ML-Powered Threat Hunting with Splunk & Jupyter Notebooks, Detection Engineering, Log Analysis & Behavioral Patterns
Sub Category
- Other IT & Software
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Objectives
- Explore the threat hunting lifecycle and how ML augments hypothesis-driven investigation.
- Analyze raw log data by cleaning, enriching, and visualizing it using Pandas, Seaborn, and Matplotlib in Jupyter.
- Apply anomaly detection techniques such as Isolation Forest and DBSCAN on telemetry data.
- Design and execute a complete ML-based hunt in Splunk and Jupyter to detect suspicious behavior.
Pre Requisites
- Learners should have basic knowledge of Python programming, be familiar with common log formats, and possess a foundational understanding of core cybersecurity concepts.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)