Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning

Ransomware is a fast-escalating privacy threat causing significant financial and data losses worldwide. As attacks grow more sophisticated, traditional static signatures and dynamic behavior analysis struggle to defend. In this paper, we propose an novel framework that couples software-level...

SAND: A Self-Supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection

The globalized semiconductor supply chain has made Hardware Trojans HT a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ...

Python commonutils.py Code Execution

The vulnerability of the Python commonUtils.py module, a set of tools for automating function design, neural network architecture search, hyperparameter tuning, and Neural Network Intelligence NNI model compression, is related to improper code generation. Exploiting this vulnerability allows an...

The First Step-by-Step Guide for Implementing Neural Architecture Search with Reinforcement…

The First Step-by-Step Guide for Implementing Neural Architecture Search with Reinforcement Learning Using TensorFlow Our team is no stranger to various flavors of AI including deep learning DL. That’s why we’ve immediately noticed when Google came out with AutoML project, designed to make AI bui...