Software Engineering Institute

After learning about malware related to cybersecurity, aspiring data scientists can:

• Gain knowledge of common problems that a data scientist encounters
• Become fluent in malware with the help of a scripting language
• Understand principles of investigating and analyzing properties of malware captured at run time
• Understand how to detect several suspicious behaviors
• Gain experience with hands-on feature engineering and building end-to-end data pipelines
• Gain experience with deep neural networks and train one to identify malicious processes
• Investigate and solve problems in the cybersecurity realm

Please note that successful completion of this course is a required component of the CERT Applied Data Science for Cybersecurity Professional Certificate. To learn more about the Professional Certificate and discounted package pricing, please go to: SEI Certificates.

• Those with a particular interest in data science and cybersecurity, but limited experience with both concepts.

After successful completion of this course, you will:

• be able to understand the fundamentals of analyzing properties of malware captured at run time
• explain and detect self-replication
• be able to recognize and determine ancestry relationships in suspicious requests
• explain the concept of suspicious requests and gather PID statistics
• explain the differences between suspicious and benign requests
• be able to understand the fundamentals of deep learning
• complete tasks involving generating feature vectors and creating a train-test split
• have an appreciation for deep neural networks
• complete tasks involving deep learning
• be able to train a deep neural network to identify malicious processes

In this course, students will learn about and investigate malware techniques relied upon in the cybersecurity realm. These include:

• fundamentals of malware
• self-replication
• behavior detectors and PID statistics
• suspicious requests and process ancestry
• fundamentals of neural networks and deep learning
• regularization within deep learning
• the bias-variance tradeoff
• training deep neural networks/identifying malicious processes

These concepts will be exercised in labs involving self-replication, suspicious requests & ancestry, count statistics and train/test split, and deep learning.

This course is presented in the form of video instruction presented by experts from the SEI CERT Division. Downloadable materials include course presentation slides, instructions for lab exercises, jupyter license, and instructions for using a jupyter notebook. Learners will also be able to access additional resources related to the subject matter.

Before registering for this course, participants must complete the Fundamentals of Statistics Applied to Cybersecurity course.

Learners should have some exposure to malware in itself and a working knowledge of a programming language (preferably Python or R). A working knowledge of calculus and linear algebra is helpful.

To access the SEI Learning Portal, your computer must have the following:

• For optimum viewing, we recommend using the following browsers: Microsoft Edge, Mozilla Firefox, Google Chrome, Safari
• These browsers are supported on the following operating systems: Microsoft Windows 8 (or higher), OSX (Last two major releases), Most Linux Distributions
• Mobile Operating Systems: iOS 9, Android 6.0
• Microsoft Edge, Firefox, Chrome and Safari follow a continuous release policy that makes difficult to fix a minimum version. For this reason, following the market recommendation we will support the last 2 major version of each of these browsers. Please note that as of January 2018, we do not support Safari on Windows.