Our advanced artificial intelligence (AI) system uses machine learning to automatically collect and extract data from our entire user base — then trains every security module. After finding a new malware sample, our products are automatically updated with new models, providing crucial, up-to-the-second protection.
Training the Avast machine learning engine
Sophisticated threat prevention in today’s world does not rely on a single machine learning engine that provides a silver bullet to all cyberattacks. Instead, it requires a combination of multiple ML engines that work hand-in-hand to defend against attacks. The engines work across devices (both on the cloud, PCs and smartphones), they use static and dynamic analysis techniques, and they are deployed in many of the layers of our defense engine.
In order to evaluate new and unknown threats, we’ve built a unique and sophisticated machine learning pipeline that allows us to rapidly train and deploy malware detection models within 12 hours. We also employ advanced techniques like deep convolutional neural networks (Deep CNN) to enhance our malware detection models. New security threats can appear suddenly, and take new and unknown forms; in such situations, our ability to update our models rapidly ensures our users remain protected.
Next-gen security technology and the data from our massive user base give us a clear advantage against hackers – and competitors.
This next-gen security technology and the data from our massive user base gives us a clear advantage against hackers – and competitors. And it is this technology that has allowed us to automatically detect and block high-profile threats such as WannaCry, BadRabbit, NotPetya ransomware, and the Adylkuzz crypto-mining attacks, without requiring a single product update.
ATTACKS STOPPED A MONTH
RANSOMWARE ATTACKS BLOCKED IN 2017