Attacks In Wireless Sensor Networks Research Articles

Security Challenges, Mitigation Strategies, and Future Trends in Wireless Sensor Networks: A Review

Wireless Sensor Networks (WSNs) represent an innovative technology that integrates compact, energy-efficient sensors with wireless communication functionalities, facilitating instantaneous surveillance and data gathering from the surrounding environment. WSNs are utilized across diverse domains, such as environmental monitoring, industrial automation, healthcare, smart agriculture, home automation, and beyond. Due to the inherent characteristics of WSNs they face many security challenges ranging from resource-based attacks, such as energy depletion or computational overload, to eavesdropping, interception, and tampering. Moreover, the dynamic and often ad-hoc deployment of sensors in varying environments increases their vulnerability to physical intrusion attacks, the distributed and collaborative nature of WSNs raises concerns about data integrity, as compromised nodes can potentially propagate misleading or malicious information throughout the network. In this article, we categorize WSN attacks, identifying vulnerabilities and corresponding mitigation strategies. We also explore current research directions in WSN security, emphasizing the challenges in addressing these issues.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network

Wireless Sensor Networks (WSNs) are susceptible to various security threats owing to its deployment in hostile environments. Intrusion detection system (IDS) contributes a critical role on securing WSNs by identifying malevolent activities and ensuring data integrity. Traditional IDS techniques often struggle with the dynamic and resource-constrained nature of WSNs. In this paper, Dynamically Stabilized Recurrent Neural Network Optimized with Intensified Sand Cat Swarm Optimization for Wireless Sensor Network Intrusion identification (DSRNN-ISCOA-ID-WSN) is proposed. Initially, the input data is amassed from WSN-DS dataset. After that, the pre-processing segment receives the data. In pre-processing stage, redundant and biased records are removed from input data with the help of Adaptive multi-scale improved differential filter (AMSIDF). Then the optimal are selected by utilizing Wolf-Bird Optimization Algorithm (WBOA). DSRNN is used to classify the data as Normal, Grey hole, Black hole, Time division multiple access (TDMA), and Flooding attacks. Then Intensified Sand Cat Swarm Optimization (ISCOA) is employed to optimize the weight parameters of DSRNN for accuracte classification. The proposed DSRNN-ISCOA-ID-WSN technique is implemented Python. The performance of the proposed DSRNN-ISCOA-ID-WSN approach attains 29.24 %, 33.45 %, and 28.73 % high accuracy; 30.53 %, 27.64 %, and 26.25 % higher precision when compared with existing method such as Machine Learning-Powered Stochastic Gradient Descent Intrusions Detection System for WSN Attacks (SGDA-ID-WSN), An updated dataset to identify threats in WSN (CNN-ID-WSN) and Denial-of-Service attack detection in WSN: a Low-Complexity Machine Learning Model (DTA-ID-WSN) respectively.

Detection of Wormhole Attack in Wireless Sensor Networks: A Survey

Detection of Wormhole Attack in Wireless Sensor Networks: A Survey

A New Lightweight Cryptographic Cipher for Detection and Prevention of Replay Attacks in Wireless Sensor Networks

A New Lightweight Cryptographic Cipher for Detection and Prevention of Replay Attacks in Wireless Sensor Networks

Localization and Detection of Sinkhole Attacks in Wireless Sensor Networks Based on Denial of Service (DoS) Attacks

A wireless sensor network (WSN) uses uniformly deployed and positioned sensors to regularly send sensed data to a centralized station. Sinkhole attacks, in which a malicious node draws packets from other legitimate sensor devices and drops them, are the main danger to the WSN network layer and continue to be a difficult problem on wireless sensor networks. Security becomes a major issue since these networks have limited assets including less memory, less energy, and less transmission capacity. Additionally, given the dynamic context in which they are deployed, they are vulnerable to a variety of Denial of Service (DoS) assaults, including wormhole, sinkhole, and black hole attacks. The most dangerous routing attack at the network layer is known as a "sinkhole assault," which directs all network traffic onto a fake path by sending false information under the impression that it is the quickest one to the server. We explore sinkhole attacks to prevent them, and this work proposes a method of detection based on the redundancy process. Messages are transmitted across multiple paths to the suspicious nodes. Upon a thorough evaluation of the response, the attacked nodes are ultimately verified. Finally, a simulation is run to evaluate the method's efficacy. Furthermore, the simulation indicates that the strategy might be somewhat successful. Finally, it draws attention to difficulties and offers a prospective viewpoint for identifying sinkhole attacks.

GD3N: Adaptive clustering-based detection of selective forwarding attacks in WSNs under variable harsh environments

Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably challenging to detect. This difficulty arises from the ability of malicious nodes to imitate the behavior of normal nodes, and selectively drop data packets, which makes them virtually indistinguishable from normal ones, particularly under conditions of poor channel quality. To address this challenge with harsh environments, we introduce a novel methodology termed GD3N. This approach is underpinned by the design of a unique type of data point that encapsulates both short-term and long-term forwarding behaviors of nodes. It combines a refined version of the Gradient Diffusion Density-based Spatial Clustering of Applications with Noise (GD-DBSCAN) algorithm, with a novel Double-Parameter Neighbor Voting (DP-NV) method based on the data set. These innovations contribute to a significant enhancement in detection accuracy and a reduction in computational complexity when compared to traditional DBSCAN and NV methods. Simulation results show that our GD3N achieves a false detection rate (FDR) of less than 2%, a missed detection rate (MDR) of below 10%, and an overall detection accuracy rate (DAR) of over 95% across various testing scenarios.

Dynamic properties of the multimalware attacks in wireless sensor networks: Fractional derivative analysis of wireless sensor networks

Abstract Due to inherent operating constraints, wireless sensor networks (WSNs) need help assuring network security. This problem is caused by worms entering the networks, which can spread uncontrollably to nearby nodes from a single node infected with computer viruses, worms, trojans, and other malicious software, which can compromise the network’s integrity and functionality. This article discusses a fractional S E 1 E 2 I R {\mathsf{S}}{{\mathsf{E}}}_{1}{{\mathsf{E}}}_{2}{\mathsf{I}}{\mathsf{R}} model to explain worm propagation in WSNs. For capturing the dynamics of the virus, we use the Mittag–Leffler kernel and the Atangana–Baleanu (AB) Caputo operator. Besides other characteristics of the problem, the properties of superposition and Lipschitzness of the AB Caputo derivatives are studied. Standard numerical methods were employed to approximate the Atangana–Baleanu–Caputto fractional derivative, and a detailed analysis is presented. To illustrate our analytical conclusions, we ran numerical simulations.

An improved localization approach based on Sybil attack for WSN

Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including wireless sensor networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite the DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new approaches of localization that combine the DV-Hop with the chicken swarm optimization approach. The primary objective is to improve the precision of DV-hop-based approaches. Through extensive simulations, we compare the efficiency of our novel localization algorithms with competitive approaches considering random network topologies. The results derived from the simulation validate the efficiency of our proposed algorithms. The proposed algorithm, the HW-DV-HopCSO, achieves a considerable improvement regarding positioning accuracy.

AutoGMM-RWKV: A Detecting Scheme Based on Attention Mechanisms Against Selective Forwarding Attacks in Wireless Sensor Networks

AutoGMM-RWKV: A Detecting Scheme Based on Attention Mechanisms Against Selective Forwarding Attacks in Wireless Sensor Networks

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks

Detecting denial of sleep attacks by analysis of wireless sensor networks and the Internet of Things

The Internet of Things (IoT) amalgamates a large number of physical objects that are distinctively identified, ubiquitously interconnected and accessible through the Internet. IoT endeavors to renovate any object in the real world into a computing device that has sensing, communicating, computing and control capabilities. There are a budding number of IoT devices and applications and this escort to an increase in the number and complexity of malicious attacks. It is important to defend IoT systems against malicious attacks, especially to prevent attackers from acquiring control over the devices. Energy utilization is significant for battery-enabled devices in the IoT and wireless sensor networks which are operated long time period. The Denial-of-Sleep attack is an explicit type of denial-of-service attack that beleaguered a battery-powered device’s power supply that results in the exhaustion of this critical resource. This paper focuses on the survey on Denial of sleep attacks in Wireless Sensor networks and the IoT.

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR

Sensor Nodes (SNs) are utilized by Wireless Sensor Networks (WSNs) to recognize their environment; in addition, the WSN delivers data from sensing nodes to the sink. The WSNs are exposed to several security threats owing to the broadcast performance of transmission along with the increase in the growth of application regions. Countermeasures like Intrusion Detection and Prevention Systems (IDPS) should be adopted to overcome the aforementioned attacks. By implementing these systems, several intrusions can be detected in WSN; also, WSN can be prevented from various security attacks. Therefore, identifying the general attack that influences the SNs mentioned as Distributed Denial of Service (DDoS) attack and recuperating the data utilizing Soft Swish (SS)-Linear Scaling-centered Adam Convolution Neural Network (SS-LSACNN) along with Two’s Compliment Shift Reverse (TCSLR) operation are the intentions of this work. Firstly, for extracting the vital features, the data gathered as of the dataset are utilized. After that, the extracted features are pre-processed. It is then utilized for attack detection. The null features and the redundant data are removed in preprocessing. By employing the Correlation Coefficient-centered Synthetic Minority Oversampling Technique (CC-SMOTE) methodology, data separation regarding classes and data balancing was performed to prevent the imbalance issue. Subsequently, to provide the preprocessed data for attack detection, the Numeralization and feature scaling are executed. After that, by utilizing Chebyshev Distance (CD)-centric K-Means Algorithm (KMA), the real-time SNs are initialized as well as clustered. The data gathered as of the SNs are utilized for attack detection following the clustering phase. Following the detection phase, the data being attacked are amassed in the log file; similarly, the non-attacked data are inputted into the prevention phase. Next, the experiential analysis is carried out for examining the proposed system’s efficacy. The outcomes revealed that the proposed model exhibits 98.15% accuracy, 97.59% sensitivity, 95.72% specificity, and 95.48% F-measure, which displays the proposed model’s efficacy.

DoS attack detection using online learning techniques in wireless sensor networks

Wireless sensor network (WSN) models pose substantial security vulnerabilities since most WSNs are deployed in unattended hostile environments. This research focuses on denial-of-service (DoS) attack detection, a crucial problem in WSN security. Previous research has mostly focused on offline machine learning algorithms, which require long-term data collection and cannot continually adapt to new data. Online learning is thus more suitable for detecting DoS attacks in WSN due to the benefit of continuous improvement with fresh data. Nevertheless, existing online DoS attack detection algorithms do not take internal and external data interference into consideration. Thus, noisy data might have a negative effect on the performance of the model. Moreover, the data includes features that are redundant or unnecessary for the classification. Hence, the selection of proper features not only decreases computational time but also improves the performance of the model. This study proposes an online-learning-based approach for detecting DoS attacks in WSN. Specifically, a feature selection method is proposed to identify the most appropriate attributes for the training process. Furthermore, a noise-tolerant online passive-aggressive multi-class classifier is also developed. The performance of our proposed method is investigated in terms of accuracy, precision, recall, and F1-score, and it proves to be competitive.

A Balanced Intrusion Detection System for Wireless Sensor Networks in a Big Data Environment Using CNN-SVM Model

Wireless Sensor Networks (WSNs) were exposed to several distinct safety issues and attacks regarding gathering and sending data. In this scenario, one of the most prevalent WSN assaults that may target any tier of the protocol stack is the Denial of Service (DoS) attack. The current research suggested various strategies to find the attack in the network. However, it has classification challenges. An effective ensemble deep learning-based intrusion detection system to identify the assault in the WSN network was, therefore, suggested in this research to address this issue. The data pre-processing involves converting qualitative data into numeric data using the One-Hot Encoding technique. Following that, Normalization Process was carried out. Then Manta-Ray Foraging Optimization is suggested to choose the best subset of features. Then Synthetic Minority Oversampling Technique (SMOTE) oversampling creates a new minority sample to balance the processed dataset. Finally, CNN–SVM classifier is proposed to classify the attack kinds. The Accuracy, F-Measure, Precision, and Recall metrics were used to assess the outcomes of 99.75%, 99.21%, 100%, and 99.6%, respectively. Compared to existing approaches, the proposed method has shown to be extremely effective in detecting DoS attacks in WSNs.

Comprehensive Trust based Routing Protocol to Mitigate Black-hole attack in Wireless Sensor Networks

Wireless Sensor Network (WSN) is a collection of sensor nodes, that sense environmental data and send it to the administrator for further processing. Sensor nodes are wireless devices with limited battery and are vulnerable to security attacks such as black hole attack, gray hole attack, sink hole attack etc. Researchers have proposed many security mechanisms to mitigate security attacks. Trust based approaches have gained tremendous interest among researchers to embed security in WSNs. A Trust Based Secure Routing Protocol to mitigate black hole attack is presented in this paper. The protocol computes comprehensive trust value for each node and routes the packets only through the nodes with trust value > 0.5. The results of the proposed protocol are compared with TBSEER [10] protocol. The results show an improvement in Packet Delivery Ratio (PDR), throughput, End to End (EED), routing overhead and energy consumption.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.

Secrecy Performance Analysis of Cooperative Multihop Transmission for WSNs under Eavesdropping Attacks.

Multihop transmission is one of the important techniques to overcome the transmission coverage of each node in wireless sensor networks (WSNs). However, multihop transmission has a security issue due to the nature of a wireless medium. Additionally, the eavesdropper also attempts to interrupt the legitimate users' transmission. Thus, in this paper, we study the secrecy performance of a multihop transmission under various eavesdropping attacks for WSNs. To improve the secrecy performance, we propose two node selection schemes in each cluster, namely, minimum node selection (MNS) and optimal node selection (ONS) schemes. To exploit the impact of the network parameters on the secrecy performance, we derive the closed-form expression of the secrecy outage probability (SOP) under different eavesdropping attacks. From the numerical results, the ONS scheme shows the most robust secrecy performance compared with the other schemes. However, the ONS scheme requires a lot of channel information to select the node in each cluster and transmit information. On the other side, the MNS scheme can reduce the amount of channel information compared with the ONS scheme, while the MNS scheme still provides secure transmission. In addition, the impact of the network parameters on the secrecy performance is also insightfully discussed in this paper. Moreover, we evaluate the trade-off of the proposed schemes between secrecy performance and computational complexity.

Genetic algorithm-based Wormhole attack detection in WSN

Wireless Sensor Network (WSN) technology has received a lot of attention and has opened new applications. Examining security assaults on the network layer and coming up with a solution for them is one of the difficult topics in modern networks. Therefore, to achieve our goals, we must develop a technique that can identify an attack, prevent the attacker from accessing the network, and do so while consuming the fewest amounts of battery power. This technique must also use a simple, reliable algorithm. The use of a genetic algorithm is suggested in this research as a detection of wormhole attacks in WSNs. The strategy makes use of a genetic algorithm to investigate and identify the ideal group of parameters for a wormhole detection technique. Even in the case of enormous networks, the search for the most effective parameters may be carried out quickly and effectively by using this genetic algorithm.