Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks

Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional...

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Bibliographic Details
Published in:Information (Basel) 2023-01, Vol.14 (1), p.41
Main Authors: Chaganti, Rajasekhar, Suliman, Wael, Ravi, Vinayakumar, Dua, Amit
Format: Article
Language:English
Subjects:
Algorithms
Alliances
Automation
Computer architecture
Cybersecurity
Data analysis
Datasets
Deep learning
Denial of service attacks
Experiments
Internet
Internet of Things
intrusion detection
Intrusion detection systems
Literature reviews
LSTM
Machine learning
Malware
Networks
Performance evaluation
software defined networks
Software-defined networking
support vector machine
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Summary:Owing to the prevalence of the Internet of things (IoT) devices connected to the Internet, the number of IoT-based attacks has been growing yearly. The existing solutions may not effectively mitigate IoT attacks. In particular, the advanced network-based attack detection solutions using traditional Intrusion detection systems are challenging when the network environment supports traditional as well as IoT protocols and uses a centralized network architecture such as a software defined network (SDN). In this paper, we propose a long short-term memory (LSTM) based approach to detect network attacks using SDN supported intrusion detection system in IoT networks. We present an extensive performance evaluation of the machine learning (ML) and deep learning (DL) model in two SDNIoT-focused datasets. We also propose an LSTM-based architecture for the effective multiclass classification of network attacks in IoT networks. Our evaluation of the proposed model shows that our model effectively identifies the attacks and classifies the attack types with an accuracy of 0.971. In addition, various visualization methods are shown to understand the dataset’s characteristics and visualize the embedding features.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14010041