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Corresponding Author

Abeer Hussein Abdulrasool

Document Type

Article

Keywords

Wireless Sensors Networks (WSNs), Deep learning technique, Long Short Term Memory (LSTM) algorithm, Reliability analysis, Life time, Energy consumption

Abstract

With the advancement of software, wireless sensor networks (WSNs) face many problems and challenges, such as loss or limitation of node power resources, as well as intrusions and other cyberattacks. This study aims to improve the reliability of WSNs to mitigate the impact of cyber-attacks by applying effective detection and prevention techniques to ensure their continuous operation, as well as to maintain the security, privacy, and integrity of information. Several efficient approaches and smart techniques have been presented in literature to enhance the reliability and security of WSNs against various cyber-attacks. Recent studies focused on the deep learning (DL) algorithms as efficient and robust solutions in detecting and eliminating the threats up on WSNs. In this study, a deep long short tern memory (LSTM) algorithm that utilizes both short-term and long-term memory and performs well with long-term dependencies was presented to handle intrusions in the sensor network. The model uses LSTM networks to manage these variances and provide security and reliability because mobile attacks might have unpredictable patterns. Every time the regulated parameters are iterated, each node communicates its position and degree of movement, along with other significant information. Important metrics included the ratio of active to dormant nodes, data received per node, communication time, energy consumption per iteration, and communication time between nodes. These measures help assess how stable the model is in different scenarios. Such measurements allow us to predict future node behaviors and the network's highly reliable operation in addition to focusing on stability and security. The proposed LSTM deep learning attack defense model has been applied to improve the reliability of the sensor network by 30%, extend the lifetime of active nodes by 22%, and reduce node energy consumption in the WSN by 55%.

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