Intrusion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection

Mourabit, Yousef El; Toumanari, Ahmed; Bouirden, Anouar; Moussaid, Nadya El

doi:10.14569/ijacsa.2015.060922

Cited by 18 publications

(1 citation statement)

References 18 publications

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“…Essentially, RF relied on this technique since it is straightforward, but also it can be used for both regression and classification. For the majority of the period, RF gives excellent results with or without extreme parameter adjustment [21]. Among learning algorithms, Random Forest is one.…”

Section: Feature Extraction Using Random Forest Algorithmmentioning

confidence: 99%

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Jayadhas

Roslin

Wilfred

2023

Preprint

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Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random forest. In order to accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.

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Section: Feature Extraction Using Random Forest Algorithmmentioning

confidence: 99%

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Jayadhas

Roslin

Wilfred

2023

Preprint

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Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Selvi

Shen

2022

Concurrency and Computation

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Wireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding-long short-term memory (GT-LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT-LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT-LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT-LSTM performs better than fuzzy, KNN, and linear regression-based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

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Improving Security of IoT Networks Using Machine Learning-Based Intrusion Detection System

Ambarkar

Shekokar

2020

Algorithms for Intelligent Systems

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Intrusion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection

Cited by 18 publications

References 18 publications

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Deep Learning model-based malicious node detection system in wireless multimedia sensor Network

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Improving Security of IoT Networks Using Machine Learning-Based Intrusion Detection System

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