built on top of our findings and freely distributed to the security community to support cyber threat report automated analysis.
  arXiv  Detail & Related papers  (2020-04-29T16:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.