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Intercept X: Deep Learning driven
Posted On Dec 14, 2020Artificial intelligence to stop malicious threats
Most of the defense of today is not designed to meet new challenges. It's too slow, and rather than constructive, reactive to threats. Every day, threats are becoming more complex, and current cybersecurity offerings are struggling to keep up. Every day there's a steady stream of new malware developed. An advanced type of machine learning, deep learning helps change the way we approach endpoint security. Deep learning functions like the human brain, delivering high levels of precision to detect current and never-before-seen malware. Intercept X is ready to face unknown challenges with quick, strong predictive defenses through the integration of deep learning. Prevention of Deep Learning Risks includes: Experienced Design in Design Our team of scientists developed the Sophos malware detection model using DARPA-driven technology, designed to uncover the "DNA" of malware. Effectiveness Proved Intercept X was tested by the best of them. Our deep learning technology has been found to be extremely efficient with low false positives by independent third-party testers time and time again. Driven by SophosLabs Data scientists at SophosLabs work with hundreds of millions of malware samples, helping them to create the best prediction models possible. To continuously change, they team up with threat researchers. A large number of artificial neurons" are arranged by deep learning systems into layers that slightly mimic the way human brains learn. (There are major variations between these neural artificial" networks and the one between your ears. But there are also certain parallels that are not trivial.) For a long time, neural networks have been around. But they had significant drawbacks, and their shortcomings led to a backlash, as with many things AI-related, in which many researchers and investors turned away from them. But many years ago, there were some major things that changed. Also, simple neural networks tend to use a lot of computing resources, and the load is significantly increased by adding layers. So most neural networks were very "shallow"-and their capacity to learn was minimal. But computing capacity exploded, and the cost of that power plummeted. One big factor is that video gamers need more efficient hardware for graphics. Specialized "graphics processing units (GPUs)" have been invented and have proved to be highly suitable for neural networks. Deep neural networks often require lots of knowledge to learn from and when you continue to send them more information, they also continue to evolve, which has not always been the case for previous techniques. There are a lot of data nowadays. And offer some well-deserved credit to the researchers: they came up with considerably smarter ways to coordinate and develop these networks.
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