| 标 题： |
A machine learning approach improving university campus security
一种提升大学校园安全的机器学习方法 |
| 作 者： |
Vassil Guliashki
Emiliano Mankolli
Senada Bushati |
| 关键词： |
University campus security [大学校园安全]
Machine learning [ 机器学习]
Artificial intelligence [ 人工智能]
Cyber security [ 网络安全] |
| 出版年： |
2023 |
| 文集名： |
2023 IEEE International Workshop on Technologies for Defense and Security (TechDefense 2023)
2023年IEEE国际国防与安全技术研讨会（TechDefense 2023） |
| 会议名： |
2023 IEEE International Workshop on Technologies for Defense and Security (TechDefense 2023)
2023年IEEE国际国防与安全技术研讨会（TechDefense 2023） |
| 届 次： |
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| 摘 要： |
This paper aims to examine the existing vulnerabilities in the cybersecurity of a university campus and the approaches and ways to prevent losses caused by wrong decisions due to the analysis of corrupted data. The data analysis is usually performed using machine learning (ML) algorithms and artificial intelligence (AI). An approach to avoiding AI and ML vulnerabilities is presented in this paper. Ways to prevent wrong conclusions in data analysis and to avoid making wrong decisions are discussed. Another important aspect of improving security is ensuring a high level of cyber security and preventing (hacker) attacks, which can greatly endanger information processes and communication in the university and can also lead to the occurrence of material damage and human victims. Examples are provided to show how massive cyberattacks are in higher schools, based on data from universities in the United States of America. Several methods of attack prevention and detection are presented from the perspective of machine learning applications.
本文旨在分析大学校园网络安全中存在的漏洞，并探讨通过分析损坏数据所导致的错误决策而造成的损失的预防方法与途径。数据分析通常采用机器学习（ML）算法和人工智能（AI）技术。本文提出了一种规避AI和ML安全漏洞的方法，讨论了如何避免数据分析中的错误结论以及防止做出错误决策的措施。提升安全性的另一个重要方面是确保高水平的网络安全，防范黑客攻击。此类攻击可能严重威胁高校的信息处理与通信，还可能导致财产损失和人员伤亡。文章基于美国高校的数据，举例说明了高等教育机构面临的大型网络攻击情况。同时，从机器学习应用的角度，提出了多种攻击防御与检测方法。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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