A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

Gautami, A.; Shanthini, J.; Karthik, S.

doi:10.32604/csse.2023.026688

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Sivanantham et al [32] proposed a new network security IDS framework by using the modified frequent patterns through the K-means algorithm. Gautami et al [33] divided WSN IDS into data collection (DG) and intrusion detection (ID) stages. In the DG stage, sensor nodes form clusters in the WSN, and then the cluster heads are selected by a distance-based Drosophila fuzzy algorithm.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user's privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

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Section: Related Workmentioning

confidence: 99%

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The results are obtained using a hybrid model with a Random Forest (RF) and achieve 94.86% accuracy [50] 2023…”

Section: Paper Year Main Idea Conclusionmentioning

confidence: 99%

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Mardenov,

Adamova,

Zhukabayeva

et al. 2023

Journal of Robotics and Control

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The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks

Goyal,

Patil,

Bhagat

2024

2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet O

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A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network

Cited by 6 publications

References 28 publications

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study

Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks

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