A Federated-ANFIS for Collaborative Intrusion Detection in Securing Decentralized Autonomous Organizations

被引：3
作者：
Tsang Y.P. [1 ]
Wu C.H. [2 ]
Dong N. [3 ]
机构：
[1] The Hong Kong Polytechnic University, Department of Industrial and Systems Engineering, Hong Kong SAR
[2] The Hang Seng University of Hong Kong, Department of Supply Chain and Information Management, Hong Kong SAR
[3] Tianjin University, School of Electrical and Information Engineering, Tianjin
来源：
IEEE Transactions on Engineering Management | 2024年 / 71卷
关键词：
Adaptive neuro-fuzzy inference system (ANFIS); blockchain; collaborative intrusion detection; decentralized autonomous organizations (DAOs); federated learning;
D O I：
10.1109/TEM.2023.3304409
中图分类号：
学科分类号：
摘要：
Blockchain has facilitated the emergence of automation and decentralization concepts, leading to significant organizational and operational changes in businesses, e.g., decentralized autonomous organizations (DAOs). In DAOs, management decisions are made collectively and automatically through smart contracts without a central authority, which results in increased cybersecurity requirements. While blockchain integration aims to eliminate single points of failure and enhance data integrity, DAOs remain susceptible to vulnerabilities in consensus mechanisms, key management, and software management, highlighting the need for intrusion detection. Collaborative intrusion detection has been identified as a potential solution to address emerging cyberattacks in a decentralized environment; however, it is not yet fully developed. This study proposes a federated adaptive neuro-fuzzy inference system (FANFIS) for collaborative intrusion detection in blockchain-Internet-of-Things (IoT) networks. The FANFIS maintains a global intrusion detection model in a privacy-preserving manner over the network. Through computational experiments with datasets of KDDCUP99 and Bot-IoT, we found that using the FANFIS reduced the computational time for model training by an average of 49.42% while maintaining a high-performance level. The superior performance of the FANFIS, as demonstrated by its accuracy, precision, and F1-score, surpasses the conventional method involving data centralization, exhibiting mean percentage errors of 1.4092%, 2.6935%, and 1.3463%, respectively. © 1988-2012 IEEE.
引用
收藏
页码：12529 / 12541
页数：12
相关论文
共 41 条
  • [1] Al-Jaroodi J., Mohamed N., Blockchain in industries:Asurvey, IEEE Access, 7, pp. 36500-36515, (2019)
  • [2] Kraus D., Obrist T., Hari O., Blockchains, Smart Contracts, Decentralised Autonomous Organisations and the Law, (2019)
  • [3] Wang S., Ding W., Li J., Yuan Y., Ouyang L., Wang F.-Y., Decentralised autonomous organisations: Concept, model, and applications, IEEE Trans. Comput. Social Syst., 6, 5, pp. 870-878, (2019)
  • [4] Liu S., Wang C., Zhou Y., Analysis of financial data risk and network information security by blockchain technology and edge computing, IEEE Trans. Eng. Manage.
  • [5] Masdari M., Khezri H., A survey and taxonomy of the fuzzy signaturebased intrusion detection systems, Appl. Soft Comput., 92, (2020)
  • [6] Elhefnawy R., Abounaser H., Badr A., A hybrid nested genetic-fuzzy algorithm framework for intrusion detection and attacks, IEEE Access, 8, pp. 98218-98233, (2020)
  • [7] Dua D., Graff C., UCI machine learning repository, (2022)
  • [8] Koroniotis N., Moustafa N., Sitnikova E., A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework, Future Gener. Comput. Syst., 110, pp. 91-106, (2020)
  • [9] Novo O., Blockchain meets IoT: An architecture for scalable access management in IoT, IEEE Internet Things J., 5, 2, pp. 1184-1195, (2018)
  • [10] Meng W., Li W., Zhu L., Enhancing medical smartphone networks via blockchain-based trust management against insider attacks, IEEE Trans. Eng. Manage., 67, 4, pp. 1377-1386, (2020)
12345