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A Case Study for Declarative Pattern Mining in Digital Forensics

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Rules and Reasoning (RuleML+RR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14244))

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Abstract

In this short paper, we briefly describe the application of a declarative AI approach to a case study concerning the analysis of real-world phone recordings. In particular, we summarize the general results obtained for a couple of mining tasks, one being sequential pattern mining and the other contrast pattern mining, reformulated within the framework of Answer Set Programming.

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Notes

  1. 1.

    DigForASP: https://digforasp.uca.es/.

  2. 2.

    ISO8601 format, HH: MM: SS.

  3. 3.

    https://clingraph.readthedocs.io/en/latest/.

  4. 4.

    External report to ASP encoding.

  5. 5.

    External report to ASP encoding.

References

  1. Alam, S.: Applying natural language processing for detecting malicious patterns in android applications. Forensic Sci. Int.: Digit. Invest. 39, 301270 (2021). https://www.sciencedirect.com/science/article/pii/S2666281721001888

  2. Besnard, P., Guyet, T.: Declarative mining of negative sequential patterns. In: DPSW 2020–1st Declarative Problem Solving Workshop, pp. 1–8 (2020)

    Google Scholar 

  3. Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011). http://doi.acm.org/10.1145/2043174.2043195

  4. Casino, F., et al.: Research trends, challenges, and emerging topics in digital forensics: a review of reviews. IEEE Access 10, 25464–25493 (2022)

    Article  Google Scholar 

  5. Chen, Y., Gan, W., Wu, Y., Yu, P.S.: Contrast pattern mining: a survey (2022). https://arxiv.org/abs/2209.13556

  6. Costantini, S., De Gasperis, G., Olivieri, R.: Digital forensics evidence analysis: an answer set programming approach for generating investigation hypotheses. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 242–249. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23264-5_21

    Chapter  MATH  Google Scholar 

  7. Costantini, S., De Gasperis, G., Olivieri, R.: Digital forensics and investigations meet artificial intelligence. Ann. Math. Artif. Intell. 86(1), 193–229 (2019)

    Article  MathSciNet  Google Scholar 

  8. Dong, G., Bailey, J.: Contrast Data Mining: Concepts, Algorithms, and Applications. CRC Press, Boca Raton (2012)

    Google Scholar 

  9. Garfinkel, S.L.: Digital forensics research: the next 10 years. Digit. Invest. 7, S64–S73 (2010). https://www.sciencedirect.com/science/article/pii/S1742287610000368

  10. Gebser, M., Guyet, T., Quiniou, R., Romero, J., Schaub, T.: Knowledge-based sequence mining with ASP. In: IJCAI 2016–25th International Joint Conference on Artificial Intelligence, p. 8. AAAI (2016)

    Google Scholar 

  11. Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + control: preliminary report. arXiv preprint: arXiv:1405.3694 (2014)

  12. Guns, T., Dries, A., Nijssen, S., Tack, G., De Raedt, L.: MiningZinc: a declarative framework for constraint-based mining. Artif. Intell. 244, 6–29 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  13. Guyet, T., Moinard, Y., Quiniou, R., Schaub, T.: Efficiency analysis of ASP encodings for sequential pattern mining tasks. In: Pinaud, B., Guillet, F., Cremilleux, B., de Runz, C. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 732, pp. 41–81. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65406-5_3

    Chapter  Google Scholar 

  14. Jabbour, S., Sais, L., Salhi, Y.: Decomposition based SAT encodings for itemset mining problems. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 662–674. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_52

    Chapter  Google Scholar 

  15. Leone, N., et al.: Enhancing DLV for large-scale reasoning. In: Balduccini, M., Lierler, Y., Woltran, S., et al. (eds.) LPNMR 2019. Lecture Notes in Computer Science(), vol. 11481. Springer, Cham (2015)

    Google Scholar 

  16. Lisi, F.A., Sterlicchio, G.: Declarative pattern mining in digital forensics: preliminary results. In: Calegari, R., Ciatto, G., Omicini, A. (eds.) Proceedings of the 37th Italian Conference on Computational Logic, Bologna, Italy, June 29 - July 1, 2022. CEUR Workshop Proceedings, vol. 3204, pp. 232–246. CEUR-WS.org (2022). http://ceur-ws.org/Vol-3204/paper_23.pdf

  17. Lisi, F.A., Sterlicchio, G.: Mining sequences in phone recordings with answer set programming. In: Bruno, P., Calimeri, F., Cauteruccio, F., Maratea, M., Terracina, G., Vallati, M. (eds.) Joint Proceedings of the 1st International Workshop on HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA 2022) and the 29th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA 2022) co-located with the 16th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR 2022), Genova Nervi, Italy, September 5, 2022. CEUR Workshop Proceedings, vol. 3281, pp. 34–50. CEUR-WS.org (2022). http://ceur-ws.org/Vol-3281/paper4.pdf

  18. Lisi, F.A., Sterlicchio, G.: A declarative approach to contrast pattern mining. In: Dovier, A., Montanari, A., Orlandini, A. (eds.) AIxIA 2022 - Advances in Artificial Intelligence. Lecture Notes in Computer Science(), vol. 13796, pp. 17–30. Springer International Publishing, Cham (2023). https://doi.org/10.1007/978-3-031-27181-6_2

    Chapter  Google Scholar 

  19. Mooney, C., Roddick, J.F.: Sequential pattern mining - approaches and algorithms. ACM Comput. Surv. 45(2), 1–39 (2013). https://doi.org/10.1145/2431211.2431218

    Article  MATH  Google Scholar 

  20. Negrevergne, B., Guns, T.: Constraint-based sequence mining using constraint programming. In: Michel, L. (ed.) CPAIOR 2015. LNCS, vol. 9075, pp. 288–305. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18008-3_20

    Chapter  MATH  Google Scholar 

  21. Samet, A., Guyet, T., Negrevergne, B.: Mining rare sequential patterns with ASP. In: ILP 2017–27th International Conference on Inductive Logic Programming (2017)

    Google Scholar 

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Acknowledgments

This work was partially supported by the project FAIR - Future AI Research (PE00000013), under the NRRP MUR program funded by the NextGenerationEU.

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Authors and Affiliations

  1. DIB & CILA, University of Bari Aldo Moro, Bari, Italy

    Francesca Alessandra Lisi

  2. DMMM, Polytechnic University of Bari, Bari, Italy

    Gioacchino Sterlicchio

  3. University of Applied Sciences in Geneva, Geneva, Switzerland

    David Billard

Authors
  1. Francesca Alessandra Lisi
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  2. Gioacchino Sterlicchio
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  3. David Billard
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Corresponding author

Correspondence to Francesca Alessandra Lisi .

Editor information

Editors and Affiliations

  1. Wageningen University and Research, Wageningen, The Netherlands

    Anna Fensel

  2. University of Oslo, Oslo, Norway

    Ana Ozaki

  3. SINTEF AS/Oslo Metropolitan University, Oslo, Norway

    Dumitru Roman

  4. Oslo Metropolitan University, Oslo, Norway

    Ahmet Soylu

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Lisi, F.A., Sterlicchio, G., Billard, D. (2023). A Case Study for Declarative Pattern Mining in Digital Forensics. In: Fensel, A., Ozaki, A., Roman, D., Soylu, A. (eds) Rules and Reasoning. RuleML+RR 2023. Lecture Notes in Computer Science, vol 14244. Springer, Cham. https://doi.org/10.1007/978-3-031-45072-3_12

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  • DOI: https://doi.org/10.1007/978-3-031-45072-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45071-6

  • Online ISBN: 978-3-031-45072-3

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