A CyBOK Funded Project
Welcome to the AI for Cybersecurity training, a project funded by CyBOK that provides active learning-based materials for students and lecturers to learn the topic. This course aims to teach higher education students how to develop and use AI to detect cyber attacks. We use an active learning approach, which means students learn each topic through in-class discussions, hands-on activities,...
This session provides an overview of cybersecurity concepts. Students learn what information security means and what cyber threats and vulnerabilities are. They will get some ideas about how attackers use different techniques to compromise users and what solutions exist to detect and prevent cyber threats. The main aim of this session is to learn the fundamentals and basics of cyber...
Introduction to Machine learning This session provides an overview of Artificial Intelligence (AI) and Machine Learning (ML) concepts. Students learn what AI and ML mean, how ML works, Examples of AI and ML applications and algorithms, etc. The aim of this session is to understand the concepts and basics of ML, which will serve as a base for the next...
This session links cybersecurity and AI concepts and helps the students understand how AI solutions have been used to detect cyber attacks. Students are expected to listen carefully and contribute to discussions.
This session explains the cyber threat landscape and different types of malware. Students will learn the types of cyber attacks and the stages involved. They should learn attack techniques and scenarios and their impacts on organisations and people. This session must contain examples of attacks (e.g., phishing, DDoS, ransomware), real-world attack scenarios, and hands-on activities (e.g., a phishing simulation).
This session provides an overview of Artificial Intelligence (AI) and Machine Learning (ML) concepts. Students learn what AI and ML mean, how ML works, Examples of AI and ML applications and algorithms, etc. The aim of this session is to understand the concepts and basics of ML, which will serve as a base for the next sessions.
This session explains how to analyse malware using static, dynamic, and automated techniques and solutions. Students will learn the steps of malware analysis and how to use tools to investigate a file. This knowledge will help build an AI to analyse files and detect malware (in the next session).
This session explains how to detect malware using machine learning. Students will learn machine learning techniques which can be used to detect malware.
This session provides an overview of network anomaly detection. The students will get some ideas about how attackers use different techniques to compromise networks and what solutions exist to detect and prevent such attacks.
This session provides an overview of network anomaly detection using AI