Security In Wireless Sensor Networks Research Articles

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

SummaryDetecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs), as they are susceptible to various attacks that compromise nodes and yield inaccurate outcomes. Conventional attack detection approaches face challenges like high false positives, vulnerability to complex attacks, and limited adaptability to changing attacks due to predefined patterns. Additionally, the computational strain on resource‐constrained nodes hampers network efficiency, demanding innovative and resilient security solutions. Therefore, this research work introduces Novel Hybrid Approaches for Privacy‐preserved Multiple Attacks Detection (NHAPMAD) framework in both network attack detection system and host attack detection system. This topology bolsters the network's defense against various types of attacks, thereby ensuring the preservation of sensitive data privacy. Federated learning has been incorporated into the NHAPMAD framework to protect user privacy in WSNs. The introduced framework provides better anomaly detection and reduces false alarms. Additionally, the introduced model protects sensitive data, ensuring the security and integrity of the entire WSN, promoting a stable and dependable operating environment. The introduced method exhibits superior performance across various metrics like overall accuracy (99.6%), F1‐score (99.59%), recall (99.47%), precision (99.63%), detection rate (99.5%), processing time (0.0009 s), etc. compared to traditional approaches, marking a significant advancement in the realm of WSN security.

A Hybrid Rider Optimization with Deep Learning Driven Intrusion Detection Farmwork in Wireless Sensor Network

Introduction: An array of hazards currently exists in cyberspace, prompting extensive research to tackle these concerns. Intrusion Detection Systems (IDS) are a mechanism used to provide security in Wireless Sensor Networks (WSN). The IDS continue to encounter significant challenges in accurately identifying unknown attacks. Conventional Intrusion Detection Systems (IDS) commonly rely on Deep Learning (DL) algorithms, which utilise binary classifiers to classify attacks. The data dimension attribute is affected inside large-scale high-dimensional data sets. Methods: This research introduces a hybrid GFSO (HGFSO) model combined with Deep Learning Driven Intrusion Detection (HGFSO-DLIDS) to tackle this problem. The HGFSO approach is developed by merging the parameter selection methods of the Felis Margarita Swarm Optimisation (FMSO), the Grampus optimisation algorithm (GOA), and the Deep Convolutional Neural Network (DCNN) with BiLSTM (Bidirectional Long Short-Term Memory) algorithm. Results: The model training utilised real-time traffic statistics, including the KDDCup 99 and WSN-DS datasets. After being trained and validated using the datasets, the model's performance is assessed by multi-class classification, achieving accuracy rates of 99.89% and 99.64% respectively.Conclusion: As a result, this occurrence leads to a decrease in the overall effectiveness of detecting assaults. Deep learning may enhance the creation of an intrusion detection system by eliminating complex features in the raw data, resulting in a more precise classification method.

Enhancing security in wireless sensor networks: A fusion of deep learning and energy-efficient routing

The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications.

Enhancing Security and Energy Efficiency in Wireless Sensor Networks for IoT Applications

Wireless Sensor Networks (WSNs) are fundamental components within the Internet of Things (IoT) scene, empowering information collection and transmission in different applications. This research papers on upgrading the security and vitality effectiveness of WSNs for ideal IoT usefulness. Novel strategies were created and assessed to address these vital perspectives. Security improvements included the usage of lightweight cryptographic calculations, intrusion detection systems (IDS), and secure directing conventions. These measures essentially reinforced organized strength against cyber dangers, with interruption discovery rates surpassing 95% and secure steering conventions accomplishing a strength score of over 97%. Vitality productivity optimizations enveloped communication convention improvements, information accumulation strategies, and energetic control administration methodologies. These procedures come about in outstanding decreases in vitality utilization, with optimized conventions diminishing vitality utilization by up to 30% and information conglomeration techniques yielding vitality reserve funds of up to 50%. Test assessments were conducted utilizing both simulation-based approaches and real-world organizations. Recreation comes about, obtained utilizing NS-3, showcasing the viability of the proposed strategies over different network topologies, activity designs, and attack scenarios. Real-world experimentation employing a WSN testbed approved the viable achievability and viability of the techniques. Comparative investigations with existing writing highlighted the oddity and importance of the commitments, checking considerable progressions in WSN security and vitality proficiency.

A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Secure and Efficient Wireless Sensor Network and Machine Learning -based Monitoring System for Student Physical and Mental Health

In this research we have reviewed the performance of machine learning algorithms and WSNs for the monitoring student physical and mental health. We have found that logistic regression is a stronger alternative after an exhaustive evaluation of machine learning algorithms that consistently produce superior performance metrics than Gaussian mixture models (GMM). This has demonstrated average accuracy of 87.1% across 10 experiments, besting the average accuracy of 83.8% of GMM. It also illustrates greater efficacy for assessing system security, producing average accuracy of 86.6%, with 91.9% for GMM. The findings demonstrate the significance of select a machine learning algorithms that meets the specific needs of monitoring student health systems. They are also an illustration of logistic regressions dependability, and adeptness at identifying subtle changes in student health indicators that allow for proactive initiation of interventions and support mechanisms tailored to individual students’ needs. Going forward, monitoring systems should next be further refined and optimised so that they can more effectively support both student well-being and academic success in the complex and diverse educational settings of Indian universities.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

New Technology for Advanced Results for Security Wireless Sensor Networks

Designing wireless sensor networks (WSNs), which are new technologies with the potential to perceive, analyze, communicate, and send data, is difficult since it impacts the network's performance and longevity. The goals of this study are to enhance network longevity, mitigate power usage, and safeguard transmitted data. An energy-saving procedure was suggested as a means to accomplish this objective. Every node in the network uses the same amount of electricity according to this protocol. The restricted resources of the nodes are also taken into account while recommending an algorithm. Additionally, in order to provide optimal outcomes for assessing the system's effectiveness. The suggested model and the network's performance were evaluated using the Quality of Service (Q o S) metrics of Packet Loss Ratio (PLR), Network Energy Consumption (NEC), and Network Throughput Ratio (NTR). , this method ensures that the data remains intact. Additionally, we came up with a method to save electricity. This protocol ensures that all nodes in the network use an equal amount of electricity

Trust based Routing – A Novel Approach for Data Security in WSN based Data Critical Applications

Wireless technology has changed the way entities communicate with one another. Wireless networks have created several opportunities in fields such as military, health care, and habitat monitoring, to name a few. However, only a few data-critical applications are built on wireless sensor networks, such as border reconnaissance, detecting infringement, and patient monitoring. These applications require the processing of a large amount of private data. Because most applications are data-sensitive, securing data transmission among wireless sensor networks is crucial. While incorporating data security, the most important requirement of wireless sensor nodes being energy optimized also need to be kept in consideration. There are various forms of assaults that are relevant in Wireless Sensor Networks (WSN). Attacks like Black Hole attacks, sink hole attacks, False data Injection attacks etc. are the most commonly seen attacks on WSNs. The common element in all these attacks is the concept of malicious / compromised node - a node which either drops / modifies the data content while forwarding it. Existing techniques for data security generally use cryptographic algorithms, but the use of cryptographic algorithms is in contrast with the energy optimization requirement of sensor nodes. An energy efficient data security scheme needs to be developed. The proposed system analyses several attacks and proposes a multi-layer data security approach to prevent change of data / dropping of data by the compromised nodes. The proposed system is a routing protocol referred as Trust Based Routing (TBR). A concept of trust value of a node is the core idea of TBR. Forwarding node is selected based on highest trust value and thus avoid malicious / compromised nodes from being involved in the routing process. The trust factor is calculated by considering the number of packets dropped, packets rejected, and the node's remaining energy. The idea of TBR is enhanced by incorporating the concept of past trust and trust of node towards a specific destination. This proposed scheme is referred as Extended Trust Based Routing (ETBR). This scheme is further enhanced by including Direct Trust, Indirect Trust and Energy Trust concepts. This scheme is referred as Consolidated Trust Estimation – Trust Based Routing (CTE-TBR). Network Simulator NS2 is used to simulate the proposed schemes. Various network factors are compared to classic Adhoc On-Demand Vector (AODV) and newly proposed schemes. The result indicates the effectiveness of the proposed data security scheme in terms of energy efficiency and Packet Delivery ratio (PDR).

Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using Bioinspired Algorithms (EKD-SOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS-TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

An Overview of Problems and Difficulties with ML in WSNs Protection

There are serious, mutually exclusive problems with resources and security in wireless sensor networks. As security complexity rises, battery consumption will follow suit. It is useless to rely on the security of common protocols like encryption and key management because of the limited capacity of wireless sensor networks and their dynamic architecture. Algorithms for machine learning are one of the proposed ways to combine judgment, awareness and observation to deliver intelligence services in this kind of network. Machine learning algorithms provide new issues related to training and the amount of data required for training. This paper neatly covers the architecture of wireless sensor networks as well as the security challenges they face. It also goes into the challenges and recommended fixes for improving sensors' ability to identify dangers, assaults, hazards, and suspicious activity via their ability to learn and grow on their own using machine learning techniques. This might be achieved by reducing the cost of WSNs across several domains. This research also addresses open issues with machine learning algorithms that are pertinent to adapting them to the properties of sensors inside that kind of network.

The Network Security in Wireless Sensor Networks: A Review

Wireless Sensor Networks (WSN) is an innovative technology predicts to be used ever more in the next to future due to their data achievement and data processing capability. Security for WSN requires to be considered in order to defend the functionality of this network such as the information it communicate and the position of nodes. The safety models & protocols used in traditional networks are not appropriate to WSNs because of its limited resource constraints.

Optimising barrier placement for intrusion detection and prevention in WSNs.

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

Enhancing wireless sensor network security and efficiency with CNN-FL and NGO optimization

In this study, we discuss how Wireless Sensor Networks (WSN) are susceptible to malicious assaults from malfunctioning nodes, which jeopardise the security of data transmission in crucial applications. We use a Convolutional Neural Network coupled with Fuzzy Logic (CNN-FL) for improved deep learning, introducing a unique method, effectively identifying and categorizing trustworthy and malicious nodes. This is combined with a routing strategy enhanced by the Neuro Genetic Optimizer (NGO), built on the LEACH routing protocol and based on a Roulette wheel selection mechanism. The WSN's lifetime is increased by our suggested routing method, which not only provides safe data transmission but also dramatically reduces latency and energy consumption. Simulation findings show that our technique outperforms current protocols as ASNGSRA, DMCNN, and FRCSROD in terms of packet delivery ratio, energy economy, and latency analysis.

Blockchain assisted node validation approach for security provisioning in WSN environment

Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput.

Trust aware angle based secure routing approach for wireless sensor network

Security in wireless sensor network (WSN) is an important approach in the present context as data breaching is becoming more. The data to be routed from source to destination needs more security as WSN has no specific security approach by default. This paper proposes trust based security in WSN using approach. The secure line is drawn from head node to its cluster end point called as angle to provide the security to the nodes which are transferring the data to the head node. Secure line becomes the trust worth line where mobile agent migrates to all the corresponding nodes which are along or near to the secure lines, collects the data and encrypt them. Finally, the data is sent to sink node from head node using a secure path. The agent paradigm is responsible for creating the angle from head node to cluster boundary. Multiple angles can be created if numbers of nodes are more and deployed at different locations. The result shows that the security provided is much better to combat the intruder involvement to breach data along with better network lifetime and minimum delay than compare to conventional techniques.