The WSN intrusion detection method based on deep data mining

Zhang, Yiwei

doi:10.1080/23742917.2022.2162195

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Input: Imbalanced train set S, scaling factor K, instance hardness threshold IH′, and sample threshold UB Output: New train set S N (1) Step1: Distinguish between easy sets and difcult sets for each sample∈ S do (2) Compute its K nearest neighbors and IH if IH > IH′ then (3) Put the samples into the difcult set (4) end (5) end (6) Difcult set S D and easy set S E � S − S D (7) Step2: Compress the majority samples in the difcult set by the cluster centroid (8) Take all the majority samples from S D and set it as S Maj (9) Use the K-means algorithm with K cluster (10) Use the coordinates of K cluster centroids and replace the majority samples in S Maj (11) Compressed the majority sample set S Maj (12) Step3: Sample the minority samples in the difcult set using the SMOTE algorithm (13) Take all the majority samples from S D and set it as S Min (14) for each sample ∈ S Min do (15) Using SMOTE sampling, the sampling threshold is set to UB (16) Putting new samples into S Z (17) end (18) Step : Merge sample sets (19) Precision is the ratio of the number of samples with positive real values to the number of samples predicted to be positive, which can represent the ability of the model to predict positive samples as follows:…”

Section: Evaluation Metrics and Baseline Methodsmentioning

confidence: 99%

“…Te experimental results show that the accuracy of the model has improved signifcantly. Ling et al [16] proposed a multiclassifer ensemble algorithm based on probability weighted voting to improve model accuracy. Xu et al [17] proposed a weighted majority algorithm based on the random forest to improve the performance of the random forest, and the model is trained on nontrafc datasets, so it has the ability to detect unknown trafc types.…”

Section: Improved Machine Learning Algorithms For Networkmentioning

confidence: 99%

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Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

Qin,

Han,

Wang

et al. 2023

Security and Communication Networks

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With the increase in cyber threats in recent years, there have been more forms of demand for network security protection measures. Network traffic classification technology is used to adapt to the dynamic threat environment. However, network traffic has a natural unbalanced class distribution problem, and the single model leads to the low accuracy and high false-positive rate of the traditional detection model. Given the above two problems, this paper proposes a new dataset balancing method named SD sampling based on the SMOTE algorithm. Different from the SMOTE algorithm, this method divides the sample into two types that are easy and difficult to classify and only balances the difficult-to-classify sample, which not only overcomes the SMOTE’s overgeneralization but also combines the idea of oversampling and undersampling. In addition, a two-layer structure combined with XGBoost and the random forest is proposed for multiclassification of anomalous traffic, since using a hierarchical structure can better classify minority abnormal traffic. This paper conducts experiments on the CICIDS2017 dataset. The results show that the classification accuracy of the proposed model is more than 99.70% and that the false-positive rate is less than 0.34%, indicating that the proposed model is better than traditional models.

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Section: Evaluation Metrics and Baseline Methodsmentioning

confidence: 99%

Section: Improved Machine Learning Algorithms For Networkmentioning

confidence: 99%

Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

Qin,

Han,

Wang

et al. 2023

Security and Communication Networks

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show abstract

“…Additionally, it employs a dynamic tree growth strategy to reduce model complexity, thereby accelerating the training speed of the model. Dong and colleagues [32] presented a distributed intrusion detection model that utilizes both DF and Random Forest classifiers. The designed model exhibits a lightweight Random Forest classifier deployment on sensors and cluster head nodes, while the DF model is deployed at the base station.…”

Section: Df-based Intrusion Detection Algorithmsmentioning

confidence: 99%

Resource-Constraint Deep Forest Based Intrusion Detection Method in Internet of Things for Consumer Electronic

Chen,

Wang,

Yang

et al. 2024

IEEE Trans. Consumer Electron.

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Consumer Electronics (CEs) are smart devices using IoT for connectivity. They're susceptible to attacks like DoS, DDoS, and web attacks, impairing functions and enabling remote hijacking. Attackers can exploit CEs to target other systems, like vehicles. Malicious code can propagate through networks or CEs, causing vehicle failures. Existing ML/DL based IDS have high classification accuracy and robustness in traditional internet environments, but they are overly complex for performance improvement, which hinders their deployment in edge small computing environments. Furthermore, the comparison experiments of these intrusion detection algorithms with other algorithms are not sufficiently comprehensive to evaluate their performance in small computing environments. Therefore, balancing "detection performance and resource consumption" is a key issue in CE network detection. To address this issue, this paper proposes a hybrid feature selection model based on chi-square test and information gain combined Ig-Chi, which effectively reduces the feature dimensionality and improves the classification accuracy of classifiers for high-dimensional data sets. Additionally, layered intrusion detection is employed to perform intrusion detection on the data after feature selection. The experiments on four public data sets demonstrate that this method surpasses six ML/DL algorithms in terms of accuracy and resource indicators.

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“…Wireless Sensor Network (WSN) [1,2] is a type of large-scale ad-hoc network that has small devices, sensors, and modern computing components. This network is properly monitored and controlled with the use of sensors, many wireless nodes, and low power computing devices.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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The WSN intrusion detection method based on deep data mining

Cited by 6 publications

References 21 publications

Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

Resource-Constraint Deep Forest Based Intrusion Detection Method in Internet of Things for Consumer Electronic

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

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