Dr. Bozhong Liu Profile

Dr. Bozhong Liu

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Proceedings Article | 26 June 2023 Paper

An automatic QoS-aware resource partitioning framework for cloud environment

Bingwei Chen, Jianquan Li, Bozhong Liu, Zhibin Yu

Proceedings Volume 12721, 127210W (2023) https://doi.org/10.1117/12.2683456

KEYWORDS: Computer hardware, Cloud computing, Machine learning, Design and modelling, Mathematical optimization

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The increasing use of computers in various fields has led to the popularization of cloud computing due to its advantages, such as large scale, high performance, low power consumption, high reliability, and low cost. However, the rapid growth of business volume and kinds of computer hardware resources, along with complex computer architecture, poses challenges in allocating hardware resources. Traditional methods struggle to meet the varying requirements of different businesses, hinder automatic tuning, and limit the improvement of overall system performance. In this regard, we propose an automatic QoS-aware resource partitioning framework that aims to maximize the overall performance of the system. Our contributions include an automatic performance tuning framework for cloud environments, a Deep Q-learning Network (DQN) based performance optimization method, and achieving a speed-up of at most 1.73 times compared to uniform partitions for CPU overload scenarios involving throughput-aware and latency-critical workloads. The proposed framework addresses the challenges faced by cloud computing by adopting different allocation methods for hardware resources that maximize performance in different application scenarios. The framework efficiently utilizes emerging hardware resource control capabilities to improve hardware and system performance. The use of DQN in performance optimization allows the framework to learn from past experiences and adapt to different situations, resulting in better resource allocation decisions. The proposed framework can significantly reduce the waste of resources and unnecessary expenditures for governments and enterprises. Our framework can serve as a guide for future research in cloud computing and related fields.

Proceedings Article | 26 May 2023 Paper

Tracking the prevalence of compromised passwords using long-term honeypot data

Jianzhou You, Bozhong Liu, Yang Wang, Laiyoumei Jiang

Proceedings Volume 12700, 127000K (2023) https://doi.org/10.1117/12.2682267

KEYWORDS: Computer security, Cyberattacks, Network security, Clouds, Analytical research, Statistical analysis, Security technologies, Internet

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Passwords are critical issues in the world of cyber security. Unfortunately, despite best efforts, passwords continue to be compromised and leaked onto the Internet, leading to an alarming number of compromised passwords in circulation. In this study, we compare honeypot-captured data from 2021 and 2023 to measure the prevalence of compromised passwords in real-world cyberattacks. Specially, we designed and deployed an online SSH honeypot on the cloud server to capture the latest cyber intelligence in the wild. Our findings show that over 90% of brute force attacks involve the use of compromised passwords, indicating a high level of password vulnerability. Additionally, we observe that the effectiveness of strong-password policies in mitigating such attacks appears limited. This study highlights the need for better password security strategies to counter the high prevalence of compromised passwords in cyberattacks.

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