Time and date: 3 May 2023 at 2:00 pm | Location: Abacws 1.04 | Speaker: Vasilis Ieropoulos
The use of machine learning (ML) in threat detection has gained significant attention in recent years due to its ability to improve the accuracy and speed of detecting malicious activities. However, the deployment of ML models on resource-constrained devices, such as IoT devices and microcontrollers, poses significant challenges due to the limited computational resources and energy constraints.
The potential for bringing security to these resource-constrained devices through the use of ML, which has traditionally been located only on a centralized device, is enormous. This traditional approach has led to a single point of failure and reliance on external devices for security, leading to large-scale botnets used for malicious attacks.
