Hybrid intrusion detection system for wireless IoT networks using deep learning algorithm

Simon, Judy; Kapileswar, N.; Polasi, Phani Kumar; Elaveini, M. Aarthi

doi:10.1016/j.compeleceng.2022.108190

Cited by 31 publications

(7 citation statements)

References 22 publications

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“…Therefore, it can be stated that, when compared to other systems, the suggested IDS, PSO + ANN provides the best accuracy and the lowest FPR (A survey on firefly algorithms et al 2022; Judy Simon et al 2022).…”

Section: Resultsmentioning

confidence: 99%

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Sivagaminathan,

Sharma,

Henge

2023

Cybersecurity

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Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study.

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Section: Resultsmentioning

confidence: 99%

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Sivagaminathan,

Sharma,

Henge

2023

Cybersecurity

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“…Simon et al [ 49 ] used the stacked contraction self-encoder (SCAE) technique to extract features from network traffic in unsupervised mode and applied an SVM as a classifier to achieve efficient and high-performance intrusion detection. Meanwhile, Wang et al [ 50 ] utilized CNN for feature selection, replaced the fully connected neural network with a decision tree(DT) and used the DT as a classifier for deep features for intrusion system classification and attack detection. Satisfactory experimental results were achieved on the NSL-KDD dataset.…”

Section: Related Workmentioning

confidence: 99%

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo,

Zhang,

et al. 2024

Heliyon

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“…It is mainly based on distribution features. The conditional probability measures are shown in Equation (26) and Equation (27):…”

Section: 1mentioning

confidence: 99%

“…But it requires a large dataset to process and train the network in IoT. The decision tree algorithm 26 is very useful for selecting the essential features from the convolution layer. It includes a minimal requirement for data preparation and robust performance on a large dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning

Shali,

Chinnasamy,

Selvakumari

2024

Trans Emerging Tel Tech

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Background of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic‐assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one‐dimensional convolutional neural network (1D‐CNN), where the features are extracted from the target‐based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency.

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Hybrid intrusion detection system for wireless IoT networks using deep learning algorithm

Cited by 31 publications

References 22 publications

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning

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