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Blockchain-Based Malware Detection Method Using Shared Signatures of Suspected Malware Files

  • Ryusei Fuji
  • Shotaro Usuzaki
  • Kentaro AburadaEmail author
  • Hisaaki Yamaba
  • Tetsuro Katayama
  • Mirang Park
  • Norio Shiratori
  • Naonobu Okazaki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

Although rapid malware detection is very important, the detection is difficult due to the increase of new malware. In recent years, blockchain technology has attracted the attention of many people due to its four main characteristics of decentralization, persistency, anonymity, and auditability. In this paper, we propose a blockchain-based malware detection method that uses shared signatures of suspected malware files. The proposed method can share the signatures of suspected files between users, allowing them to rapidly respond to increasing malware threats. Further, it can improve the malware detection by utilizing signatures on the blockchain. In the evaluation experiment, we perform a more real simulation compared with our previous work to evaluate the detection accuracy. Compared with heuristic methods or behavior-based methods only, the proposed system which uses these methods plus signature-based method using shared signatures on the blockchain improved the false negative rate and the false positive rate.

Notes

Acknowledgements

This work was supported by the Japan Society for the Promotion of Science, KAKENHI Grant Numbers JP17H01736, JP17K00139, and JP18K11268.

References

  1. 1.
    Two years after WannaCry, a million computers remain at risk. https://techcrunch.com/2019/05/12/wannacry-two-years-on/. Accessed 17 May 2019
  2. 2.
    Sultan, H., et al.: A survey on ransomware: evolution, growth, and impact. Int. J. Adv. Res. Comput. Sci. 9(2) (2018)Google Scholar
  3. 3.
    Barrera, D., Molloy, I., Huang, H.: IDIoT: Securing the Internet of Things like it’s 1994. arXiv preprint arXiv:1712.03623 (2017)
  4. 4.
    AV-Test “Security report 2017/18”. https://www.av-test.org/fileadmin/pdf/security_report/AV-TEST_Security_Report_2017-2018.pdf. Accessed 02 Dec 2018
  5. 5.
    Bazrafshan, Z., et al.: A survey on heuristic malware detection techniques. In: Information and Knowledge Technology (IKT) 2013 5th Conference, pp. 113–120 (2013)Google Scholar
  6. 6.
    Hashimoto, R., Yoshioka, K., Matsumoto, T.: Evaluation of anti-virus software based on the correspondence to non-detected malware. Distributed Processing System (DPS), pp. 1–8 (2012). (in Japanese)Google Scholar
  7. 7.
    Fuji, R., et al.: Investigation on sharing signatures of suspected malware files using blockchain technology. In: International Multi Conference of Engineers and Computer Scientists (IMECS), pp. 94–99 (2019)Google Scholar
  8. 8.
    Zheng, Z., et al.: An overview of blockchain technology: architecture, consensus, and future trends. In: IEEE 6th International Congress on Big Data, pp. 557–564 (2017)Google Scholar
  9. 9.
    Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)Google Scholar
  10. 10.
    Wüst, K., Gervais, A.: Do you need a Blockchain? In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 45–54. IEEE (2018)Google Scholar
  11. 11.
    Gu, J., et al.: Consortium blockchain-based malware detection in mobile devices. IEEE Access 6, 12118–12128 (2018) CrossRefGoogle Scholar
  12. 12.
    Graf, R., King, R.: Neural network and blockchain based technique for cyber threat intelligence and situational awareness. In: 2018 10th International Conference on Cyber Conflict (CyCon). IEEE (2018)Google Scholar
  13. 13.
    Ethereum Project. https://www.ethereum.org/. Accessed 02 Dec 2018
  14. 14.
    Hyperledger - Open Source Blockchain Technologies. https://www.hyperledger.org/. Accessed 02 Dec 2018
  15. 15.
    uPort.me. https://www.uport.me/. Accessed 02 Dec 2018
  16. 16.
    Fan, Y., Ye, Y., Chen, L.: Malicious sequential pattern mining for automatic malware detection. Expert Syst. Appl. 52, 16–25 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ryusei Fuji
    • 1
  • Shotaro Usuzaki
    • 1
  • Kentaro Aburada
    • 1
    Email author
  • Hisaaki Yamaba
    • 1
  • Tetsuro Katayama
    • 1
  • Mirang Park
    • 2
  • Norio Shiratori
    • 3
  • Naonobu Okazaki
    • 1
  1. 1.Department of Computer Science and Systems EngineeringUniversity of MiyazakiMiyazakiJapan
  2. 2.Kanagawa Institute of TechnologyAtsugiJapan
  3. 3.Chuo UniversityTokyoJapan

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