Dr. Kaiguo Yuan Profile

Dr. Kaiguo Yuan

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Proceedings Article | 16 October 2024 Paper

A recommendation algorithm based on adaptive privacy protection

Yiyang Song, Xiaoyong Li, Kaiguo Yuan, Weiyu Kong

Proceedings Volume 13291, 132913H (2024) https://doi.org/10.1117/12.3034017

KEYWORDS: Data privacy, Matrices

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Differential privacy, as a provable privacy protection framework, has been widely applied in recommendation systems. However, the integration of existing collaborative filtering algorithms with differential privacy typically encounters two issues. Firstly, algorithms often use the same privacy budget for data from different time periods, introducing unnecessary noise and reducing performance. Secondly, when using gradient descent to solve collaborative filtering models, the allocation of privacy budgets is relatively simple, usually evenly distributed across each iteration. This allocation requires a predefined number of iterations. If the number is too small, the algorithm may terminate before reaching the optimal solution; if too large, each budget per iteration may be too small, leading to excessive noise. To address these issues, an adaptive privacy budget recommendation algorithm is proposed. Firstly, a dynamic time window-based privacy budget calculation algorithm is utilized to provide varying levels of privacy protection for rating data from different time periods, thus avoiding unnecessary noise. Then, an adaptive privacy budget gradient descent strategy is employed to solve the collaborative filtering model, which dynamically allocates privacy budgets based on the noise gradient of each round. This algorithm is compared with existing differential privacy-based recommendation algorithms on the Movielens-1M dataset, demonstrating superior predictive accuracy and lower sensitivity to privacy protection.

Proceedings Article | 16 October 2024 Paper

PAUP: a pre-awareness insider threat detection method based on user psychoanalysis

Weiyu Kong, Xiaoyong Li, Kaiguo Yuan, Yiyang Song

Proceedings Volume 13291, 132914P (2024) https://doi.org/10.1117/12.3033591

KEYWORDS: Psychology, Decision trees, Data analysis, Modeling, Matrices, Fuzzy logic, Network security, Information security, Data modeling, Risk assessment

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Internal personnel within an organization often have privileged access to critical systems and sensitive information. They are familiar with the internal network structure, business processes, and security measures, which can lead to insider threats that are more insidious, long-term, destructive and diverse, posing a serious threat to enterprises and organizations. However, existing models for detecting insider threats primarily focus on modeling user behavior information but seldom take into account the valuable information from the psychological personality of insider personnel for threat detection. To address this limitation and better analyze the impact of user attributes on insider threats, a new direction for insider threat user analysis is proposed. This involves analyzing and visualizing the relationship between users' personalities and the execution of insider threat behaviors, using data analysis. Additionally, a decision tree model is constructed to realize insider threat detection based on user psychology, using feature_importance, a relative importance metric of features generated in the decision tree decision-making process, to judge the importance of different personality traits for insider threat detection. To further enhance the detection process, an insider threat user clustering method based on Fuzzy C-Means clustering is realized, and the groups are divided according to the user's psychological assessment scores to realize the early perceived localization of risky users. These approaches provide new ideas for finding new research directions in the field of insider threat.

Proceedings Article | 8 June 2024 Paper

DRLMS: a multipath scheduler based on deep reinforcement learning

Mengyang Zhang, Kaiguo Yuan, Xiaoyong Li

Proceedings Volume 13171, 131711L (2024) https://doi.org/10.1117/12.3031955

KEYWORDS: Neural networks, Education and training, Data transmission, Telecommunication networks, Design, Environmental sensing

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Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers.

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