Artificial Intelligence for Cyber Security


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.

Programme details

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:

Day One

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.

Secure Web

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.

Day Two

Cyber Security Threats and Development of Secure Software

Web Application Security

This section focuses on security around web applications covering themes like:

  • Injection
  • Broken authentication
  • Sensitive data exposure
  • XML External Entities (XXE)
  • Broken access control
  • Security misconfiguration
  • Cross-Site Scripting (XSS)
  • Insecure deserialization
  • Using components with known vulnerabilities
  • Insufficient logging and monitoring

Securing IOT Infrastructure

This section will explore security issues in systems where computation is carried out to sense, analyse, and control physical system elements. These systems typically react in real time to external events.

  • Autonomous vehicles and traffic management systems, to power distribution systems

  • Automated manufacturing systems
  • Robotic applications and web enabled toys

Many of these will soon operate as part of the "Internet of things". A breach in the security of the systems of interest could also have catastrophic safety consequences. Complications arise when intrusions are detected, e.g. closing down a system may simply not be possible. Companies referred to here include Darktrace, Nvidia and Microsoft.

Secure AI Development

This section covers the security analysis as well as the secure development of software-based systems both on architectural level and system level.

The main goal of this section is to teach the foundations of secure software design, secure programming, and security testing. The section requires a basic understanding of Application Programming Interface (API) and example APIs of companies referred to are:  Darktrace, Vectra and Cylance.

Impact of AI on Cyber Security

This section provides an in-depth view of threat hunting in memory, file system and network data and an introductory analysis of malicious programs.

Practical sessions will elaborate on key concepts of incident handling, cyber threat hunting and digital investigation along with detailed analysis of real-world case studies.

We will also introduce some unusual and non-virulent types of malware:

  • KNN (K - Nearest Neighbors) for threat visualisers
  • Isolation forest for anomaly detection
  • LSTM for multi-vector correlation 
  • DBSCAN for riskware detection and fraud
  • LSTM (Autoencoder) for endpoint protection

Day Three

Large scale deployment of AI algorithms on production

This section will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level)

  • It will explore parallelization of algorithms and algorithmic approaches such as stochastic gradient descent

  • There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice such as SPARK
  • Programming languages and deployment on elastic computing structures, cloud computing and/or GPUs

Case Study

End-to-end case study for a secure IoT application in a devops ecosystem


Participants who attend the full course will receive a University of Oxford certificate of attendance. This will be presented to you prior to the end of the course wherever possible.

The certificate will show your name, the course title and the dates of the course you attended.


Description Costs
Course fee £995.00


Fees include course materials, tuition, refreshments and lunches. The price does not include accommodation.

All courses are VAT exempt.

Register immediately online 

Click the “book now” button on this webpage. Payment by credit or debit card is required.

Request an invoice

If you require an invoice for your company or personal records, please complete an online application form. The Course Administrator will then email you an invoice. Payment is accepted online, by credit/debit card, or by bank transfer. Please do not send card or bank details via email.


Raj Sharma

Lead Tutor and Course Leader

Raj Sharma has over 20 years of experience in software consulting, entrepreneurship with artificial intelligence (machine learning and deep learning) , big data (Cloudera/Hortonworks), Databricks and Cloud (AWS/Azure/Google). 

As the founder of CyberPulse Ltd (AI and CyberSecurity Consultancy), Raj leads and delivers full stack data science projects and works with startups focusing on building Tech using AI and Big Data for domains such as cybersecurity, robotics and education. He has been involved in implementing artificial intelligence cyber security algorithms based on an ensemble of autoencoders.

Raj also has experience in creating Enterprise DevOps pipelines for development, training, testing and deploying ML algorithms on production environment) using GPUs in AWS/Azure/Google; Spark ML library in Python and Scala.

He has a Master's Degree in Information Security certified by GCHQ, the UK Government Communications Headquarters, with a Research Project in AI and has a Master's Degree in Software Development and Algorithm Design, along with a strong software engineering background with mathematics and statistics.  

Ajit Jaokar


Based in London, Ajit's work work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). 

He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations. Besides this, he also conducts the University of Oxford courses: AI for Cybersecurity and Computer Vision.

Ajit works as a Data Scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare.

Besides the University of Oxford, Ajit has also conducted AI courses in the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM) and as part of the The Future Society at the Harvard Kennedy School of Government.

He is also currently working on a book to teach AI using mathematical foundations at high school level. 

Ajit was listed in the top 30 influencers for IoT for 2017 along with Amazon, Bosch, Cisco, Forrester and Gartner by the German insurance company Munich Re.

Ajit publishes extensively on KDnuggets and Data Science Central.

He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), and 19th among the top 50 twitter IoT influencers (IoT Institute). 

His PhD research is based on AI and Affective Computing (how AI interprets emotion).


If you would like to discuss your application or any part of the application process before applying, please click Contact Us at the top of this page.


Although not included in the course fee, accommodation may be available at our on-site Rewley House Residential Centre. All bedrooms are en suite and decorated to a high standard, and come with tea- and coffee-making facilities, free Wi-Fi access and Freeview TV. Guests can take advantage of the excellent dining facilities and common room bar, where they may relax and network with others on the programme.

To check prices, availability and to book rooms please visit the Rewley House Residential Centre website.