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Hidden Pattern Recognition (HPR): The only response for AI attackers for NDR

Just like organizations are using AI to cut through the noise and provide insights into their data – bad actors are also taking advantage of AI for launching today’s most sophisticated attacks. They leverage AI for the identification of vulnerabilities, exposing weaknesses, and launching custom attacks that go undetected.

In face of such threats, the legacy solutions’ approach of looking for very specific built-in attack signatures keeps collapsing. The reason is very simple - They are being told what to hunt for, very specific pre-configured alerts, which intuitively makes AI-based attacks very powerful.

Thus, the response must be to fight machines with machines in order to overcome these attackers.

What makes AI and ML so helpful is their ability to detect the hidden patterns that signal attacks, to reveal what’s really taking place across networks in real-time, and, more importantly, to autonomously learn new “unknown” attacks that were not used in their training and thus not limited to a specific set of pre-configured alerts

This is why AI and ML techniques must play a key role in network security going forward.

The good news is that the future is already here with Cynamics HPR (Hidden Pattern Recognition) - using ground-breaking AI to infer the behavior of the full 100% network traffic, based on the sampling of just a small fraction of network data. Our technology analyzes network behavior in different layers and is able to predict threats and attacks before they happen based on their patterns, automatically learns if a network pattern is legitimate or suspicious, and autonomously “understands” changing trends in the network.

The key to understanding here is that Cyber attacks are not a singular event that just happens one day out of the blue, they are actually the outcome of a flow of events in your network - for example, it may start in browsing the web and clicking on the wrong link, getting your device infected, having the bad actor penetrating into the network, then propagating inside the network and infecting other devices, sending ‘keep-alive’ messages outside and eventually launching the attack.

Each step in this process has a network pattern that is very different from the normal day-to-day network patterns and can be predicted by Cynamics HPR.

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