Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

Yasin, Adwan; Sabaneh, Kefaya

doi:10.14569/ijacsa.2016.070932

Cited by 6 publications

(6 citation statements)

References 13 publications

(18 reference statements)

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“…WSNs provide special challenges for security protocol designers due to the following unique characteristics [6,7,8,15,19,21,22,23,26,36,37,39,40,41]:…”

Section: Challenges Of Security Protocols In Wsnsmentioning

confidence: 99%

A Review of Security Challenges and Solutions in Wireless Sensor Networks

Hassan,

Madkour,

Nouh

2023

Journal of Al-Azhar University Engineering Sector

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Wireless Sensor Networks (WSNs) are a vital component of the Internet of Things (IoT) since it is a viable technology for a variety of real-time applications. This is mainly because WSNs are naturally distributed, low-cost, fault-tolerant, self-organizing, easily deployable, scalable, and function without a predefined infrastructure. Their applications include surveillance, smart power grids, traffic networks, telecommunications systems, smart cities, intelligent transportation, industrial quality control, and the military, to mention a few. However, the security of WSNs is a major concern due to their vulnerability to various types of attacks, including node compromise, eavesdropping, and denial-of-service. This paper covers the security issues in WSNs and discusses the security challenges faced by these networks and the existing solutions. It illustrates the possible attacks and explores the different security mechanisms that can be used to protect WSNs, including encryption, authentication, and key management. Trust and reputation management are presented being effective techniques for mitigating routing attacks, and the role of machine learning techniques in enhancing WSN security is outlined. The paper concludes by discussing future research directions in WSN security. The findings of this paper are valuable for researchers, network administrators, and policymakers who are involved in the design and implementation of secure WSNs.

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“…WSNs provide special challenges for security protocol designers due to the following unique characteristics [6,7,8,15,19,21,22,23,26,36,37,39,40,41]:…”

Section: Challenges Of Security Protocols In Wsnsmentioning

confidence: 99%

A Review of Security Challenges and Solutions in Wireless Sensor Networks

Hassan,

Madkour,

Nouh

2023

Journal of Al-Azhar University Engineering Sector

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“…Rathore et al [ 13 ] used biological inspirations and ML to identify fraudulent nodes. An enhanced trust model based on the radial base artificial neural network (RBANN) was presented by Yasin et al [ 14 ] to predict each node’s future behavior and detect malicious nodes. Yoon et al [ 15 ] provided a DL-based approach to verify the trustworthiness of sensors by considering the sensor data only.…”

Section: Related Workmentioning

confidence: 99%

Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication

Yin

Qiu

et al. 2021

Sensors

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When a wireless sensor node’s wireless communication fails after being deployed in an inaccessible area, the lost node cannot be repaired through a debugging interaction that relies on that communication. Visible light communication (VLC) as a supplement of radio wave communication can improve the transmission security at the physical layer due to its unidirectional propagation characteristic. Therefore, we implemented a VLC-based hybrid communication debugging system (HCDS) based on VLC using smartphone and sensor node. For the system’s downlink, the smartphone is taken as the VLC gateway and sends the debugging codes to the sensor node by the flashlight. To improve the transmission efficiency of the downlink, we also propose a new coding method for source coding and channel coding, respectively. For the source coding, we analyze the binary instructions and compress the operands using bitmask techniques. The average compression rate of the binary structure reaches 84.11%. For the channel coding, we optimize dual-header pulse interval (DH-PIM) and propose overlapped DH-PIM (ODH-PIM) by introducing a flashlight half-on state. The flashlight half-on state can improve the representation capability of individual symbols. For the uplink of HCDS, we use the onboard LED of the sensor node to transmit feedback debugging information to the smartphone. At the same time, we design a novel encoding format of DH-PIM to optimize uplink transmission. Experimental results show that the optimized uplink transmission time and BER are reduced by 10.71% and 22%, compared with the original DH-PIM.

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“…This makes the evaluation of the RCF both intelligent and accurate. ANNs are a good choice for WSNs because of their efficiency, robustness, parallelism, and noise tolerance, which are important in these kinds of environments [12,37].…”

Section: Introductionmentioning

confidence: 99%

A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks

Hassan

Madkour

Nouh

2023

JCSANDM

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Wireless sensor networks (WSNs) are vulnerable to security attacks due to the unbounded nature of the wireless medium, restricted node resources, and cooperative routing. Standard cryptography and authentication mechanisms help protect against external attacks, but a compromised node can easily bypass them. This work aims to protect WSNs against internal attacks, which are mostly launched from compromised nodes to disrupt the network’s operation and/or reduce its performance. The trust and reputation management framework provides a routing cost function for selecting the best secure next hop. Tuning the trust weights is essential to cope with the constant changes in the network environment, such as the sensor nodes’ behaviours and locations. To allow real-time operation, the proposed framework introduces an artificial neural network (ANN) in each sensor node that automatically adjusts the weights of the considered trust metrics according to the WSN state. A large dataset is generated to train and test the ANN using a multitude of simulated cases. A prototype is developed and tested using the J-Sim simulator to show the performance gain resulting from applying the adaptive trust model. The experimental results showed that the adaptive model has robust performance and has achieved an improved packet delivery ratio with reduced power consumption and reduced average packet loss. The results showed that when sensor nodes were static and malicious nodes were present, the average accuracy was 99.6%, while when they were in motion, it was 88.1%.

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Enhancing Wireless Sensor Network Security using Artificial Neural Network based Trust Model

Cited by 6 publications

References 13 publications

A Review of Security Challenges and Solutions in Wireless Sensor Networks

A Review of Security Challenges and Solutions in Wireless Sensor Networks

Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication

A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks

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