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Syllabus Information


Fall 2021
Sep 28, 2022
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Information Use this page to maintain syllabus information, learning objectives, required materials, and technical requirements for the course.

Syllabus Information
CS 52900 - Security Analytics
Associated Term: Fall 2021
Learning Outcomes: 1. Explain commonly used machine learning algorithms relevant to information security; identity their strengths and weaknesses, and illustrate their relevance through examples. 2. Identify security problems that can be solved by using machine learning (including deep learning) techniques. 3. Explain the concepts of artificial neural networks, including feed forward networks, convolutional neural networks, and recurrent neural networks. 4. Deploy machine learning algorithms (including artificial neural networks) using softwares such as NumPy, SciPy, and TensorFlow. 5. Apply Spark and HDFS to perform data analysis. 6. Apply machine learning algorithms to security problems. 7. Assess the effectiveness of applying data analytics techniques to different security problems and explain existing shortcomings of ML techniques. 8. Explain what type of data visualization can be effective for security problems (e.g., in fraud detection). 9. Explain the concept of adversarial machine learning and the common attacks/defenses.
Required Materials:
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