NDLI: Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

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Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

Content Provider	Tech Science Press
Author	Jin, Zilong Ran, Guo Sun, Zeyong
Abstract	Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems. IDS consists of two modules, namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module. The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers, but the accuracy does not meet the requirements. The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking. The active learning query module improves the detection of known attacks through the committee algorithm, and the improved KNN algorithm can better help detect unknown attacks. We have tested the latest industrial IoT dataset with satisfactory results.
Related Links	https://www.techscience.com/jiot/v4n2/51966
Starting Page	99
Ending Page	111
ISSN	25790099
DOI	10.32604/jiot.2022.037416
Issue Number	2
Journal	Journal on Internet of Things (JIOT)
Volume Number	4
e-ISSN	25790080
Language	English
Publisher Date	2023-03-28
Access Restriction	Open
Subject Keyword	stacking ensemble learning unknown attacks Intrusion detection active incremental learning IDS
Content Type	Text
Resource Type	Article
Subject	Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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