Anomaly detection for fault detection in wireless community networks using machine learning

Cerdà-Alabern, Llorenç; Iuhasz, Gabriel; Gemmi, Gabriele

doi:10.1016/j.comcom.2023.02.019

Cited by 17 publications

(6 citation statements)

References 35 publications

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“…Assuming that the WOA population is N and the GA population is G, the new population is a combined (N + G) population, leading to a population boom after some iterations. To avoid a population explosion, which leads to high computational costs, a conditional choice strategy, inspired by the Altruism strategy [44], is proposed to sacrifice individuals in the new population with the number of sacrificed whales equal to G. Therefore, the similarity index (SI), which is described in Equation (15), is used to calculate the similarity score of each whale with their counterparts in the population.…”

Section: Proposed Genetic Sacrificial Whale Optimizationmentioning

confidence: 99%

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Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Nguyen,

Vo,

Yoo

2024

Sensors

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Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost’s hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively.

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Section: Proposed Genetic Sacrificial Whale Optimizationmentioning

confidence: 99%

“…Figure 6 illustrates that the second feature subset, which has the lowest fitness value, is chosen as the output of GSWO for FS. In summary, the most useful feature subset, represented by the second whale, consists of features of order [0, 5,6,8,9,13,14,15,17].…”

Section: Applying Gswo For Feature Selectionmentioning

confidence: 99%

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Nguyen,

Vo,

Yoo

2024

Sensors

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“…Indeed, a research of the performance of 4 unsupervised ML approaches based on different principles using this dataset has been carried out in Cerdà-Alabern et al. [3] . In addition, the dataset presented in this paper extends the dataset used in Cerdà-Alabern et al.…”

Section: Objectivementioning

confidence: 99%

“…In addition, the dataset presented in this paper extends the dataset used in Cerdà-Alabern et al. [3] by adding network topology information, including routing protocol metrics, routing tables and adjacency matrices. We believe that this data can be useful to study a wider set of ML methods, e.g.…”

Section: Objectivementioning

confidence: 99%

Dataset for anomaly detection in a production wireless mesh community network

Cerdà-Alabern

Iuhasz

2023

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“…− Anomaly detection. These methods use unsupervised learning techniques to detect outliers or anomalies in the sensor data that indicate faults [61].…”

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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Anomaly detection for fault detection in wireless community networks using machine learning

Cited by 17 publications

References 35 publications

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Dataset for anomaly detection in a production wireless mesh community network

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

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