RETRACTED ARTICLE: Performance Analysis of Malicious and Link Failure Detection System Using Deep Learning Methodology

Kalaiselvi, K.; Vanitha, L.; Thilak, K. Deepa; Kumar, Tarun; Saranya, S.; Kumaresan, K.

doi:10.1007/s11277-021-08790-9

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Alongside Table 8, Figure 20 shows the complete comparison of the presented results with similar findings from the literature discussed and enlisted in the present scenario. The work presented in Table 8 and Figure 20 shows that our proposed approach works better compared to other techniques presented in [40][41][42][43]54].…”

Section: Comparison Of Results With Existing Techniquesmentioning

confidence: 82%

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Rashid

Saeed

Ali

et al. 2023

Sensors

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Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network’s performance has improved because training and testing time do not increase when we include more nodes in the network.

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Section: Comparison Of Results With Existing Techniquesmentioning

confidence: 82%

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

Rashid

Saeed

Ali

et al. 2023

Sensors

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“…The development of WBAN networks has enabled the human body to detect, monitor, and control numerous vital signs, such as temperature and humidity. The WBAN comprises many sensor nodes, each serving a particular function [1]. The majority of WBAN deployments take place via various use cases.…”

Section: Introductionmentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use.

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“…WBAN is a type of network created specifically for the human body and its needs, such as the ability to detect, monitor, control, and transmit various vital signs such as temperature, humidity, etc. There are a lot of network nodes in a WBAN, and they all do different things [1,2]. Due to the several fields of engineering that have made strides in recent years, conventional medical methods can be bolstered by cutting-edge technology solutions.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem

et al. 2023

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Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classification approach, hence strengthening the safety of such networks. Feature extraction process is done by using Linear Regression-Based Principal Component Analysis (LR-PCA). The test results demonstrated that the proposed IGO-ANN method attains the greatest performance in terms of accuracy, end to end delay and packet delivery ratio regarding trusted WBAN nodes classification than certain existing methods.

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RETRACTED ARTICLE: Performance Analysis of Malicious and Link Failure Detection System Using Deep Learning Methodology

Cited by 6 publications

References 27 publications

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)

A Deep learning approach for trust-untrust nodes classification problem in WBAN

An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem

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