A new pioneering course that blends the domains of cyber security and artificial intelligence (AI). For existing cyber security professionals who want to understand the impact of AI and also for AI professionals who are interested in cyber security.
The Artificial Intelligence for Cyber security course is a three-day course for cyber security professionals who want to understand AI and AI professionals who want to work with cyber security.
Where coding is needed, Python will be used. Participants are expected to be familiar with coding but not to master any specific language. The hands-on sessions will include a demonstration of code but participants would not need to code themselves.
The structure of the course is as follows:
Fundamentals of Cyber Security
In this module, we cover some fundamental concepts, properties, and mechanisms in security such as:
- Identity, authentication, confidentiality, privacy, anonymity, availability and integrity
- Exploring cryptographic algorithms together with major attacks (using a break-understand-and-fix approach)
- Exploring high-level security protocols (passwords, graphical passwords, key distribution and authentication protocols) together with some rigorous mechanisms for reasoning about their correctness (e.g. belief logics). Other mechanisms such as biometric authentication are also covered
- Compliance and security assessment: this section focuses on security assessment carried out in an organisation including Red Team assessment, penetration testing, Active Directory Security Assessment (ASDA) and cyber insurance risk assessment
Fundamentals of AI for Security
Here, we cover deep learning fundamentals from a security perspective. We cover the fundamentals of AI and how AI can solve problems in the cyber security space. Examples of companies used as examples here include Cylance and FireEye.
In this module, we address the challenges of how AI helps create the secure web. Examples of themes covered include: making websites secure using AI techniques for injection using regular expressions and identifying patterns and matching with existing scores (a higher the score is an indicator of vulnerability. Examples of companies covered include FireEye and Akamai. In this module, we use statistical patterns and Bayesian statistics.
Deep learning applications
In the machine learning applications module, we aim to detect patterns and model behaviour and identify anomalous behaviour. AI Technologies include: statistical patterns, Bayesian statistics, statistical distributions and natural language processing. Companies covered include Darktrace and Cylance.