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AI System Promises Smarter Malware Defense With Privacy Protection
Reading time: 2 min
Last Updated: Aug 19, 2025
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Fact-Checked by Sarah Frazier Content Manager
Researchers have developed a new system to detect and fight malware using a technique called federated learning (FL).
In a rush? Here are the quick facts:
- Uses federated learning to protect privacy while training models.
- Lab tests showed 96% success against major cyberattacks.
- Real-world accuracy dropped to 59% with complex data.
A group of researchers developed a new way to contrast computer viruses and cyberattacks inside large networks. They explain that the system uses artificial intelligence, and a method called “federated learning” to stop threats while keeping personal data private.
The idea is to combine the strengths of modern networks, which have a central “control hub,” with AI that learns in a safe, decentralized way. Instead of collecting all user data in one place, the system shares only updates to the AI model.
“Our architecture minimizes privacy risks by ensuring that raw data never leaves the device; only model updates are shared for aggregation at the global level,” the team said.
In early lab tests, the system did very well. It stopped up to 96% of big cyberattacks like botnets and Distributed Denial of Service (DDoS) attacks. But when tested with more real-world situations, the accuracy dropped to about 59%. The researchers say this shows just how tricky real cyber threats can be.
Even so, the system worked quickly, spotting attacks in less than a second and helping networks recover speeds from 300 to 500 megabits per second. It also managed the heavy data traffic without slowing everything down.
The new tool is especially good at spotting obvious, high-impact attacks. But it still struggles with subtle ones, like when hackers secretly steal information over time. To fix this, the researchers plan to train the AI with better data and improve the way it learns patterns. They also want to add stronger privacy tools, like secure data-sharing methods.
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