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Adversarial attacks to machine learning based models in cybersecurity

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks.The act of deploying attacks towards machine learning based systems is known as Adversarial Machine Learning (AML) [1]. The aim is to exploit the weaknesses of the pretrained model which has “blind spots” between data points it has seen during …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Computer Science

Cyber-security aspects of battery storage systems

Lithium-ion (Li-ion) batteries are a key energy storage component in various electrical and electronic systems such as mobile phones, electric vehicles and grid storage. A properly designed battery management system (BMS) is crucial to guarantee the safety, reliability, and optimal performance of the battery as well as to interconnect the battery systems with each other and external systems through communication channels. However, security threats of the Li-ion battery systems are often overlooked by BMS developers in the design phase. The …

Study level
PhD, Master of Philosophy
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)

Centre for Clean Energy Technologies and Practices

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