The Data Labs have helped fund the Data Science of Cyber Security course. Overall the content will provide one of the most extensive coverage of Data Science within Cyber Security. It splits into four main units – and which can either be taken as units or cognitive subject areas. The aim of the course is to provide a foundation in key areas of Data Science within Cyber Security and thus support those who wish to further develop their career, or who aim to pursue it as a new career path.
The key learning outcomes are to:
- Define the key elements of network systems and the data evidence trails which can be used to understand the detection of cyber security threats.
- Understand how to use Data Science to investigate network and host log files, and how to search, aggregate, link, parse and join data sets, as well as providing successful results.
- Outline how cryptography can be used to protect data and add trustworthiness to transactions within a data infrastructure.
- Define the methods used within Machine Learning and how they can be applied to Cyber Security for the detection of a range of threats to improve incident response.
- Implement Data Science methods on real-life data sets which will operate in the detection and analysis of a range of Cyber Security threats.
- Use data visualisation tools and techniques to present security information, highlighting data anomalies and potential security threats to improve decision-making.
The presenters will be Prof Bill Buchanan and Dr Owen Lo.
The units and subjects are:
Unit 1: Fundamentals
- Subject 1: Cyber Intelligence.
- Subject 2: Defence Systems, Policies and Risks.
- Subject 3: Cryptography and Access Rights.
- Subject 4: Blockchain and distributed ledgers.
Unit 2: Fundamental for Cyber Security
- Subject 5: Memory, Big Data and SIEM.
- Subject 6: Network Forensics.
- Subject 7: Intrusion Detection Systems.
- Subject 8: Classification Metrics.
Unit 3: Threat analysis
- Subject 9: Big Data Analysis using Splunk.
- Subject 10: Insider Detection.
- Subject 11: Open Source Intelligence.
Unit 4: Learning, Searching and Matching in Cyber Security
- Subject 12: Introduction to Data Science.
- Subject 13: Similarity Matching and Searching.
- Subject 14: Machine Learning Methods.
We will be running free-of-charge initial taster workshops starting October and November 2019:
- Cryptography (Symmetric Key, Hashing and Key Exchange). This workshop will provide an outline the methods used in symmetric key encryption, key exchange and hashing. Date:16 October 2019 (2-5pm). Register your interest to attend here.
- Cryptography (Public Key and Signing). This workshop will provide an outline on the usage of methods such as RSA and Elliptic Curve Cryptography (ECC), and show how public key encryption can be used to protect and sign data. Date: 13 November 2021 (2-5pm). here.
- Open Source Intelligence. This workshop will investigate the key methods used in gathering intelligence information from open sources, and provide real-life analysis of a number of open source platforms including Twitter and Redit. Date: 27 November 2021 (2-5pm).
- Similarity Matching and Search. This workshop will outline the core methods used in matching entities, and how string searches can be performed using regular expressions and directed graphs.
All labs will be hands-on and highly practical in the scope, and will be using a mixture of Python, Node.js and Go for the workshops. More workshops will be planned after this, so watch out for more.
Registering your interest in the course
If you are interesting in attending any of these courses, please contact Bill ([email protected]) with the subject field of “Data Science of Cyber Security”. The full course will lead to an academic qualification.