Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks

Document Type

Article

Publication Date

1-1-2022

Publication Title

Intelligent Systems Reference Library

Volume

217

First Page

169

Last Page

183

Abstract

In recent years, Generative Adversarial Networks (GAN) have become powerful industrial tools to facilitate various learning tasks, including anomaly detection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for efficient estimation of a confidence measure during the operation that reflects how well samples can be assigned to benign data. Furthermore, this chapter designs an intrusion detection system by developing a GAN-based anomaly detector. To do this, we study the effect of the proposed approach and the selected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task.

DOI

10.1007/978-3-030-91390-8_8

ISSN

18684394

E-ISSN

18684408

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