Efficient energy for one node and multi-nodes of wireless body area network

Wali, Sondous Sulaiman; Abdullah, Mohammed Najm

doi:10.11591/ijece.v12i1.pp914-923

Cited by 6 publications

(4 citation statements)

References 24 publications

(29 reference statements)

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“…Figure (1) demonstrates how WSN can be utilized for detecting intrusions. Sensor nodes (SN), cluster heads (CH), and sink nodes (sink) are all types of nodes in a such network [29,30]. To reduce communication burden and energy consumption, this network employs distributed intrusion detection.…”

Section: Wsn Intrusion Detection Architecturementioning

confidence: 99%

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Saleh,

Marouane,

Fakhfakh

2024

IEEE Access

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Communication in cyber-physical systems relies heavily on Wireless Sensor Networks (WSNs), which have numerous uses including ambient monitoring, object recognition, and data transmission. However, they are vulnerable to cyberattacks because they are connected to the IoT. In order to combat the difficulties associated with WSN intrusion detection, this research employs machine learning techniques, notably the Gaussian Nave Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms. The effectiveness of recommendation systems is improved with the introduction of context awareness. To lessen the burden on the computer, we first do a principal component analysis and singular value decomposition on the raw traffic data. The system was tested on two datasets, yielding extremely high accuracy results. This is evidence of the system's strength, even when the dataset is changed. On the WSN-DS dataset, the suggested SG-IDS model achieved a 96% accuracy rate, outperforming state-of-the-art algorithms with higher rates of 98% accuracy, 96% recall, and 97% F1-measurement. In an evaluation on an IoMT dataset, the SG-IDS performed admirably, with an accuracy of 0.87 and a precision of 1.00 in intrusion detection tasks.

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Section: Wsn Intrusion Detection Architecturementioning

confidence: 99%

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Saleh,

Marouane,

Fakhfakh

2024

IEEE Access

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“…The two algorithms proposed in the paper are Cuckoo Search and K-means, and the hybrid model intends to make the intrusion detection process more accurate and effective due to the benefits of both systems. [27]. Distributed intruder detection is a way of minimizing the communication overhead and ensuring efficiency in energy consumption.…”

Section: Figure 1: Wireless Sensor Network' Intrusion Detection Systemmentioning

confidence: 99%

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

Hassan,

Najim,

Al-Sharhanee

et al. 2024

Preprint

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Integrating machine learning (ML) into intrusion detection systems (IDS) is considered an important topic for preventing the spread of cyber threats. However, when it comes to machine learning techniques, IDSs face challenges in accurately identifying various types of attacks within the complex structures of a network. This study addresses the lack of research on combining metaheuristic optimization techniques with unsupervised machine learning algorithms in IDS design. The proposed model uses the cuckoo search metaheuristic and the K-means method to improve IDS precision. Here, the cuckoo search algorithm is used to increase the efficiency of feature selection. Meanwhile, the k-means clustering methodology is used to discretize the data and reduce its dimensionality by using two clusters, C1 and C2. The proposed model, developed carefully, includes data preprocessing (handling missing values), data transformation (label encoding), and data normalization. A stochastic operator assesses the impact of the K-means operator. The model is evaluated using an accessible intrusion dataset and compared with other state-of-the-art models. From the research conclusions, the presented model also demonstrates better results compared to the rest, especially when it reaches accuracy (99. 79%), precision (99. 78%), recall (99. 51%), and the F1-score (99).

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“…The unique characteristics of WSNs necessitate specialized intrusion detection approaches. Traditional methods used in computer networks are ill-suited to safeguard WSNs due to disparities in terminal types, data transmission, network topology, and other factors [14]. An effective WSN IDS must possess high detection efficacy for both known and unknown threats while imposing minimal impact on the WSN infrastructure [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

Alhasan,

Hamza

2023

RIA

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Wireless sensor networks (WSNs) play a critical role in cyber-physical systems, enabling communication between autonomous sensors. When integrated with the Internet of Things (IoT), WSNs unfortunately become vulnerable to various attacks, such as blackhole, grayhole, flooding, and scheduling, thereby posing significant security threats. Existing methods for intrusion detection in WSNs often suffer from low detection rates, significant computational overhead, and false alarms, primarily due to resource constraints and data correlations. This study introduces IDS-CNN, a novel intrusion detection method leveraging Convolutional Neural Networks (CNNs). The proposed IDS-CNN model, designed to optimize efficiency and reduce processing time, comprises nine convolutional neural network layers and six Max-Pooling1D layers. To alleviate computational demands, dimensionality reduction techniques, specifically Principal Component Analysis and Singular Value Decomposition, are applied to raw traffic data. The IDS-CNN model is then employed to classify and categorize network threats. Experimental evaluations suggest that the IDS-CNN approach yields a high accuracy rate of 99% compared to existing methods, based on tests performed on two datasets, WSN-DS and UNSW-NB15. Notably, with the UNSW-NB15 dataset, accuracy rates were further improved to 99.99% and 100%. By leveraging deep learning techniques to enhance intrusion detection in WSNs, this study presents a significant contribution to the field. The IDS-CNN model advances our understanding of WSN security by exceeding the accuracy rates of prior models. As it addresses the limitations of existing methods, the implications of this research are substantial, offering a more reliable and efficient solution for WSN intrusion detection. The findings underscore the potential of IDS-CNN in safeguarding WSNs and IoT systems from sophisticated and evolving cyber threats.

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Efficient energy for one node and multi-nodes of wireless body area network

Cited by 6 publications

References 24 publications

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

Stochastic Gradient Descent Intrusions Detection for Wireless Sensor Network Attack Detection System Using Machine Learning

A Hybrid Cuckoo Search-K-means Model for Enhanced Intrusion Detection in Internet of Things

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

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