Intrusion Detection and Prevention Using RNN in WSN

Yadav, Ashok; Kumar, Arun

doi:10.1007/978-981-16-6723-7_40

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…In the suggested technique, the IDS agent is chosen based on internal node congestion and given a matrix. The authors in [28] implemented a smart security architecture employing random neural networks to develop an intrusion detection system. The suggested security solution is applied for an existing WSN system to evaluate its viability, and its functioning is nearly proven by successfully identifying any suspicious sensor nodes and unusual behavior in the base station with high accuracy and little overhead.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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“…Studies were carried out on the WSN-DS dataset. A system has been proposed by Yadak and Kumar [25] to detect and prevent distributed denial-ofservice attacks in wireless sensor networks. Recurrent neural network is used as a classifier in the proposed model.…”

Section: Related Workmentioning

confidence: 99%

STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment

Dener

Orman

2022

IEEE Access

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Wireless Sensor Networks(WSNs) are vulnerable to a variety of unique security risks and threats in their data collection and transmission processes. One of the most common attacks on WSNs that can target all layers of the protocol stack is the DoS attack. In this study, a unique DoS Intrusion Detection System (DDS) is proposed to detect DoS attacks specific to WSNs. The proposed system is an ensemble intrusion detection system called STLGBM-DDS, which is developed on Apache Spark big data platform in Google Colab environment, combining LightGBM machine learning algorithm, data balancing and feature selection processes. In order to reduce the effects of data imbalance on system performance, data imbalance processing consisting of Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links sampling methods called STL was used. In addition, Information Gain Ratio was used as a feature selection technique in the data preprocessing stage. The effects of both data balancing and feature selection stages on the detection performance of the system were investigated. The results obtained were evaluated using the Accuracy, F-Measure, Precision, Recall, ROC Curve and Precision-Recall Curve parameters. As a result, the proposed method achieved an overall accuracy of 99.95%. Also, it achieved 99.99%, 99.96%, 99.98%, 99.92%, 99.87% accuracy performance according to Normal, Grayhole, Blackhole, TDMA and Flooding classes, respectively. According to the results obtained, the proposed method has achieved very successful results in DoS attack detection in WSNs compared to current methods.

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

Anusha,

Tapas Bapu,

et al. 2024

Multimed Tools Appl

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Intrusion Detection and Prevention Using RNN in WSN

Cited by 8 publications

References 21 publications

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment

Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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