It likely will come as no surprise that the machine learning (ML) market is expected to experience a compound annual growth rate of almost 40 percent through 2027 – when it’s estimated the value of the market will then be $117 billion. What may come as more of a surprise is that the top two ML use cases are …
Machine learning takes the guesswork out of network capacity planning Read More »
Whether you’re monitoring an enterprise network for performance or security, you need a tool to help collect and process data. Data is good to have, but it’s really just documentation unless you can put it to use. Almost every organization wonders how they can use data to improve network performance or security. Machine learning helps …
Hallmarked by the application of machine learning to network metadata, Network Detection and Response leverages advanced intelligence and integrations with response-capable solutions such as NAC and SIEM to provide incredible visibility into an organization’s network traffic. This is a stark contrast to the traditional approach of leveraging thresholds and pre-defined traffic patterns to automate network …
The 7 best Network Detection and Response use cases Read More »
Last year Plixer released version 19 of Scrutinizer, and with it came the introduction of Plixer Security Intelligence, our machine learning appliance. The ML appliance learns and establishes what is normal in network traffic behaviors across hundreds of data models. When Scott’s PC reaches out to an application that he has never touched, or exhibits …
Malware detection using machine learning and NetFlow Read More »
Plixer has been recognized by Cyber Defense Magazine’s 2021 InfoSec Award as the Most Innovative in Network Detection & Response. This award acknowledges Plixer’s continued evolution of our industry-leading NDR platform.
A few years ago, we added a behavioral algorithm to Plixer Scrutinizer that looked at all the flow data that was collected and determined if there was possible ICMP tunneling taking place. That algorithm alarmed if it determined that packet sizes were abnormal for ICMP traffic from a Windows or Linux platform.
The purpose of this blog is to de-mystify the hype around machine learning (ML) by exploring three topics: What kind of ML is Plixer using and why? What insights or predictions can be drawn out of NetFlow, IPFIX, and metadata? Once applied, how do the results reduce the strain on network and security operations teams?
Although the concepts of artificial intelligence and machine learning are not new, they have recently garnered mainstream attention. As is normal in the technology space, the initial hype makes it sound like a panacea and cybersecurity vendors are scrambling to hitch their marketing messages to it. This blog will help you differentiate fact from folly …
What is Machine Learning: The Hype, the Promise, and the Reality Read More »