Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

Yao, Chengpeng; Yang, Yuanping; Yin, Kun; Yang, Jiaji

doi:10.1109/access.2022.3210189

Cited by 21 publications

(5 citation statements)

References 46 publications

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“…The suggested model's classification results were evaluated using ROC curves, several evaluation parameters, and a confusion matrix to confirm validity. Regarding the classification effect, the suggested method has a reduced size and outperforms other traditional anomalous traffic detection models in experimental comparison [17].…”

Section: Literature Surveymentioning

confidence: 97%

“…Chengpeng Yao et al (2022) suggested design offers a compact framework along with improved feature extraction capabilities, allowing it to detect unusual network traffic on WSN systems with restricted storage space. The suggested model's classification results were evaluated using ROC curves, several evaluation parameters, and a confusion matrix to confirm validity.…”

Section: Literature Surveymentioning

confidence: 99%

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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: 97%

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

View full text Add to dashboard Cite

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“…Yao et al (27) the proposed study focuses on WSNs' susceptibility to assaults and their devices' limited storage capacity. To resolve this problem, the researchers suggest a new approach that associates PCA (Principle Component Analysis) with a DCNN for the purpose of detecting DoS traffic anomalies in WSNs.…”

Section: Related Workmentioning

confidence: 99%

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

Sedhuramalingam,

Saravana Kumar

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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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.

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“…At last, robust IDS using LSTM is implemented on the CHs. Yao et al [15] suggest a technique based on DCNN and principal component analysis (PCA) for traffic anomaly detection of DoS in WSN, depending on the WSN vulnerability to attacks and the restricted memory capacity of their devices. The presented algorithm has a more effective capability of feature extraction and lightweight structure that could successfully identify network abnormal traffic in WSN with restricted memory capacity.…”

Section: Related Workmentioning

confidence: 99%

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

Alruwaili,

Asiri,

Alrayes

et al. 2023

IEEE Access

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The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying and responding to great security attacks and threats that can cooperate with the integrity, availability, and privacy of the network and its data. Machine learning (ML) algorithms are deployed for detecting difficult patterns and subtle anomalies in IoT data. Artificial intelligence (AI) driven methods are learned and adapted from novel data for improving detection accuracy over time. In this article, we introduce a Red Kite Optimization Algorithm with an Average Ensemble Model for Intrusion Detection (RKOA-AEID) technique for Secure IoT-based WSN. The purpose of the RKOA-AEID methodology is to accomplish security solutions for IoT-assisted WSNs. To accomplish this, the RKOA-AEID technique performs pre-processing to scale the input data using min-max normalization. In addition, the RKOA-AEID technique performs an RKOA-based feature selection approach to elect an optimum set of features. For intrusion detection, an average ensemble learning model is used. Finally, the Lévy-fight chaotic whale optimization Algorithm (LCWOA) can be executed for the optimum hyperparameter chosen for the ensemble models. The performance evaluation of the RKOA-AEID algorithm can be tested on the benchmark WSN-DS dataset. The extensive experimental outcomes stated the higher outcome of the RKOA-AEID algorithm with other approaches with an improved accuracy of 98.94%.

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Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

Cited by 21 publications

References 46 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

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

Red Kite Optimization Algorithm With Average Ensemble Model for Intrusion Detection for Secure IoT

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