The purpose of this presentation is to use python scripts to perform some tests of efficiency and detection in various endpoint solutions, regard to the efficiency in its detection by signatures, NGAV and Machine Learning. The first objective will be to simulate targeted attacks using a python script to download these artifacts directly on the victim's machine. The second objective is to run more than one python script with daily malware, made available by MalwaresBazaar upon request via API access, downloanding daily batches of malwares. With the final product, the front responsible for the product will have an instrument capable of guiding a mitigation and/or correction process, as well as optimized improvement, based on the criticality of the risks.
Employing machine learning for cybersecurity offers tremendous benefits and has become a vital component in many security solutions. However, there are also many risks that security professionals must understand. This presentation will briefly cover some underlying machine learning concepts and how machine learning can improve security. Then, we will look at the possible vulnerabilities and risks, including attacks on the machine learning process, data, and models. Finally, the presentation will explore implementing adversarial machine learning to test and protect our machine learning models.
This talk will go through some of the considerations and reflections of deploying AI & Automation solutions for 5G deployments. It touches on what cybersecurity is in this context, the Tech-Telecom convergance, the importance of well managed data and some considerations for the security of analytics and automation in this context.
