Cyber&Data

[Cyber&Data Home][Home]

This is the home page for the Cyber & Data course. There are four main units involved. Units 1 and 2 are self-study, but you can register for Units 3 and 4, and study towards an MSc module. Some unique features include: On-line Python integration, On-line Intrusion Detection Editor, Splunk Data Analysis, and Machine Learning Environment for Splunk. The Splunk elements of the course require a login (contact [email protected] for a login).

Unit 1: Fundamentals

1. Cyber and Intelligence. This topic provides an introduction to Cyber and Intelligence.
2. Defence Systems, Policies and Risk. This topic provides an overview of the some of the core defence mechanisms and risk approaches.
3. Open Source Intelligence. This topic outlines the usage of open source intelligence including using Shodan, Redit and Twitter mining.
4. So What is Data? This topics outlines the core data formats using in cybersecurity, and introduces the concept of magic numbers within files.

Unit 2: Data Capture and Analysis

5. Memory, Big Data and SIEM. This topic outlines some of the core principles used around Big Data methods and in the integration of SIEM (Security Information and Event Management).
6. Network Forensics. This topic provide fundamental knowledge in how networks works, and how we can analyse different network protocols, including ARP, IP, TCP, FTP and HTTP.
7. Intrusion Detection Systems. This topic outlines the usage of IDS and applies with using Snort.

Unit 3: Data Science and Cyber

8. Classification Metrics and ML.
9. Introduction to Data Science.
10. NumPy and Pandas. An outline of the integration of Numpy and Pandas for datasets.
11. Similarity and Matching. This unit outlines some of the key methods used within similarity and matching of text data. This includes the application of regular expressions, similarity metrics and similarity hashes.

Unit 4: Data Applications and Machine Learning

12. Splunk. An introduction to Splunk.
13. Splunk (Charting and Analysis). An introduction to Splunk with Charting and Analysis.
14. Machine Learning (Core Methods). An outline of the core methods.
15. Application of Machine Learning. A tutorial in applying ML with Splunk

Unit 5: Cyber Applications

16. Face Recognition. Implementation of face recognition.