Mixed Strategy Analysis in Attack-Defense Game Model Based on 5G Heterogeneous Network of CPS Using ncPSO

Liu, Ning; Chai, Qing-Wei; Liu, Shangkun; Meng, Fanying; Zheng, Weimin

doi:10.1155/2022/1181398

Cited by 3 publications

(2 citation statements)

References 45 publications

(48 reference statements)

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“…For the breast tumor segmentation problem, we choose the Dice function as the supervision loss function, and the binary cross-entropy (BCE) function as the consistency loss function, and the specific calculations are shown in Eq (8) and Eq (9):…”

Section: Loss Functionmentioning

confidence: 99%

“…With the development of technology, artificial intelligence (AI) has been widely applied in various fields. [4]- [8] Deep learning is widely used in the field of image segmentation [9]. Convolutional Neural Network (CNN) has successfully achieved the extraction of image feature representation, and generates pixel prediction through fully connected layer to achieve segmentation [10].…”

Section: Introductionmentioning

confidence: 99%

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A Tumor Segmentation Method Based on Mean-Teacher Reusing Pseudo-Labels

Jiang,

Liu,

Wang

et al. 2024

IEEE Access

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Breast tumor is a common female physiological disease, and the malignant tumor is one of the main fatal diseases of women. Accurate examination and assessment of tumor shape can facilitate subsequent treatment and improve the cure rate. With the development of deep learning, automatic detection systems are designed to assist doctors in diagnosis. However, the blurry edges, poor visual quality, and irregular shapes of breast tumors pose significant challenges to design a highly efficient detection system. In addition, the lack of publicly available labeled data is a major obstacle in developing highly accurate and robust deep learning models for breast tumor detection. To overcome the aforementioned issues, we propose SRU-PMT+, a pseudo-label reusing Mean-Teacher architecture based on squeeze-and-excitation residual (SE-Res) attention. We utilize the proposed segmentation network, SRU-Net++, to generate pseudo-labels for unlabeled data, and guide the learning of the student model using the generated pseudo-labels and groundtruth, improving the accuracy and robustness of the model. Our proposed semi-supervised method has been rigorously evaluated on the available labeled dataset, i.e., Breast Ultrasound Images (BUSI) dataset. Results show that our proposed method outperforms current segmentation methods and has good performance. Importantly, our strategy of reusing pseudo-labels improves the performance of breast tumor segmentation.

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Section: Loss Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Tumor Segmentation Method Based on Mean-Teacher Reusing Pseudo-Labels

Jiang,

Liu,

Wang

et al. 2024

IEEE Access

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PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

Liu,

Zheng

2024

Complex Intell. Syst.

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The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

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