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Biologically inspired smart contract: A blockchain-based DDoS detection system

conference contribution
posted on 2020-11-04, 00:00 authored by Xu Han, Bai Zhang, Xingzi Liu, Frank JiangFrank Jiang
With the increase of Internet usage, the identification and recovery from cyber-attacks become the major concerns for cyber industries. Therefore, the harm caused by network attacks has caused widespread concern. Distributed Denial of Service (DDoS) attack is a very common destructive cyber attack. This is a network attack that destroys the network and can cause multiple computers to be attacked at the same time, failing to perform services properly. Therefore, based on the understanding of blockchain structure and DDoS characteristics, a blockchain-based DDoS detection model framework is proposed to form a blockchain-based collaborative detection system. We use the blockchain consortium chain structure to treat all participants as part of the private chain in the system. Each participating organization has its own channel, and other organizations cannot access its information, thus fully protecting the privacy of each participant. Our experimental results show that smart contracts can detect DDoS data and generate anomalous chains on each node. The time required to generate an exception chain and information sharing is very short, which indicates that the system can protect the privacy of user data. While sharing data in time, good results can be obtained as a collaborative detection system.

History

Event

ICNSC - Networking, Sensing and Control. IEEE International Conference (2020 : Nanjing, China (online)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Location

Nanjing, Chine (online)

Place of publication

Piscataway, N.J.

Start date

2020-10-30

End date

2020-11-02

ISBN-13

9781728168531

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2020, IEEE

Title of proceedings

ICNSC 2020 : Proceedings of IEEE International Conference on Networking, Sensing and Control

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