Machine learning is becoming widespread, and organizations are using it in a variety of ways, including improving cybersecurity, enhancing recommendation engines, and optimizing self-driving cars. Here's a look at 11 interesting use cases for this technology.
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In 2014, Kaspersky Lab reported it was detecting 325,000 new malicious files every day. At that rate, humans and even signature-based security solutions can't keep up, which is why machine learning and deep learning are necessary.
"Nearly all new malware differs less than 2% from previous malware," said Eli David, CTO of institutional intelligence company Deep Instinct, in an interview. "Our deep learning model has no problem dealing with the 2% - 10% mutations we see every day."
Deep Instinct uses a large core of several million malicious files, tens of millions of legitimate files and malware that Deep Instinct may have mutated by 20% - 50% for training purposes. The more radical malware mutations make the training more difficult, but they also make the model more resilient. Once the training is finished and the synapses have been updated, a text file of the synapses can run deep learning in prediction mode.
Cybersecurity Strategies for the Digital EraAt its core, digital business relies on strong security practices. In addition, leveraging security intelligence and integrating security with operations and developer teams can help organizations push the boundaries of innovation.