Ewch i’r prif gynnwys
Eirini Anthi

Dr Eirini Anthi

Darlithydd mewn Seiberddiogelwch

Yr Ysgol Cyfrifiadureg a Gwybodeg

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Trosolwyg

I am a lecturer in cybersecurity at the School of Computer Science & Informatics. My research interests revolve around the security of Industrial Control Systems and Internet of Things devices. More specifically, I research how machine learning methods can be used to defend such systems. In addition, I am particularly interested in the security and robustness of such machine learning models against Adversarial Machine Learning (AML) attacks.

My research which focused on the intelligent detection of attacks in smart home environments was featured in the Welsh Government Innovation Magazine.

Testbed Design and Implementation

I have also been responsible for designing and partially implementing the new cybersecurity demonstrator at the school. This involved the development of a set of cybersecurity training scenarios in different environments that were implemented and are showcased using a state-of-the-art Augmented Reality (AR) system that visualises these infrastructures and a cutting-edge cyber-range that realistically simulates such networks.

I have also designed, implemented, and continue to maintain the IoT smart home testbed at the school which enables the cohort to conduct cybersecurity research on modern smart devices, enhancing the impact of their research by simulating real-life cases

A few of the awards I have won include:

- A member of a team awarded 1st place in an open-source intelligence competition, 2019.

- Best paper award at the second EAI International Conference on Cloud, Networking for IoT Systems in Italy, 2017.

- Best research poster, Cardiff University, 2017.

Cyhoeddiad

2024

2023

2022

2021

2019

2018

2017

Articles

Conferences

Monographs

Thesis

Ymchwil

2021

2020

2019

2018

2017

Addysgu

Over the past 4 years apart from giving a range of guest lectures, assisting with labs, and creating CTF based teaching events, I have also supervised more than 30 state-of-art research based final year projects in cyber security for both postrgaduate and undergraduate students. Few of the projects I supervised included:

  • Investigating adversarial machine learning against malware detection systems
  • Investigating Malware propagation via IoT devices to internal networks
  • Vulnerability assessment and risk modeling of attacks in IT systems
  • Detecting network based attacks in Industrial Control Systems
  • Evaluating the robustness of a lattice-based cryptosystem
  • Investigating the Security and Privacy of a Real-World Internet of Things Environment (During the project the student Identified and reported to the company a vulnerability against the IoT device)
  • Investigation and analysis of security issues in smartphone applications
  • Intelligent Attack Detection for IoT Networks (Led to publication)
  • Investigating Radio Frequency vulnerabilities in the Internet of Things using HackRF (Aim to produce a publication)
  • Cryptanalysis of a Wireless Security System
  • Simulating the Effects of Releasing Malware into the Internet of Things
  • Security Issues, Privacy, and Challenges in the Internet of Things: Vulnerabilities, Threats and Attacks.

Bywgraffiad

Eirini Anthi is a lecturer at the School of Computer Science & Informatics, Cardiff University. Her research interests revolve around the security of the Internet of Things (IoT), SCADA, and Industrial Control Systems. More particularly, her research examines the security issues that come along with these devices/systems and focuses on developing intelligent and more robust cyber-attack detection mechanisms for such networks using machine learning and adversarial machine learning techniques. As part of her doctorate, she developed state-of-the-art tools to detect and defend against network-based cyber attacks in such infrastructures.

Meysydd goruchwyliaeth

- Internet of Things Security

- Industrial Control Systems Security

- Network Security

- Smartphone security 

- Machine Learning & Adversarial Machine Learning

- Malware Analysis

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