Threat Detection

Threat detection research focuses on developing robust and efficient methods to identify malicious activities across diverse digital environments, from computer networks and IoT devices to blockchain systems and smart grids. Current efforts concentrate on leveraging machine learning, particularly deep learning architectures like convolutional neural networks, transformers, and generative adversarial networks, often coupled with techniques like federated learning and transfer learning to improve accuracy and scalability. These advancements are crucial for enhancing cybersecurity, protecting critical infrastructure, and mitigating the increasing risks posed by sophisticated and evolving cyber threats.