System security will focus on Systems-Theoretic Process Analysis for Safety and Security (STPA-Sec). The objective is to provide students with a comprehensive understanding of how to design, implement, and evaluate secure systems by considering both safety and security as integral parts of the development process. By the end of the course, students will have a deep understanding of the challenges involved in creating safe and secure systems and the skills necessary to apply coengineering principles effectively. They will also be able to critically analyze existing systems and propose solutions to improve their overall safety and security posture.
Courses
This course outlines a comprehensive path for learning and applying probabilistic methods to various real-world scenarios, equipping students with the theoretical knowledge and practical skills to leverage uncertainty and randomness in their professional activities. From the beginning, students will learn the fundamental concepts and mathematical frameworks of probability theory and develop proficiency in statistical reasoning and inference to analyse data and make informed decisions with insight into developing a mathematical understanding of probabilistic models and how they can be employed to interpret data, make predictions, and inform decision-making processes. Besides, in the tutorials and labs, students will gain hands-on experience through several coding assignments and exercises. Finally, student will participate in a project to apply probabilistic methods to model uncertainty and solve problems across various applications such as Monte Carlo simulations to tackle real-world challenges in technology, finance, and research.
This course deeply explores issues such as fairness, transparency, accountability, and bias mitigation. The course delves into the legal and regulatory landscape surrounding AI and cybersecurity, including intellectual property rights, data privacy laws, and liability considerations. Moreover, students examine the societal implications of AI across various domain, fostering critical thinking and ethical decision-making skills. Through case studies and discussions, students gain a nuanced understanding of the complex interplay between technology, ethics, and society, preparing them to navigate the ethical, legal, and social challenges posed by AI in their professional endeavors.
Artificial Intelligence (AI) in healthcare focuses on the application of artificial intelligence technologies to enhance healthcare outcomes, operational efficiencies, and patient care. In this course, we will delve into the understanding the fundamental AI concepts and how they can be applied to solve healthcare problems, how to analyse and interpret healthcare data using AI and machine learning techniques. Moreover, students will get a comprehensive understanding of the current state and future possibilities of AI in the healthcare sector, real-world examples of AI technologies being implemented in healthcare settings, including success stories and challenges faced. The course explores various AI tools, including machine learning algorithms, natural language processing, and computer vision, and their applications in diagnostics, treatment personalization, patient monitoring, and health administration. Students will learn through a variety of formats including interactive videos, practice quizzes, presentations, assignments, and discussion forums to assess the potential of AI technologies in improving diagnostics, treatment planning, and patient care, to finally explore logistical and ethical challenges around data use and AI.
This course covers the computational methods used in crime investigation (forensics) and prevention (intelligence). It is a cross-disciplinary course that combines knowledge from forensic sciences, criminology, law, AI, signal processing, and others. We cover topics such as deepfakes, data manipulation, biometrics and surveillance, and others. We will held guest talks with law-enforcement agencies.
This course bridges the gap between Machine Learning (ML)and cybersecurity, focusing on how ML algorithms can be utilized to fortify cybersecurity measures and applications. We will cover the application of ML models to detect anomalies, predict attacks, and automate threat intelligence. Students will learn about the challenges and opportunities of applying ML in a cybersecurity context, including ethical considerations and the need for robust, secure ML models as there are many security applications which have large amount of data related to the system as well as adversarial actions. In the course, students will learn the theoretical concepts during the lectures as exploring different problem domains, gain hands-on experience to build up their skills by practicing on assignments, and finally demonstrate their knowledge and skills by participating in a final project to identify the type of machine learning algorithms that are useful for specific security applications and how to improve the defence against attacks to ultimately anticipate the potential attack variants that may rise in the future.
Network Solutions for IoT
This advanced course delves into the intricacies of relational database systems, tackling a broad spectrum of topics from sophisticated query optimization techniques to the internal workings of database management systems (DBMS). It emphasizes practical skills in designing and implementing scalable, efficient, and secure database solutions, as well as theoretical knowledge on the principles underlying database systems. Students will grasp the architecture and operation of distributed database systems, including big data integration, evaluate the applicability and limitations of NoSQL and NewSQL databases in various scenarios discussing their use cases, strengths, and how they complement traditional relational database systems to lastly implement effective strategies for data warehousing, business intelligence, and database security. Students will learn through a variety of formats including interactive videos, practice quizzes, presentations, assignments, discussion forums and handing in a final project, which will cover the technical and practical aspects of designing and deploying at least one relational database management system (e.g., MySQL, PostgreSQL, Oracle), highlighting the gained knowledge required to tackle the challenges of modern database systems and prepare for future advancements in the field.
Database security concerns the use of various controls to protect the confidentiality, integrity and availability of databases. These controls can be technical (e.g. encryption, access controls), procedural/administrative (e.g. security policies, auditing), or physical (e.g. securing server rooms). In this course we will examine database encryption, access controls, data masking, and key management.
This course will equip students with the business and leadership skills to effectively manage transformational projects in their organisations, grow a new business unit, or move towards creating their own venture. Core tools such as value-proposition models, personas, validation canvas, and others will be introduced through practical assignments. Industry leaders will provide real-world stories on innovation management and digital transformation.
This subject focuses on Industrial Internet of Things (IIoT) applications, emphasizing industrial protocols, cloud computing, edge computing, and predictive maintenance strategies. Students will delve into protocols like IO-Link, Modbus, and other industrial communication standards. Additionally, they will explore how cloud computing and edge computing technologies enhance data processing efficiency in industrial settings while integrating predictive maintenance techniques for optimal operational performance.
This course provides a comprehensive overview of network security, beginning with fundamental concepts and the importance of securing digital communications. It then delves into the protocol stack, explaining how data moves through networks and where vulnerabilities can arise. The discussion on security weaknesses covers various risks, including software flaws and human errors that attackers exploit. To counter these threats, the course explores defensive technologies such as hardening, firewalls, honeypots and intrusion detection systems. Lastly, it addresses privacy concerns in networks, examining techniques to protect user data and ensure anonymity in digital interactions.
The course covers Internet of Things (IoT) sensor systems, emphasizing the transformative potential of integrating sensor technology with the IoT across various industries. A systems engineering approach will be adopted throughout the course, where students will review and critically discuss key technologies employed at different levels of the IoT stack and how they're integrated to form complete IoT systems. Through lectures and practical activities students will gain a comprehensive understanding of the IoT ecosystem and the critical role of sensors, develop skills in designing, implementing, and managing sensor systems for IoT applications, to finally comprehend the challenges of data acquisition, processing, and management in sensor-based systems, communication technologies and protocols essential for IoT sensor networks. Students will learn through a variety of formats including interactive videos, practice quizzes, presentations, assignments, discussion forums and final project to delve into the technical and practical aspects of designing and deploying sensor systems within the IoT infrastructure with a key focus on exploring considerations of energy efficiency, sustainability, security, and privacy in IoT sensor systems.
Cybersecurity of industrial control systems protects industrial processes and equipment from threats. With increasing reliance on automation and computerized systems, it has become crucial to understand the unique vulnerabilities faced by industrial environments. This course delves into core areas like risk assessment, defense mechanisms, incident response, and regulatory compliance. We will address both Information Technology (IT) and Operational Technology (OT) security.