Security In Sensor Networks Research Articles

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.

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review

A Deterministic Approach with Markov Chain Algorithm to Impose Higher Order Security in Dynamic Wireless Sensor Networks (DWSN)

A Deterministic Approach with Markov Chain Algorithm to Impose Higher Order Security in Dynamic Wireless Sensor Networks (DWSN)

Securing wireless sensor networks with novel hybrid lightweight cryptographic protocols

WSN are essential in the Internet of Things (IoT) framework as they enable the gathering and transmission of data in different fields. The limited resources of sensor nodes in these networks require security protocols that are lightweight, striking a balance between data protection and efficiency. This research paper introduces a new hybrid cryptographic protocol called AES-128-CCM + Diffie-Hellman (ACDH), which aims to tackle the security issues encountered by WSN. Here present thorough assessment of the ACDH protocol, with a specific emphasis on critical factors including Security Strength, Energy Efficiency and Communication Overhead. The comparative study examines the ACDH protocol in relation to three well-established lightweight cryptographic protocols like Lightweight IPSec, BLAKE2, and Simon. We analyze and highlight the differences between these protocols. These protocols function as standards for evaluating the efficiency of ACDH in a practical WSN setting. The evaluation unequivocally showcases the supremacy of the proposed ACDH model across various crucial aspects. ACDH demonstrates exceptional Security Strength, guaranteeing strong resilience against established cryptographic attacks and providing a high degree of data safeguarding. It demonstrates exceptional Energy Efficiency, enabling sensor nodes to preserve energy resources while upholding a high standard of security ACDH reduces Resource Utilization, optimizing the utilization of memory and processing power. The protocol’s Communication Overhead is significantly minimal, guaranteeing the most efficient utilization of restricted bandwidth. The ACDH hybrid model consistently surpasses its counterparts in performance, making it the optimal selection for securing WSNs while adhering to the resource limitations of sensor nodes. The results of our research highlight the capability of ACDH as an innovative solution for developing lightweight cryptographic protocols that can improve the security and efficiency of Wireless Sensor Networks in the growing IoT environment.

Robust and energy-efficient authentication protocols for medical sensor networks

The growing incorporation of medical sensor networks into healthcare systems has emphasized the crucial requirement for secure and energy-efficient authentication protocols. The purpose of this study is to present a new method called Sidechain-based Trusted Platform Module (s-TPM) authentication, which aims to overcome the inherent difficulties in ensuring the security of medical sensor networks. This study performs a thorough comparison of s-TPM with two well-established authentication protocols, LEAP and TinySec, across multiple evaluation parameters, such as security, energy efficiency, and performance. The s-TPM authentication method utilizes sidechain technology to strengthen the resilience of authentication procedures in medical sensor networks. s-TPM utilizes a decentralized and tamper-proof system to reduce security weaknesses, offering a robust protection against unauthorized entry and data breaches. The results indicate that s-TPM surpasses LEAP and TinySec in terms of security resilience, guaranteeing a dependable protection against diverse cyber threats. Moreover, s-TPM demonstrates exceptional energy efficiency, which enhances the longevity of medical sensor devices’ operation while upholding a high standard of security. The results emphasize the importance of utilizing innovative methods, like s-TPM, to create authentication protocols that are specifically designed for the distinct difficulties encountered in medical settings.

Challenges and Potential Approaches in Wireless Sensor Network Security

Challenges and Potential Approaches in Wireless Sensor Network Security

Wireless sensor network security defense strategy based on Bayesian reputation evaluation model

AbstractIn order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

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.

Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

Retracted: Sensor Network Security Risk Prediction and Control Method Based on Big Data Analysis

Retracted: Sensor Network Security Risk Prediction and Control Method Based on Big Data Analysis

Location Privacy Protection and Coverage Hole Effects in Event Monitoring Wireless Networks for Internet of Things Applications

Source-location privacy (SLP) protection improves security in event monitoring wireless sensor networks (WSNs) for Internet of Things (IoT) applications. However, due to constrained energy resources, WSNs incur coverage holes that affect the level of SLP protection. Existing studies have ineffectively analysed the effects of coverage holes on SLP protection. Noting the limitation, this study investigated the coverage hole effects. Performance of various SLP routing protocols was evaluated based on the sensor node utilization ratio, attack success rate, end-to-end delay, and packet delivery ratio. Then, considering the challenge of limited battery power in IoT sensors, the performance gains and limitations of the protocols were identified. Simulation results demonstrate that the data dissemination SLP routing protocol (DIRP) outperforms other protocols. However, the performance of DIRP is significantly affected by coverage holes. The results suggest that for DIRP to be viable in IoT applications, the integration of distributed energy resources should be considered.

Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Secrecy of WSN Data Over Nakagami m Fading Channels with Selection Combining Diversity

We consider the security of wireless sensor network (WSN) data over Nakagami – m fading channels at the physical layer. A WSN in which the fusion center performs selection diversity has been considered for better quality reception. The links between the WSN node and fusion center are assumed to follow Nakagami-m fading distribution. Closed-form expressions for secrecy outage probability (SOP) are derived, and it is established that SOP analysis also leads to the analysis of the existence of secrecy as a special case of SOP. The analytical expressions have been validated through results from simulations. The analysis is valid for all positive real values of the fading parameter, m. The limits on the signal-to-noise ratio can be obtained to secure the transmitted data against eavesdropping with the required SOP and secrecy rate using the analysis presented in this paper.

A Comprehensive Analysis: Evaluating Security Characteristics of Xbee Devices against Zigbee Protocol.

In recent times, the security of sensor networks, especially in the field of IoT, has become a priority. This article focuses on the security features of the Zigbee protocol in Xbee devices developed by Digi International, specifically in the Xbee 3 (XB3-24) devices. Using the TI LaunchXL-CC26X2R1 kit, we intercepted and analyzed packets in real-time using the Wireshark application. The study encompasses various stages of network formation, packet transmission and analysis of security key usage, considering scenarios as follows: without security, distributed security mode and centralized security mode. Our findings highlight the differences in security features of Xbee devices compared to the Zigbee protocol, validating and invalidating methods of establishing security keys, vulnerabilities, strengths, and recommended security measures. We also discovered that security features of the Xbee 3 devices are built around a global link key preconfigured therefore constituting a vulnerability, making those devices suitable for man-in-the-middle and reply attacks. This work not only elucidates the complexities of Zigbee security in Xbee devices but also provides direction for future research for authentication methods using asymmetric encryption algorithms such as digital signature based on RSA and ECDSA.

Sinkhole detection and prediction using watermarking (SNDW)

Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique.

Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things

Wireless Nano-Sensor Networks (WNSNs) are molecular-level networks consisting of nano-machines that have very limited energy capacity. Due to the high energy consumption of the nodes in WNSNs during the data transmission, energy-efficient routing algorithms may help to reduce overall energy consumption. On the other hand, metaheuristics are useful for solving problems such as routing, energy, security and connectivity for traditional sensor networks. Therefore, this study proposes a hybrid Firefly and BAT Optimization based energy-efficient multi-hop routing algorithm, namely nanoFFBAT, between nano-sensor nodes and randomly placed convenient nano-routers for WNSNs-supported Internet of Nano-Things (IoNT) applications. In this hybrid approach, Firefly optimization is used for selecting the most energy-efficient neighbor by a nano-sensor node based on the flashing behavior of fireflies on the way for forming multi-hop paths and the BAT algorithm is adapted for building energy-efficient routing path discovery in WNSNs and also finding the optimal solution in solution space. In addition, nanoFFBAT detects redundant nodes on the energy-efficient multi-hop routing paths and shortens these paths if there exists any neighbor node already has been selected. The results of nanoFFBAT are compared with the shortest path from a nano-node to the convenient nano-router, a genetic algorithm-based energy-efficient routing algorithm for sensor networks and a WNSN routing protocol in the literature. According to the experimental simulation results, nanoFFBAT saves 306.929 nJ on a path on average and prolongs network lifetime 16.298 times more on average compared to the mentioned algorithms.

Providing an effective key management scheme to increase transaction security of homogeneous mobile wireless sensor networks

Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14% . 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks.

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

In the recent past, patient wearable devices and implantable biosensors revealed exponential growth in digital healthcare, because they have the capability to allow access to the information anywhere and every time to improve the life standard of multifarious disease effected patients followed by healthy people. Following these advantages, digital healthcare demands a secure wireless communication infrastructure for interconnected self-empowered biosensor devices to maintain the trust of patients, doctors, pharmacologists, nursing staff, and other associated stakeholders, etc. Several authentications, privacy, and data preservation schemes had been used in the literature to ensure the security of this emerging technology, but with time, these counteraction prototypes become vulnerable to new security threats, as the hackers work tirelessly to compromise them and steal the legitimate information of user’s or disrupt the operation of an employed self-empowered wireless sensor network (SWSN). To discuss the security problems of SWSN applications, in this review article, we have presented a detailed survey of the present literature from 2019 to 2022, to familiarize the readers with different security threats and their counteraction schemes. Following this, we will highlight the pros and cons of these countermeasure techniques in the context of SWSN security requirements to underscore their limitations. Thereafter, we will follow-up the underlined limitations to discuss the open security challenges of SWSN that need the concerned authorities’ attention. Based on this, we will pave a road map for the future research work that could be useful for every individual associated with this technology. For the novelty and uniqueness of this work, we will make comparative analysis with present survey papers published on this topic to answer the question of reviewers, readers, editors, and students that why this paper is in time and needed in the presence of rival papers.

Study of Anomaly Detection in IoT Sensors

Abstract: The rapid proliferation of Internet of Things (IoT) technology has resulted in an exponential increase in the number of connected devices and sensors. These sensors play a crucial role in collecting and transmitting data, enabling various applications and services in diverse domains. However, the large-scale deployment of IoT sensors also introduces new challenges, particularly in the realm of anomaly detection. This research paper presents a comprehensive study of anomaly detection techniques specifically designed for IoT sensors. We delve into the different types of anomalies that can occur in IoT sensor data, including sudden changes, outliers, and malicious attacks. Moreover, we explore the unique characteristics and requirements of IoT sensor networks, such as resource constraints, heterogeneous data, and dynamic network topologies. To address these challenges, we provide an overview of state-of-the-art anomaly detection methods tailored to IoT sensor networks. These methods encompass both traditional statistical approaches and machine learning algorithms, considering their applicability and effectiveness in the IoT context. We discuss the strengths and limitations of each technique, highlighting their suitability for different anomaly detection scenarios. Furthermore, we analyze and compare the performance of these methods using real-world IoT sensor datasets, evaluating their accuracy, efficiency, and scalability. The findings of our study shed light on the strengths and limitations of existing techniques, enabling researchers and practitioners to make informed decisions when choosing an appropriate anomaly detection method for their IoT sensor networks. By enhancing the reliability and security of IoT sensor networks, the outcomes of this research contribute to the advancement of IoT technology and its widespread adoption in various domains, including smart cities, healthcare, transportation, and industrial automation

Fuzzy enhanced location aware secure multicast routing protocol for balancing energy and security in wireless sensor network

Fuzzy enhanced location aware secure multicast routing protocol for balancing energy and security in wireless sensor network