Detection In Wireless Sensor Networks Research Articles

Deep Learning-Enhanced Cluster Head Optimization for Intrusion Detection in Wireless Sensor Networks

Deep Learning-Enhanced Cluster Head Optimization for Intrusion Detection in Wireless Sensor Networks

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

AbstractIn 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.

Enhancing intrusion detection in wireless sensor networks through deep hybrid network empowered by SC-attention mechanism

Enhancing intrusion detection in wireless sensor networks through deep hybrid network empowered by SC-attention mechanism

An adaptive trust system for misbehavior detection in wireless sensor networks

An adaptive trust system for misbehavior detection in wireless sensor networks

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising  approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

SVM‐SFL based malicious UAV detection in wireless sensor networks

SummaryIn the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

IRADA: integrated reinforcement learning and deep learning algorithm for attack detection in wireless sensor networks

IRADA: integrated reinforcement learning and deep learning algorithm for attack detection in wireless sensor networks

Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms

This study discusses fully distributed fault detection via a wireless sensor network. Initially, we suggested using the Convex hull approach to determine a range of extreme points including nearby nodes. As the number of nodes rises, the message's duration is constrained. Secondly, in order to enhance convergence performance and identify node errors, we suggested using a convolution neural network (CNN) and a Naïve Bayes classifier. Lastly, we use real-world datasets to examine CNN, convex hull, and Naïve bayes algorithms to find and classify the defects. Based on performance measures, the results of simulations and experiments demonstrate that the CNN algorithm has better-identified defects than the convex hull technique while maintaining feasibility and economy.

Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability

As the digital landscape expands rapidly due to technological advancements, cybersecurity concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are crucial for identifying unusual network traffic indicative of malicious activity, have become a necessity. These systems can be either hardware or software-based. However, traditional IDS models often fail to adequately protect data privacy and detect complex, unique breaches, particularly within Wireless Sensor Networks (WSNs). To address these limitations, this paper proposes a novel Stacked Convolutional Neural Network and Bidirectional Long Short Term Memory (SCNN-Bi-LSTM) model for intrusion detection in WSNs. This model leverages Federated Learning (FL) to enhance intrusion detection performance and safeguard privacy. The FL-based SCNN-Bi-LSTM model is unique in its approach, allowing multiple sensor nodes to collaboratively train a central global model without revealing private data, thereby alleviating privacy concerns. The deep learning methodology of the SCNN-Bi-LSTM model effectively identifies sophisticated and previously unknown cyber threats by meticulously examining both local and temporal linkages in network patterns. The model has been specifically designed to detect and categorize different types of Denial of Service (DoS) attacks using specialized WSN-DS and CIC-IDS-2017 datasets. Compared to traditional Artificial Deep Neural Network (ADNN) models, our proposed FL-SCNN-Bi-LSTM model demonstrated superior detection rates for complex and unknown attacks, significantly improving IDS performance. The model achieved a notable classification accuracy of approximately 99.9% precision and recall on both datasets, substantially reducing false positives and negatives. Our research underscores the potential of federated learning and deep learning in enhancing the security and privacy of WSNs. The proposed FL-SCNN-Bi-LSTM architecture not only facilitates the identification of complex cyber threats but also exemplifies how deep learning techniques can be employed to bolster intrusion detection systems while preserving user data privacy.

WSN-BFSF: A New Data Set for Attacks Detection in Wireless Sensor Networks

WSN-BFSF: A New Data Set for Attacks Detection in Wireless Sensor Networks

Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods

This paper proposes an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The study employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. The Synthetic Minority Excessively Technique is introduced for data balancing, followed by intrusion detection systems and network traffic categorization. The research evaluates a deep learning-based feed-forward neural network algorithm's accuracy, precision, recall, and F-measure across three vital datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. Comparative analysis against benchmark machine learning approaches is also conducted. The proposed algorithm demonstrates exceptional performance, achieving high accuracy and reliability in intrusion detection for WSNs. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.

Analysis of Machine Learning Algorithms for Intrusion Detection in Wireless Sensor Networks

Analysis of Machine Learning Algorithms for Intrusion Detection in Wireless Sensor Networks

Fault detection in wireless sensor networks with deep neural networks

This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.

A Study on DDoS Attack Detection Technique Prior to Machine Learning in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are integral to a wide range of critical applications, including environmental monitoring, healthcare, and industrial control systems. Then delves into DDoS attacks, elucidating the various attack types, motives, and their detrimental effects on WSNs. The core of the study investigates DDoS detection techniques, encompassing signature-based, anomaly-based, and hybrid approaches. Optimization mechanisms for DDoS detection in WSNs are explored in-depth, including resource-efficient algorithms and scalability solutions. Consideration is given to striking a balance between detection accuracy and resource consumption, essential in the energy-limited world of WSNs. Machine learning and artificial intelligence's role in DDoS detection is elucidated, with a focus on feature selection, dimensionality reduction, and the application of various supervised and unsupervised learning algorithms. Cross-layer approaches, coordinating physical, data-link, and network layers, are discussed to enhance DDoS detection's robustness against sophisticated attacks. Energy efficiency considerations are integral, with strategies outlined to prolong sensor nodes' lifespans while ensuring reliable DDoS detection. The study culminates in the presentation of real-world experiments and simulations, offering insights into the practical performance of DDoS detection and optimization mechanisms. Various evaluation metrics are employed, such as detection rate, false positives, and resource utilization.

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

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.

Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Hybrid Model-Based Intrusion Detection in Wireless Sensor Network on the Basis of Risk and Link Quality

Nowadays, Wireless Sensor Networks (WSN) face more security threats due to the increased service of data transmission at high speed in almost all applications. The security of the network must be ensured by identifying abnormal traffic and current emerging threats. The most promising model for safeguarding the core network from outside attacks is Intrusion Detection Systems (IDS). This work focuses on the introduction of clustering-based intrusion detection in WSN. Initially, clustering takes place, where the nodes are grouped under certain constraints via selecting the optimal Cluster Head (CH). The considered constraints are energy, delay, distance, risk, and link quality. This optimal selection takes place by a new hybrid optimization algorithm termed as Truncate Combined Bald Eagle Optimization (TCBEO) algorithm. The subsequent process is intrusion detection, where a hybrid detection model combining a Convolutional Neural Network (CNN) & Bi-directional Gated Recurrent unit (Bi-GRU) is employed, which is trained with features like improved entropy and correlation taking into consideration of constraints like energy and distance, respectively. Eventually, the suggested work’s effectiveness is affirmed against existing techniques using various performance metrics.

Multi-modal for Energy Optimization and Intrusion Detection in Wireless Sensor Networks

Multi-modal for Energy Optimization and Intrusion Detection in Wireless Sensor Networks

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.

Energy-aware and Context-aware Fault Detection Framework for Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.