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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, i...

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Main Authors: Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Khalaf, Bashar Ahmad, Mostafa, Salama A.
Format: Article
Language:English
Published: De Gruyter 2023
Subjects:
T Technology (General)
T173.2-174.5 Technological change
Online Access:http://eprints.uthm.edu.my/8775/1/J15750_9d9880322873c2021c6ffa0622006ab4.pdf
http://eprints.uthm.edu.my/8775/
https://doi.org/10.1515/jisys-2022-0155
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spelling my.uthm.eprints.87752023-06-12T07:05:46Z http://eprints.uthm.edu.my/8775/ Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks Mohammed Aswad, Firas Ahmed, Ali Mohammed Saleh Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi Khalaf, Bashar Ahmad Mostafa, Salama A. T Technology (General) T173.2-174.5 Technological change With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. De Gruyter 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/8775/1/J15750_9d9880322873c2021c6ffa0622006ab4.pdf Mohammed Aswad, Firas and Ahmed, Ali Mohammed Saleh and Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi and Khalaf, Bashar Ahmad and Mostafa, Salama A. (2023) Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks. Journal of Intelligent Systems. pp. 1-13. ISSN 20220155 https://doi.org/10.1515/jisys-2022-0155
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
T173.2-174.5 Technological change
spellingShingle T Technology (General)
T173.2-174.5 Technological change
Mohammed Aswad, Firas
Ahmed, Ali Mohammed Saleh
Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi
Khalaf, Bashar Ahmad
Mostafa, Salama A.
Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
description With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.
format Article
author Mohammed Aswad, Firas
Ahmed, Ali Mohammed Saleh
Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi
Khalaf, Bashar Ahmad
Mostafa, Salama A.
author_facet Mohammed Aswad, Firas
Ahmed, Ali Mohammed Saleh
Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi
Khalaf, Bashar Ahmad
Mostafa, Salama A.
author_sort Mohammed Aswad, Firas
title Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
title_short Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
title_full Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
title_fullStr Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
title_full_unstemmed Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks
title_sort deep learning in distributed denial-ofservice attacks detection method for internet of things networks
publisher De Gruyter
publishDate 2023
url http://eprints.uthm.edu.my/8775/1/J15750_9d9880322873c2021c6ffa0622006ab4.pdf
http://eprints.uthm.edu.my/8775/
https://doi.org/10.1515/jisys-2022-0155
_version_ 1769845092820975616
score 13.211869

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