Advanced Analytics: Malware
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
- 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.
This is an eLearning course.Register Now
Course Fees [USD]
- eLearning: $500.00
The course contains approximately 3 hours of instructor lecture and 2 hours of lab exercises related to the material presented within the course and demonstration/instruction for installing and using tools from SEI experts, supplemented by guided exercises and expert solutions.
Learners can proceed through the course at their convenience and can review and repeat course sessions as often as needed. Learners will have one year to complete the course. Upon completing all course elements, the learner is awarded an electronic certificate of course completion.
Advanced Analytics: Digital Forensics
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CERT Applied Data Science for Cybersecurity Certificate Examination
- Day Course
This examination provides an objective validation of conceptual knowledge and practical understanding of data analysis for cybersecurity from netflow, malware, and digital forensics activity, as presented in the required courses. The examination consists of 60 multiple choice questions. Students proceed through the examination at their convenience...Learn More
CERT Applied Data Science for Cybersecurity Certificate Package
Students who wish to purchase the certificate program package (four eLearning courses, certificate exam) will receive a discount from the total cost. CERT Applied Data Science for Cybersecurity Certificate Package consists of the following courses: Fundamentals of Statistics Applied to Cybersecurity Advanced Analytics: Netflow Advanced...Learn More
Fundamentals of Statistics Applied to Cybersecurity
Through the fundamentals of statistics related to cybersecurity, aspiring data scientists can: Gain knowledge of common problems that a data scientist encounters Become fluent in statistics with the help of a scripting language Increase predictive power and reduce risk within a model Better estimate parameters for a dataset Investigate and...Learn More
Training courses provided by the SEI are not academic courses for academic credit toward a degree. Any certificates provided are evidence of the completion of the courses and are not official academic credentials. For more information about SEI training courses, see Registration Terms and Conditions and Confidentiality of Course Records.