An Efficient Intrusion Detection Method Based on Federated Transfer Learning and an Extreme Learning Machine with Privacy Preservation. (11th October 2022)

• Record Type: Journal Article
• Authors: Wang, Kunpeng; Li, Jingmei; Wu, Weifei
• Other Names: Nazir Shah Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-10-11
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/2913293
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Current network security is becoming increasingly important, and intrusion detection is an effective method to protect the network from malicious attacks. This study proposes an intrusion detection algorithm FLTrELM based on federated transfer learning and an extreme learning machine to improve the effect of intrusion detection, which implements data aggregation through federated learning and facilitates the construction of personalized transfer learning for all organizations. FLTrELM first builds a transfer extreme learning machine model to solve the problem of insufficient samples and probability adaptation, then uses the model to learn to protect data privacy without sharing training data under the federated learning mechanism, and finally obtains an intrusion detection model. Experiments on the NSL-KDD, KDD99, and ISCX2012 datasets verify that the proposed method can achieve better detection results and robust performance, especially for small samples and new intrusions, and protects data privacy.