Possible Option: Spring 2027
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Applied Machine Learning in Cybersecurity
This course explores artificial intelligence (AI) and machine learning (ML) techniques applied to cybersecurity. Students examine foundational AI/ML concepts, key algorithms, and real-world cyber threats such as malware, spam, and intrusion detection. The course covers supervised learning methods, including k-Nearest Neighbors, decision trees, ensemble models, neural networks, LSTM networks for anomaly detection and time-series analysis, and transfer learning. Ethical and privacy considerations in AI/ML are emphasized throughout. Hands-on projects using real-world datasets prepare students to design, evaluate, and optimize AI-driven cybersecurity solutions.
Course Objectives
- Evaluate ML’s Role in Mitigating Cyber-Attacks.
- Evaluate the ML Pipeline in Enhancing Cybersecurity Measures.
- Apply and Evaluate Classification Techniques for Spam Detection.
- Design and Implement ML Models for Malware Detection.
- Develop Intrusion Detection Systems Using Ensemble Learning.
- Understand and Apply Unsupervised Learning in Cybersecurity.
- Explore Neural Networks for Cybersecurity Applications.
- Implement Advanced Time Series Models for Cybersecurity.
- Develop and Evaluate Generative AI Models for Cybersecurity.