mlsanityData to 7 Oct 2026

XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics

arXiv 2402.02452 ↗First posted 4 Feb 2024cs.CR cs.AIcited by 1

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Abstract (arXiv)

With the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify and explain the results to the stakeholders of CF, such as forensic analysts and members of the court, for them to make an informed decision. If we want to apply AI successfully in CF, there is a need to develop trust in AI systems. Some other factors in accepting the use of AI in CF are to make AI authentic, interpretable, understandable, and interactive. This way, AI systems will be more acceptable to the public and ensure alignment with legal standards. An explainable AI (XAI) system can play this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and is still in its infancy. In this paper, we explore and make a case for the significance and advantages of XAI-CF. We strongly emphasize the need to build a successful and practical XAI-CF system and discuss some of the main requirements and prerequisites of such a system. We present a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF. We discuss some challenges facing XAI-CF. We also provide some concrete solutions to these challenges. We identify key insights and future research directions for building XAI applications for CF. This paper is an effort to explore and familiarize the readers with the role of XAI applications in CF, and we believe that our work provides a promising basis for future researchers interested in XAI-CF.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 54 of its 270 references that are in the map. First posted on arXiv 4 Feb 2024.

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54 of 270 references.

References · 54

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  7. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  8. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  9. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  10. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  11. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  12. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
38 more
  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  3. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  4. 2018Metrics for Explainable AI: Challenges and Prospectscited by 257
  5. 2019Explainable Machine Learning in Deploymentcited by 210
  6. 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 192
  7. 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
  8. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  9. 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
  10. 2020Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAIcited by 125
  11. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  12. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 98
  13. 2020The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategiescited by 91
  14. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  15. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  16. 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
  17. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  18. 2020On quantitative aspects of model interpretabilitycited by 71
  19. 2019Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AIcited by 63
  20. 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 63
  21. 2014Falling Rule Listscited by 58
  22. 2017Interpretable & Explorable Approximations of Black Box Modelscited by 54
  23. 2018Stakeholders in Explainable AIcited by 48
  24. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  25. 2018Transparency and Explanation in Deep Reinforcement Learning Neural Networkscited by 40
  26. 2022SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretabilitycited by 28
  27. 2022Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Researchcited by 24
  28. 2021Fooling Partial Dependence via Data Poisoningcited by 16
  29. 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 15
  30. 2015Interpretable Two-level Boolean Rule Learning for Classificationcited by 10
  31. 2021When and How to Fool Explainable Models (and Humans) with Adversarial Examplescited by 10
  32. 2023A Survey on Explainable Artificial Intelligence for Cybersecuritycited by 9
  33. 2020SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigationcited by 7
  34. 2016Making Tree Ensembles Interpretablecited by 6
  35. 2020Is there a role for statistics in artificial intelligence?cited by 5
  36. 2021Research trends, challenges, and emerging topics of digital forensics: A review of reviewscited by 5
  37. 2023ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinementcited by 5
  38. 2019Founding The Domain of AI Forensicscited by 4

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