Optimization of network security protection situation based on data clustering

Ye, Wei; Wang, Hongkai; Zhong, Yijun

doi:10.1007/s13198-021-01529-6

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Zhang et al [24] established a financial audit model based on artificial neural network (ANN) technology to solve the problem of complexity of contemporary economic activities caused by the increase of financial data. Ye et al [25] constructed a data clustering-based network security state distribution model in a complex network environment, and adopted a big data mining method to data mine network security information to achieve security posture assessment and precise evaluation. Son et al [26] proposed an innovative PV generation forecast method based on future cloud image prediction, for which a hybrid deep learning technique combining the generative adversarial network (GAN) and the long short-term memory (LSTM) model was used.…”

Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

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Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

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Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

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On Real-time Cooperative Trajectory Planning of Aerial-ground Systems

Huang,

Chen,

Zhang

et al. 2024

J Intell Robot Syst

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Cooperative trajectory planning of aerial-ground systems is a fundamental and challenging problem, which aims to leverage the aerial information to assist the ground tasks. Existing methods often suffer from suboptimal trajectories or computation burden. In this paper, we address cooperative trajectory planning of aerial-ground systems in which an unmanned ground vehicle (UGV) plans its local trajectory in real-time with the assistance of an unmanned aerial vehicle (UAV). Firstly, the UAV generates guidance trajectory using nonlinear model predictive control (NMPC), which considers the obstacle distribution density as a factor reflecting the coupling effect of multiple obstacles on the UGV, thereby avoiding local minima problem and improving the feasibility of the planned trajectory. Secondly, a null-space-based behavioral control (NSBC) framework is employed to merge the guidance trajectory into the UGV’s own planned one as a task. Finally, an event triggering task supervisor is developed for the UGV to decide the priorities of all tasks, which reduces the switching frequency of task priorities brought by traditional rule-based task supervisors. Both simulation and experiment results show that the proposed approach has superior trajectory planning performance in terms of trajectory error, on-line computation time and the success rate of task execution.

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