Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

Lu, Xue; Han, Dong‐Guk; Duan, Letian; Tian, Qiuting

doi:10.1504/ijcse.2020.10028357

Cited by 3 publications

(1 citation statement)

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“…The proposed IDS for hierarchical WSN are developed after training with many experimental samples. Experiments demonstrate that utilizing the NSL-KDD data set for simulation increases the precision rate of the two classifications to 85.4% and the accuracy rate of the five categories to 80.5% [24].…”

Section: Literature Surveymentioning

confidence: 99%

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

2023

Preprint

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Wireless sensor networks (WSN) are rapidly growing in popularity, and their flexibility and ease of implementation cause an increasing number of security issues, making it critical to research network intrusion defense for wireless sensor networks. Denial of service (DoS) is a cyber-attack that shuts down the targeted network. A DoS attack on a WSN device will be fatal. It is prone to malicious attacks and difficult to prevent because every single node is independent of the others, yet there's no central or monitoring node, which is tough to avoid. Numerous lightweight authentication systems have been utilized in real-time to ensure encrypted communication. However, with the lack of synchronization between nodes during data routing, WSNs are highly prone to Denial of Service (DoS) attacks. This article combines the Adaptive Sunflower Optimization (ASFO) method with an improved Deep Convolutional Neural Network (IDCNN) to enhance the degree of security against DoS attacks on WSNs term ASFO-IDCNN. This paper initially utilizes the ASFO method to improve the initial values of IDCNN to prevent getting into the local optimum. Then, the ASFO-IDCNN technique is used to detect intrusions in WSNs. The results of numerous simulated situations are shown, and the associated data is compared. DoS protection research is precious in analyzing the anti-attack efficiency of WSN nodes. The influence of DoS attacks on the functioning of WSNs is considered in this research.

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Section: Literature Surveymentioning

confidence: 99%

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

2023

Preprint

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

Ghadi,

Mazhar,

Shloul

et al. 2024

IEEE Access

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Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. This article focuses on machine learning algorithms used to solve the security issues of wireless sensor networks. This article also focuses on different types of attacks on layers of the wireless sensor network. Moreover, this study addresses several unsolved issues, including adapting machine learning algorithms to accommodate the sensors' functionalities in this network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

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Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm

Al-Rawashdeh,

Aljughaiman,

Albuali

et al. 2024

IEEE Access

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Intrusion detection of wireless sensor networks based on IPSO algorithm and BP neural network

Cited by 3 publications

References 0 publications

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

Effective detection of DoS attack in wireless sensor network using ASFO algorithm and Improved Deep Convolutional Neural Network

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

Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm

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