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Experimenting and assessing machine learning tools for detecting and analyzing malicious behaviors in complex environments

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Abstract

This paper proposes applying and experimentally assessing machine learning tools to solve security issues in complex environments, specifically identifying and analyzing malicious behaviors. To evaluate the effectiveness of machine learning algorithms to detect anomalies, we consider the following three real-world case studies: (i) detecting and analyzing Tor traffic, on the basis of a machine learning-based discrimination technique; (ii) identifying and analyzing CAN bus attacks via deep learning; (iii) detecting and analyzing mobile malware, with particular regard to ransomware in Android environments, by means of structural entropy-based classification. Derived observations confirm the effectiveness of machine learning in supporting security of complex environments.

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Notes

  1. http://archive.wired.com/politics/security/news/2005/05/67542?currentPage=all.

  2. http://www.csoonline.com/article/2131589/investigations-forensics/how-online-black-markets-work.html.

  3. http://www.cs.waikato.ac.nz/ml/weka/.

  4. http://www.unb.ca/cic/research/datasets/tor.html.

  5. www.can.bosch.com.

  6. https://sites.google.com/a/hksecurity.net/ocslab/Datasets/car-hacking-dataset.

  7. http://www.cs.waikato.ac.nz/ml/weka/.

  8. https://github.com/Waikato/wekaDeeplearning4j.

  9. https://goo.gl/ht8Rqd.

  10. https://goo.gl/C94swo.

  11. https://goo.gl/t7UbkU.

  12. https://goo.gl/iRJYfH.

  13. http://www.cs.waikato.ac.nz/ml/weka/.

  14. http://users.aber.ac.uk/rkj/book/wekafull.jar.

  15. https://developers.virustotal.com/reference.

  16. https://www.virustotal.com/.

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Acknowledgements

This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII.

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Authors and Affiliations

  1. DIA Department, University of Trieste and ICAR-CNR, Trieste, Italy

    Alfredo Cuzzocrea

  2. ICAR-CNR, Rende, Italy

    Alfredo Cuzzocrea

  3. IIT Institute, National Research Council, Pisa, Italy

    Fabio Martinelli & Francesco Mercaldo

  4. COSPECS Department, University of Messina, Messina, Italy

    Giorgio Mario Grasso

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  1. Alfredo Cuzzocrea
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  2. Fabio Martinelli
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  3. Francesco Mercaldo
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  4. Giorgio Mario Grasso
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Correspondence to Alfredo Cuzzocrea.

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Cuzzocrea, A., Martinelli, F., Mercaldo, F. et al. Experimenting and assessing machine learning tools for detecting and analyzing malicious behaviors in complex environments. J Reliable Intell Environ 4, 225–245 (2018). https://doi.org/10.1007/s40860-018-0072-3

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  • DOI: https://doi.org/10.1007/s40860-018-0072-3

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