WEDMS: An advanced mean shift clustering algorithm for LDoS attacks detection

Tang, Dan; Man, Jianping; Liu, Tang; Feng, Yi; Yang, Qiuwei

doi:10.1016/j.adhoc.2020.102145

Cited by 34 publications

(7 citation statements)

References 17 publications

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“…Commonly, data sets can be reasonably classified by cluster analysis according to the degree of closeness. 46…”

Section: The Proposed Mwkpca Fd Methodsmentioning

confidence: 99%

A data-based fault detection scheme for the stratospheric airship control system

Hu,

Zhu,

Zheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This brief proposed an innovative fault detection method based on analytical data for the stratospheric airship control system. The control system considered is subject to both space disturbance and nonlinear characteristics; the faults of sensors and actuators are all taken into account. The proposed method is developed in two phases. In the first phase, the moving window kernel principal component analysis is employed to construct the fault detection model with the training data under normal operating conditions and update the fault detection model online until abnormal data are detected. Second, a fault detection model updating mechanism is designed to reduce computational complexity and cost with a clustering algorithm, which compounds the mean shift clustering with weighted Euclidean distance to reflect the data density distribution feature to make the updating to be adaptive. Finally, the proposed method is applied to detect fault for an illustrative simulation stratospheric airship control model. The fault detection results validate the effectiveness of proposed fault detection method for different sensor and actuator fault cases. Comparing to some extended moving window kernel principal component analysis method, the proposed method reduces the computational cost significantly.

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“…Commonly, data sets can be reasonably classified by cluster analysis according to the degree of closeness. 46…”

Section: The Proposed Mwkpca Fd Methodsmentioning

confidence: 99%

A data-based fault detection scheme for the stratospheric airship control system

Hu,

Zhu,

Zheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Expanding on this approach, they also advanced a LDoS attack detection scheme utilizing a Mean Shift clustering algorithm with a weighted Euclidean distance (WEDMS). The weighting factor was determined by the significance of the features [ 18 ]. Nevertheless, it required more intricate computations and more extensive model training.…”

Section: Related Workmentioning

confidence: 99%

“…Cutting-edge feature-based detection methods require significant computational resources and time for feature selection and model training, while time–frequency domain detection methods suffer from the detection of features in a small scale [ 17 , 18 , 19 ]. To fill the gap in detecting LDoS attacks using HHT-based spectral features, we propose the HCN method, which combines the Hilbert–Huang Transform and Convolutional Neural Network (CNN) methods to detect LDoS traffic.…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Liu

Sun

Zhang

et al. 2023

Sensors

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Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

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“…This research project describes a novel approach to cluster ensembles predicated on spectral clustering. This function is the first step in the CE algorithm, and it is possible to improve the outcomes of individual clustering algorithms because it is the major step in the algorithm [30] [31]. The final consensus partition is found, which is the result of any CE technique that has been used.…”

Section: Consensus Functionmentioning

confidence: 99%

Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

Hicham¹,

Karim²

2022

IJACSA

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Customer segmentation is key to a corporate decision support system. It is an important marketing technique that can target specific client categories. We create a novel consumer segmentation technique based on a clustering ensemble; in this stage, we ensemble four fundamental clustering models: DBSCAN, K-means, Mini Batch K-means, and Mean Shift, to deliver a consistent and high-quality conclusion. Then, we use spectral clustering to integrate numerous clustering findings and increase clustering quality. The new technique is more flexible with client data. Feature engineering cleans, processes, and transforms raw data into features. These traits are then used to form clusters. Adjust Rand Index (ARI), Normalized Mutual Information (NMI), Dunn's Index (DI), and Silhouette Coefficient (SC) were utilized to evaluate our model's performances with individual clustering approaches. The experimental analysis found that our model has the best ARI (70.14%), NMI (71.75), DI (75.15), and SC (72.89%). After retaining these results, we applied our model to an actual dataset obtained from Moroccan citizens via social networks and email boxes between 03/06/2022 and 19/08/2022.

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WEDMS: An advanced mean shift clustering algorithm for LDoS attacks detection

Cited by 34 publications

References 17 publications

A data-based fault detection scheme for the stratospheric airship control system

A data-based fault detection scheme for the stratospheric airship control system

A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks

Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

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