A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Pan, Jeng-Shyang; Fang, Fan; Chu, Shu-Chuan; Zhao, Huiqi; Liu, Gaoyuan

doi:10.1155/2021/5540895

Cited by 25 publications

(7 citation statements)

References 54 publications

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“…Hence, the IDS is one of the most suitable option for WSN security, which supports to spot the intrusions or unauthenticated activities in the network by analyzing the features of network and data. Pan, et al [24] deployed a lightweight and intelligent intrusion detection model for guaranteeing the privacy, security, and con dentiality of WSN. Here, the K-Nearest Neighbor (KNN) algorithm incorporated with the Sine Cosine Algorithm (SCA) was utilized to minimize the false alarm rate and increase the classi cation accuracy of this detection framework.…”

Section: Related Workmentioning

confidence: 99%

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

Nabi

2023

Preprint

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With the global adoption of Internet services, service providers are having a difficult time securing their systems, especially against new attacks and intrusions. Various anomalous detection approaches have been developed for protecting WSN from cyber-attacks. However, those systems suffer from the major issues of a high number of false alarms, increased over-fitting, and complexity. Therefore, this paper motivates to develop a novel and intelligent IDS framework for protecting WSN from cyber-attacks. For this purpose, an Intensive Binary Pigeon Optimization (IBiPO) and Bi-directional Long Short Term Memory (Bi-LSTM) mechanisms are developed for accurate intrusion detection and classification.

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Section: Related Workmentioning

confidence: 99%

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

Nabi

2023

Preprint

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“…Pan et al [28] also proposed a lightweight intelligent IDS model for wireless sensor networks. The authors used KNN and the sine cosine algorithm to design their model.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

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Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

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“…A parameter is required to measure the lightness of the proposed model. Pan et al [28] also suggested a lightweight intelligent intrusion detection system (IDS) architecture for wireless sensor networks. To create their model, the authors used KNN and the sine cosine technique.…”

Section: Related Workmentioning

confidence: 99%

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

View full text Add to dashboard Cite

Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

show abstract

A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Cited by 25 publications

References 54 publications

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

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A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Cited by 25 publications

References 54 publications

Cyber-Attacks in WSN &amp; Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) &amp; Bi-LSTM-based IDS Framework

Cyber-Attacks in WSN &amp; Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) &amp; Bi-LSTM-based IDS Framework

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Contact Info

Product

Resources

About

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework

Cyber-Attacks in WSN & Security Optimization By a Novel Technique based Intensive Binary Pigeon Optimization (IBiPO) & Bi-LSTM-based IDS Framework