machine learning

Researchers have learned to recognize the positions and poses of people indoors using Wi-Fi signals. To do this, they used ordinary home routers and machine learning.

How hackers exploit chatbot features to restore encrypted chats from OpenAI ChatGPT, Microsoft Copilot, and most other AI chatbots.

Getting all the benefits of ChatGPT, Copilot, and Midjourney locally — without leaking your data to the internet.

AI tools can be seen everywhere — from operating systems and office suites to image editors and chats. How do you use ChatGPT, Gemini, and the many add-ons to these without jeopardizing your digital security?

We propose six principles of ethical use of AI in the cybersecurity industry — to be presented at the global Internet Governance Forum.

Audio deepfakes that can mimic anyone’s voice are already being used for multi-million dollar scams. How are deepfakes made and can you protect yourself from falling victim?

It’s obvious already that AI needs regulating, but how? Here’s Eugene Kaspersky telling us how he sees it.

A new generation of chatbots creates coherent, meaningful texts. This can help out both cybercriminals and cyberdefenders.

We’ve been using machine learning in Kaspersky Internet Security for Android for years now. Here’s why — and what we’ve achieved.

How ontologies can provide the world with greater, faster protection from cyberthreats and more.

Our method for training models to filter out spam lets you maintain privacy without losing efficiency.

With deepfakes becoming more and more common — and more and more convincing — how can you protect your business?

We examine the workings of emotion-recognition technologies, their usefulness, and the privacy concerns they inspire.

This year’s Kaspersky NEXT event centred around AI, gender equality, and social robotics.

Social engineering augmented with machine-learning algorithms can deceive even high-ranking executives.

In this episode of the Kaspersky Lab podcast, Dave and Jeff take a look at the latest Facebook snafu, AI in Dota 2, dumb criminals, and more.

To reduce the attack surface you can block many vulnerable features of software. The question is, how can you do that but not interfere with business-processes?

What surprises do machine learning have in store for us? How difficult is it to trick a machine? And will we end up with Skynet and rise of the machines? Let’s take a look.

Any piece of malware can be captured if you know it for what it is — for example, if you use a trainable behavioral model.

What’s troubling the cybersecurity community most in 2018?