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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Two years after WannaCry, a million computers remain at risk. https://techcrunch.com/2019/05/12/wannacry-two-years-on/. Accessed 17 May 2019
Sultan, H., et al.: A survey on ransomware: evolution, growth, and impact. Int. J. Adv. Res. Comput. Sci. 9(2) (2018)
Barrera, D., Molloy, I., Huang, H.: IDIoT: Securing the Internet of Things like it’s 1994. arXiv preprint arXiv:1712.03623 (2017)
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
Bazrafshan, Z., et al.: A survey on heuristic malware detection techniques. In: Information and Knowledge Technology (IKT) 2013 5th Conference, pp. 113–120 (2013)
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)
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)
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)
Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)
Wüst, K., Gervais, A.: Do you need a Blockchain? In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 45–54. IEEE (2018)
Gu, J., et al.: Consortium blockchain-based malware detection in mobile devices. IEEE Access 6, 12118–12128 (2018)
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)
Ethereum Project. https://www.ethereum.org/. Accessed 02 Dec 2018
Hyperledger - Open Source Blockchain Technologies. https://www.hyperledger.org/. Accessed 02 Dec 2018
uPort.me. https://www.uport.me/. Accessed 02 Dec 2018
Fan, Y., Ye, Y., Chen, L.: Malicious sequential pattern mining for automatic malware detection. Expert Syst. Appl. 52, 16–25 (2016)
Acknowledgements
This work was supported by the Japan Society for the Promotion of Science, KAKENHI Grant Numbers JP17H01736, JP17K00139, and JP18K11268.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fuji, R. et al. (2020). Blockchain-Based Malware Detection Method Using Shared Signatures of Suspected Malware Files. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-29029-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29028-3
Online ISBN: 978-3-030-29029-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)