Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks Article Swipe
Firas Mohammed Aswad
,
Ali Mohammed Saleh Ahmed
,
Nafea Ali Majeed Alhammadi
,
Bashar Ahmad Khalaf
,
Salama A. Mostafa
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1515/jisys-2022-0155
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1515/jisys-2022-0155
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%.
Related Topics
Concepts
Denial-of-service attack
Computer science
Intrusion detection system
Trinoo
Deep learning
Internet of Things
Computer security
Recurrent neural network
Artificial intelligence
Convolutional neural network
Application layer DDoS attack
Machine learning
Computer network
The Internet
Distributed computing
Artificial neural network
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/jisys-2022-0155
- https://www.degruyter.com/document/doi/10.1515/jisys-2022-0155/pdf
- OA Status
- gold
- Cited By
- 37
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4315705084
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4315705084Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1515/jisys-2022-0155Digital Object Identifier
- Title
-
Deep learning in distributed denial-of-service attacks detection method for Internet of Things networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Salama A. MostafaList of authors in order
- Landing page
-
https://doi.org/10.1515/jisys-2022-0155Publisher landing page
- PDF URL
-
https://www.degruyter.com/document/doi/10.1515/jisys-2022-0155/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.degruyter.com/document/doi/10.1515/jisys-2022-0155/pdfDirect OA link when available
- Concepts
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Denial-of-service attack, Computer science, Intrusion detection system, Trinoo, Deep learning, Internet of Things, Computer security, Recurrent neural network, Artificial intelligence, Convolutional neural network, Application layer DDoS attack, Machine learning, Computer network, The Internet, Distributed computing, Artificial neural network, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
37Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11, 2024: 17, 2023: 9Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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