Artificial Intelligence for Cyber Security (online)



Overview

A new pioneering course that blends the domains of cyber security and artificial intelligence (AI).  
This course has been designed for cyber security professionals who want to understand AI, and AI professionals who want to work with cyber security.  

Artificial intelligence impacts all the personas in cyber security (threat actors, defenders, regulatory and government agencies, etc.).  
In this course, we aim to create an overall framework spanning personas, technology components, and platforms and study the impact of artificial intelligence on this ecosystem.   

When securing an AI system, there are three primary components that one could evaluate and mitigate risks for:  

  1. the training data 

  2. the model parameters,  

  3. and the trained model itself.  

However, there are many nuances these AI considerations apply to individual ecosystem components (personas, platforms, and technology components).  

An outline of this course is shown below.  

Where coding is needed, Python will be used.  
You are expected to be familiar with coding but are not required to master any specific language. Some code will be used in demonstrations, but you will not need to do any coding yourself. 

The course uses the book: Machine Learning Security Principles - an electronic copy will be provided to you as part of the course.

Programme details

Fundamentals of Cyber Security 

This section explores the fundamentals of cyber security.  
The themes covered include:  

  • Identity  

  • Authentication  

  • Confidentiality  

  • Privacy  

  • Anonymity  

  • Availability and integrity  

  • Cryptographic algorithm,  

  • Major attack types 

  • High-level security protocols  

  • Authentication 

  • Compliance 

  • Security assessment.

Fundamentals of AI for Security 

This module discusses the fundamentals of AI and cyber security, including the algorithms, benefits, and threats to AI models.  
Here, we take a case study approach and discuss the strategies of specific vendors.  

Actors in the Cyber Security ecosystem and how Artificial Intelligence impacts their roles 

This section discusses each participant in the cyber security ecosystem and how they are impacted by artificial intelligence.  

Securing a Machine Learning System 

In this section, we discuss how to secure a machine learning system with the aim of understanding the types of attacks on a machine learning system and its mitigations.  

Mitigating Risk at Training by Validating and Maintaining Datasets  

Data is one of the most significant risks to AI and cyber security.  
This section covers issues like dataset-related threats, data corruption, feature manipulation threats, and dataset modification risks.  

Detecting and Analysing Anomalies  

Here, we cover the general strategies for detecting and analysing anomalies using machine learning and deep learning.  
We cover specific algorithms, case studies, code walkthroughs, and vendor examples. 

Network-level threats and mitigation using machine learning 

In this section, we cover network level threats and mitigation using machine learning. We cover specific algorithms, case studies, code walkthroughs, and vendor examples. 

IoT threats and mitigation using machine learning  

In this section, we cover IoT threats and mitigation using machine learning. We cover specific algorithms, case studies, code walkthroughs, and vendor examples. 

Emerging threats and mitigations 

In this section, we discuss emerging threats and mitigations like: 

  • Mitigating Inference Risk by Avoiding Adversarial Machine Learning 

  • Considering the Ramifications of Deepfakes  

  • Implications of explainable AI 

  • Threats and mitigation for Large language models like chatGPT 

The above may be subject to minor changes and revisions

Course Delivery

This course will run over six live online sessions on Mondays, Wednesdays and Fridays.

Session dates: Monday 6, Wednesday 8, Friday 10, Monday 13, Wednesday 15 and Friday 17 November 2023. 

Sessions will be 14:00 to 18:30 UK time (with a half-hour break) and delivered online via Microsoft Teams.

A world clock, and time zone converter can be found here: https://bit.ly/3bSPu6D

No attendance at Oxford is required and you do not need to purchase any software.

Accessing Your Online Course 

Details about accessing the private MS Teams course site will be emailed to you during the week prior to the course commencing.  

Please get in touch if you have not received this information within three working days of the course start date. 

Digital certification

Participants who attend the full course will receive a University of Oxford electronic certificate of attendance. 

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

You will be required to attend all of the live sessions on the course in order to be considered for an attendance certificate. 

Fees

Description Costs
Course Fee £1195.00

Payment

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

Tutors

Ajit Jaokar

Course Tutor

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

Ajit is the Course Director and/or tutor for Continuing Education’s portfolio of artificial intelligence courses for professionals:

Ajit is also a Visiting Fellow in the Department of Engineering Science here at the University of Oxford.

He also 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).

Olu Odeniyi

Guest Speaker

Olu Odeniyi has over 30 years’ experience helping organisations maximise commercial gain from technology solutions.
During this time, Olu held several key senior leadership, strategic and operational positions in the public and private sectors where he gained awards for innovation and exceeding objectives.  

He advises companies on cyber security, information security and digital transformation.
Working with the University of West London Enterprise hub, Olu created and led workshops on cyber security, AI, and information security. He has also created and taught cyber security courses to audiences from the public and private sectors.  

Olu led the cyber security and big data themes for the Science and Innovation Audit (SIA) sponsored and by the department of Business, Energy and Industrial Strategy (BEIS) led by Brunel University. He also led a workshop on “cyber security for airports” involving experts from Glassgow and Royal Holloway university.
Olu co-authored the final report published by BEIS.  

As a virtual CISO, Olu has led several cyber security incident responses and liaised with forensic investigators.
He leverages his practical expertise and scholarly inquiry to produce insights and guidance on emerging cyber security challenges. He has also reviewed books on cyber security and is quoted regarding his acuities on “the threat of deepfakes”. 

Olu is currently researching application of AI for cyber security within IoT, working with the University of West London. He is also working on a new start-up focused on educating boardrooms on cyber security governance. Olu speaks on various cyber security topics at public events and private gatherings. 

Researching the use of Large Language Models (LLMs) within the cyber security domain is another current research interest for Olu. 

Olu has a degree in Electronic Systems Engineering and is a professional member of BCS (Chartered Institute for IT).
Special Interest groups Olu has joined include Artificial Intelligence, Cybercrime Forensics, Information Risk Management Assurance, Information Security, Agile Methods.

Raj Sharma

Course Director

Raj Sharma has over 24 years of experience in software consulting, entrepreneurship with artificial intelligence (machine learning and deep learning), Big data technologies, CyberSecurity and AI specialists, Data engineering, Tech Executive, and Focus on Sustainability in Digital.

As the founder and Principal Consultant of AI and CyberSecurity Consultancy, Raj leads and delivers full-stack data science projects and works with startups, scaleups, MNCs, UK Government, focusing on building Tech using AI and Big Data for domains such as cybersecurity, healthcare, and education. He has been involved in implementing artificial intelligence cyber security algorithms based on a Generative AI.

Raj also has experience in creating Enterprise Data platforms and secure Machine learning pipelines for development, training, testing, and deploying ML algorithms in a production environment. 

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, statistics, and physics. Pursuing PhD from Royal Holloway University of London (Generative AI applications in Information Security).

Application

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.

IT requirements

This course is delivered online using Microsoft Teams. You will be required to follow and implement the instructions we send you to fully access Microsoft Teams on the University of Oxford's secure IT network.

This course is delivered online; to participate you will need regular access to the Internet and a computer meeting our recommended Minimum computer specification.

It is advised to use headphones with working speakers and microphone.

Terms & conditions for applicants and students

Information on financial support