On Certification of Artificial Intelligence Systems

Machine learning systems are today the main examples of the use of Artificial Intelligence in a wide variety of areas. From a practical point of view, we can say that machine learning is synonymous with the concept of Artificial Intelligence. The spread of machine learning technologies leads to the need for their application in the so-called critical areas: avionics, nuclear energy, automatic driving, etc. Traditional software, for example, in avionics, undergoes special certification procedures that cannot be directly transferred to machine learning models. The article discusses approaches to the certification of machine learning models. - from our new paper On Certification of Artificial Intelligence Systems

Attacks on Machine Learning Models Based on the PyTorch Framework

This research delves into the cybersecurity implications of neural network training in cloud-based services. Despite their recognition for solving IT problems, the resource-intensive nature of neural network training poses challenges, leading to increased reliance on cloud services. However, this dependence introduces new cybersecurity risks. The study focuses on a novel attack method exploiting neural network weights to discreetly distribute hidden malware. It explores seven embedding methods and four trigger types for malware activation. Additionally, the paper introduces an open-source framework automating code injection into neural network weight parameters, allowing researchers to investigate and counteract this emerging attack vector. from our new paper