Wireless Sensor Networks Dataset Research Articles

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

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.

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Wireless Sensor Networks (WSNs) play a pivotal role in modern data-driven applications, yet concerns persist regarding the privacy and security of sensitive location information. The attributes of Wireless Sensor Networks (WSNs) make them susceptible to eavesdropping, enabling attackers to intercept data packets across single or multiple communication links. This interception allows for the extraction of sensitive data from various sensor information, presenting a significant challenge to location privacy. Consequently, it becomes crucial to implement effective measures for safeguarding the privacy of training sample data when utilizing WSNs. In this manuscript, Dynamically Stabilized Recurrent Neural Network (DSRNN) optimized with Mother Optimization Algorithm (DSRNN-MOA) is proposed. Initially data is taken from WSN dataset. Afterward the data is fed to Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) based pre-processing process. The pre-processing output is provided to the Dynamically Stabilized Recurrent Neural Network to enhance source location protection while addressing challenges related to recurrent network stability and gradient issues. The learnable parameters of the DSRNN is optimized using MOA. The proposed strategy, LPWSN-DSRNN-MOA, is implemented in MATLAB, and its effectiveness is assessed using a number of performance evaluation measures, including ROC analysis, accuracy, precision, recall, f1-score, mean squad error, and recall. The proposed LPWSN-DSRNN-MOA method shows the highest accuracy of 98%, precision of 99%, specificity of 98% and F1-score of 99% while comparing other existing methods such as Location Protection for Wireless Sensor Networks based on Artificial Neural Network(LPWSN-ANN), Location Protection for Wireless Sensor Networks based on Deep Neural Network (LPWSN-DNN), and Location Protection for Wireless Sensor Networks based on Machine Learning (LPWSN-ML) respectively.

MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

In the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.

DDoS Attack Detection in WSN using Modified Invasive Weed Optimization with Extreme Learning Machine

Wireless sensor networks (WSN) are the wide-spread methodology for its distribution of the vast amount of devoted sensor nodes (SNs) that is employed for sensing the atmosphere and gather information. The gathered information was transmitted to the sink nodes via intermediate nodes. Meanwhile, the SN data are prone to the internet, and they are vulnerable to diverse security risks, involving distributed denial of service (DDoS) outbreaks that might interrupt network operation and compromises data integrity. In recent times, developed machine learning (ML) approaches can be applied for the discovery of DDoS attacks and accomplish security in WSN. To achieve this, this study presents a modified invasive weed optimization with extreme learning machine (MIWO-ELM) model for DDoS outbreak recognition in the WSN atmosphere. In the presented MIWO-ELM technique, an initial stage of data pre-processing is conducted. The ELM model can be applied for precise DDoS attack detection and classification process. At last, the MIWO method can be exploited for the parameter tuning of the ELM model which leads to improved performance of the classification. The experimental analysis of the MIWO-ELM method takes place using WSN dataset. The comprehensive simulation outputs show the remarkable performance of the MIWO-ELM method compared to other recent approaches.

Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be a victim of distributed attacks. Attackers can perform intrusions to break down the network during the routing process and intercept gathered data by dropping them for example. To prevent the increase of security issues in WSN, many attack identification methods and models were proposed in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. For this work, a new method is introduced to analyze and classify an own WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security and its efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system that learns from routing datasets to identify network malicious entries. The proposed models are based on Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) stochastic assumptions. Also, the dimensionality reduction technique was deployed to select the most relevant features for the training. The experimentation phase was realized on its own and made the dataset that reflects different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with a classification accuracy of 92.18% using a 2 HMM/3 GMM classifier. This result demonstrates the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.

Hybrid Cyber-Security Model for Attacks Detection Based on Deep and Machine Learning

Nowadays, numerous attacks can be considered high risks in terms of the security of Wireless Sensor Networks (WSN). As a result, different applications are introduced to manage the data and information exchange and related security sides to be save in transmission of data. Recently, most of the security attacks are classified as cyber ones. These attacks interest in the system halting and destroying the data rather than stealing the data. In this paper, a cyber-attacks detection system is proposed based on an intelligent hybrid model that uses deep and machine learning technologies. The proposed model improves the cyber-attack detection speed. In addition, a feature reduction model is proposed using machine learning methods (PCA and SVD) to select the most related features to the adopted classes of attacks. This can positively affect the deep-learning model complexity. The obtained results demonstrate the superiority of the proposed hybrid model-based cyber detection system in comparison to the traditional ones in reaching an accuracy of 99.98%, 100%, 100%, 100% for precision, recall, and F1-measure respectively, and reducing the time to 23s for the datasets of Message Queuing Telemetry Transport-Dataset (MQTT-DS) and Wireless Sensor Networks Dataset (WSN-DS).

A many-objective whale optimization algorithm to perform robust distributed clustering in wireless sensor network

The substantial increase in the usage of wireless sensor networks (WSNs) encourages to develop data clustering in event monitoring applications. Many centralized algorithms with single objective optimization are employed to solve this problem. However privacy, security and technical constraints are key issues in traditional centralized approach. Moreover, many WSN applications like condition monitoring and target tracking require more than three objectives for effective partitioning of dataset. This paper proposes many-objective whale optimization algorithm to handle robust distributed clustering in WSN. Initially, a swarm based many-objective whale optimization (MaOWOA) is discussed where reference point based leader selection method is utilized in updating the solutions instead of grid based leader selection as in multi-objective approach. This method gives better convergence and diversity. The simulation result of proposed approach is evaluated on many-objective DTLZ test problems against existing many-objective methods which is faster in terms of simulation time and gives competitive results in terms of generational distance (GD), inverse generational distance (IGD), spacing (SP) and hyper volume difference (HVD). Further, the encouraging results of the proposed MaOWOA are applied to perform robust distributed clustering in WSNs which is termed as distributed many-objective clustering using whale optimization algorithm (DMaOWOA). In this approach, a weight based method is incorporated to detect and remove the outliers and diffusion method of cooperation is used for distributed clustering. The proposed DMaOWOA is tested on one synthetic and three practical WSN datasets. It is observed that DMaOWOA based clustering performs up to 6% and 8% improvement in terms of Silhouette index as compared to particle swarm optimization based many-objective distributed clustering (DMaOPSO) and distributed K-Means (DK-Means) clustering algorithm, respectively.

A New Regularized Recursive Dynamic Factor Analysis With Variable Forgetting Factor and Subspace Dimension for Wireless Sensor Networks With Missing Data

The dynamic factor analysis (DFA) is an effective method for reducing the dimension of multivariate time series measurements in wireless sensor networks (WSNs) for prediction, monitoring, and anomaly detection. In large-scale systems, it is crucial to be able to track the time-varying loadings (or subspace) and the underlying factor signals, identify their dimensions, and impute missing or outlier samples, which can arise from transmission loss, hardware failure, and other factors. This article proposes a new regularized recursive DFA algorithm with a variable forgetting factor (FF) and subspace dimension, which is computationally efficient and capable of missing data imputation. A novel multiple deflation (MD) and rank-one-modification-based approach is proposed to recursively estimate the eigenvalues and associated eigenvectors from the progressively extracted subspace via MD. This allows us to update efficiently the subspace dimension and loading (eigen) vectors. By modeling the factor signals as independent univariate autoregressive (AR) processes, one can utilize the forecast value for outlier detection and missing samples' imputation. Furthermore, variable FF and regularization schemes are proposed to improve the tracking capability and reduce the variance of the proposed recursive DFA algorithm. Experimental results using real WSN datasets show that the proposed algorithm achieves better tracking performance and accuracy than other conventional approaches, which may serve as a useful tool for prediction, monitoring, and anomaly detection in WSNs.

CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks.

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STASVDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STASVDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.

IDEG: Integrated Data and Energy Gathering Framework for Practical Wireless Sensor Networks Using Compressive Sensing

Minimizing the energy consumption through efficient data gathering as well as energy harvesting both are open research problems in the context of practical wireless sensor networks (WSNs). Although the number of approaches has been proposed to cater the above, they are limited in terms of computational complexity, energy efficiency, and the ease of implementation. In this paper, we propose an integrated data and energy gathering framework, named hereby as “iDEG,” for practical WSNs. The proposed work is based on compressive sensing and utilizes partial canonical identity (PCI) matrix that samples/picks fewer sensor nodes randomly at any time point instead of picking up a linear combination of data from all sensor nodes at all time points as used in the conventionally popular Gaussian or Bernoulli matrices. This reduces the computation cost, implementation complexity, and energy losses while improving the data recovery. Furthermore, the fewer sensors at any time point selected by PCI contribute for recovering data, while the rest of the nodes harvest energy during those time points. Performance of iDEG has been tested on a real WSN dataset from Intel Lab. Comparative results of iDEG with the conventional approaches highlight its efficacy.

A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks

In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consuming process in WSN, several energy efficient techniques have been proposed. Data compression is a popular energy efficient technique which helps to reduce the amount of data to be transmitted in the network resulting in significant power saving. This paper proposes a new algorithm called neighborhood indexing sequence (NIS) for data compression in WSN. The proposed NIS algorithm dynamically assigns shorter length codewords to each character in the input sequence by exploiting the occurrence of neighboring bits. Using the real world WSN dataset, it is shown that the compression performance of the NIS algorithm is superior to existing compression algorithms. Compared with existing methods, the proposed compression algorithm is not only efficient but also highly robust for different WSN dataset. The proposed algorithm attains a compression ratio of 89.13 with the bit rate of 1.74 per sample. Moreover, it achieved power savings up to 87.57% for the applied WSN dataset.

A new approach of anomaly detection in wireless sensor networks using support vector data description

Anomaly detection is an important challenge in wireless sensor networks for some applications, which require efficient, accurate, and timely data analysis to facilitate critical decision making and situation awareness. Support vector data description is well applied to anomaly detection using a very attractive kernel method. However, it has a high computational complexity since the standard version of support vector data description needs to solve quadratic programming problem. In this article, an improved method on the basis of support vector data description is proposed, which reduces the computational complexity and is used for anomaly detection in energy-constraint wireless sensor networks. The main idea is to improve the computational complexity from the training stage and the decision-making stage. First, the strategy of training sample reduction is used to cut back the number of samples and then the sequential minimal optimization algorithm based on the second-order approximation is implemented on the sample set to achieve the goal of reducing the training time. Second, through the analysis of the decision function, the pre-image in the original space corresponding to the center of hyper-sphere in kernel feature space can be obtained. The decision complexity is reduced from O( l) to O(1) using the pre-image. Eventually, the experimental results on several benchmark datasets and real wireless sensor networks datasets demonstrate that the proposed method can not only guarantee detection accuracy but also reduce time complexity.

An Efficient and Robust Data Compression Algorithm in Wireless Sensor Networks

Data compression is a useful technique in the deployments of resource-constrained wireless sensor networks (WSNs) for energy conservation. In this letter, we present a new lossless data compression algorithm in WSNs. Compared to existing WSN data compression algorithms, our proposed algorithm is not only efficient but also highly robust for diverse WSN data sets with very different characteristics. Using various real-world WSN data sets, we show that the proposed algorithm significantly outperforms existing popular lossless compression algorithms for WSNs such as LEC and S-LZW. The robustness of our algorithm has been demonstrated, and the insight is provided. The energy consumption of our devised algorithm is also analyzed.

Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, $(k)$-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust.

Nonuniform Compressive Sensing for Heterogeneous Wireless Sensor Networks

In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial profile of some physical phenomena. We base our work on two observations. First, most physical phenomena are compressible in some transform domain basis. Second, most WSNs have some form of heterogeneity. Given these two observations, we propose a nonuniform compressive sensing method to improve the performance of WSNs by exploiting both compressibility and heterogeneity. We apply our proposed method to real WSN data sets. We find that our method can provide a more accurate temporal-spatial profile for a given energy budget compared with other sampling methods.