Artificial Intelligence for Cyber Security (Online)

Overview

A new pioneering course that blends the domains of cyber security and artificial intelligence (AI).

This online course has been designed for both 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. 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.

Programme details

Fundamentals of Cyber Security

  • 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 will also be covered
  • Compliance and security assessment:
    • This section will have a focus 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

  • deep learning fundamentals from a security perspective.
  • fundamentals of AI and how AI can solve problems in the cyber security space. 
    • Examples of companies using AI for Security, such as Cylance and FireEye.

Secure Web

In this module, we address the challenges of how AI helps create the secure web, such as:

  • 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, statistical patterns and Bayesian statistics will be used.

Deep learning applications

By looking at a variety of AI Technologies, you will be taken through how 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.

Cyber Security Threats and Development of Secure Software

Web Application Security

  • 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

You will be taken through 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.

Real-life examples include Darktrace, Nvidia and Microsoft.

Secure AI Development

The main goal of this section is to teach the foundations of secure software design, secure programming, and security testing, covering security analysis as well as the secure development of software-based systems both on architectural level and system level.

A basic understanding of Application Programming Interface (API) is needed here.

Examples of companies using APIs are: Darktrace, Vectra and Cylance.

Impact of AI on Cyber Security

You will be given an in-depth view of threat hunting in memory, file system and network data and an introductory analysis of malicious programs.

Some key concepts of incident handling will be elaborated on, such as 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 Neighbours) for threat visualisers
  • Isolation forest for anomaly detection
  • LSTM for multi-vector correlation 
  • DBSCAN for riskware detection and fraud
  • LSTM (Autoencoder) for endpoint protection

Large scale deployment of AI algorithms on production

This element of the course 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.

Course Delivery

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

Session dates: Fri 16, Sat 17, Mon 19, Fri 23, Sat 24 and Mon 26 April 2021

Sessions will be 15:00 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.

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 £995.00

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

Raj Sharma

Course Director

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

Course Tutor

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).

Ian McDonald

Guest Speaker

Dr Ian McDonald has been a visionary technology leader for over 20 years and is currently global tech team leader at Microsoft for Startups covering Western Europe.

He’s previously lead teams at a range of corporates and startups and built global scale systems.

Ian has a first class honours degree in Computing and Maths and a doctorate in network congestion. He also has been a contributor to the Linux kernel and other open source projects.

Greg Ainsile-Malik

Guest Speaker

Greg is a Machine Learning Architect at Splunk where he helps customers deliver advanced analytics and uncover new ways of insight from their data.

Prior to working at Splunk he spent a number of years with Deloitte and before that BAE Systems Detica working as a data scientist.

Before getting a proper job he spent way too long at university collecting degrees in maths including a PhD on “Mathematical Analysis of PWM Processes”.

When he is not at work he is usually herding his three young lads around while thinking that work is significantly more relaxing than being at home.

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 MS Teams.

To participate you must be familiar with using a computer for purposes such as sending email and searching the Internet. You will also 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.