Enhancing Cybersecurity: LLM-based Intrusion Detection System.
The threat of cyberattacks is challenging modern digital infrastructure. While traditional intrusion detection systems are already effective, they have certain limitations, for example, in detecting new attack patterns. With ...
Dynamic Anomaly Detection for Evolving Cyber Threats Using Adaptive LSTM Networks
Cybersecurity systems must adapt to constantly evolving threats, where traditional static models, such as LSTM, struggle with concept drift and unseen attack vectors. This thesis proposes an adaptive anomaly...
A Transformer-Based Approach to Intrusion Detection in IoT Networks
The Internet of Things (IoT) is transforming industries like healthcare and industrial automation, while becoming a key part of everyday life through smart home devices. However, the complex and interconnected nature of ...
Towards an Evaluation Framework for Co-Simulation Middlewares
The integration of complex, multi-domain models through co-simulation frameworks is essential for the analysis of modern cyber-physical systems. However, the landscape of available tools presents a critical challenge: prominent framework...
Edge-Centric Digital Twin Framework for Cyberattack Simulation and Response Modeling in Smart Grids
The increasing integration of digital and communication technologies in power systems has made smart grids a crucial part of modern infrastructure. However, this interconnectedness has also exposed t...
Enhancing Smart Grid Security: A Deep Learning Approach to Adversarial Intrusion Detection
Smart grids are modern energy infrastructures that integrate digital communication and automation to enhance efficiency and reliability. However, their increased reliance on digital technologies exposes them ...
