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Detailed Course Information

 

Fall 2019
Aug 25, 2019
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CS 52900 - Security Analytics
Credit Hours: 3.00. This course focuses on applied data mining, machine learning, data analytics techniques, and their application and relevance in information security. The course covers basic concepts of data mining and machine learning, computation platforms in support of big data analytics including Map-Reduce and Spark, machine learning algorithms such as classification trees, logistic regression, naive Bayes, k Nearest Neighbors, Support Vector Machines, Artificial Neural Networks (including Feed Forward, Convolutional, and Recurrence), the application of these algorithms to security tasks such as Spam/Phishing detection, malware detection, intrusion detection, and situational awareness. The future and potential role of applying machine learning techniques in information and data security is explored. Typically offered Fall.
3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Lecture

Offered By: College of Science
Department: Computer Science

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette

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.


Restrictions:
Must be enrolled in one of the following Programs:     
      Computer Science-PHD
      Computer Science-MS

Prerequisites:
GR CS 52900 Requisites

General Requirements:

Student Attribute: GR
May not be taken concurrently.  )
and
Course or Test: CS 52600
Minimum Grade of C
May not be taken concurrently. )


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