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Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network

The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data ove...

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Bibliographic Details
Main Author: Zeeshan, Ahmad
Format: Thesis
Language:English
English
Published: UNIMAS 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf
http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf
http://ir.unimas.my/id/eprint/41363/
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spelling my.unimas.ir.413632024-07-23T05:08:33Z http://ir.unimas.my/id/eprint/41363/ Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network Zeeshan, Ahmad QA75 Electronic computers. Computer science The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this thesis proposes an efficient novel Multistage Spectrogram image-based network anomaly detection system using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%. UNIMAS 2023-02-22 Thesis NonPeerReviewed text en http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf text en http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf Zeeshan, Ahmad (2023) Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network. PhD thesis, Faculty of Computer Science and Information Technology.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zeeshan, Ahmad
Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
description The revolutionary idea of the internet-of-things (IoT) architecture has gained enormous recognition over the last decade, resulting in exponential growth in the networks, connected devices, and the data processed therein. Since IoT devices generate and exchange a massive amount of sensitive data over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this thesis proposes an efficient novel Multistage Spectrogram image-based network anomaly detection system using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%.
format Thesis
author Zeeshan, Ahmad
author_facet Zeeshan, Ahmad
author_sort Zeeshan, Ahmad
title Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_short Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_full Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_fullStr Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_full_unstemmed Spectrogram based Anomaly Detection Scheme for Internet-of-Things using Deep Convolutional Neural Network
title_sort spectrogram based anomaly detection scheme for internet-of-things using deep convolutional neural network
publisher UNIMAS
publishDate 2023
url http://ir.unimas.my/id/eprint/41363/5/Thesis%20Form_Zeeshan%20Ahmad.pdf
http://ir.unimas.my/id/eprint/41363/6/Zeeshan%20Ahmad%20ft.pdf
http://ir.unimas.my/id/eprint/41363/
_version_ 1806456038200705024
score 13.211869

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