描述
网络安全问题与挑战。监督与无监督学习。分类与回归算法。网络安全领域的性能评估。不平衡领域的技术。异常检测。主动学习策略。数据流的网络安全。机器学习脆弱性。面向网络安全的自然语言处理。恶意软件、入侵、信用卡欺诈检测、网络钓鱼、身份验证滥用防护。
先修课程
- 先修课程：本课程无正式先修课程要求。但学生应对人工智能、机器学习有合理了解，并具备高阶语言的一些编程经验。
条件与方式
- 先修课程：本课程无正式先修课程要求。但学生应对人工智能、机器学习有合理了解，并具备高阶语言的一些编程经验。
原文参考文本
Cybersecurity problems and challenges. Supervised and unsupervised learning. Classification and regression algorithms. Performance assessment in cybersecurity domains. Techniques for imbalanced domains. Anomaly detection. Active learning strategies. Cybersecurity of data streams. ML vulnerability. NLP for cybersecurity. Malware, intrusion, credit card fraud detection, phishing, authentication abuse prevention.
- Prerequisite(s): There are no official prerequisites for this course. However, students should have reasonable exposure to Artificial Intelligence, Machine Learning, and some programming experience in a high-level language.
来源与参考
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来源参考 : https://calendar.carleton.ca/grad/courses/COMP/