Behavior Profiling on Wireless Networks

Wireless networks are dynamic environments that experience change over time. Companies must adapt to these changes – no one wants a wild network running fast and loose – but doing so means monitoring this dynamic environment and eliminating unnecessary redesign. Companies aren’t without resources; in fact, in almost no time at all they can access billions of bytes of data. But it isn’t that simple. IT teams want meaningful metrics, not garbage information. Technology has to target good data – quality over quantity – and that kind of distinction only comes after gaining a deep understanding of how a network operates. Learning and intelligence come into play and that’s where we are increasingly seeing behavioral profiling. Profiling technology helps companies to eliminate guesswork and avoid overloading infrastructure with unnecessary data. Used successfully, profiling plays an important role in making a network scalable, secure and user friendly.

We’ve talked before about device fingerprinting and the ways it’s changing the IT world. Behavioral profiling takes those fingerprints and dives even deeper into network ecosystems. If fingerprints provide a snapshot of network activity, profiling provides a life history of activity and experiences. Collecting and collating this information allows profiling systems to determine normal network behavior, and then recognize changes to that behavior. What we want to see is a system starting with raw data but ending with actionable intelligence. This means companies can answer questions like – are there historical correlations to apply to present day activity? Is equipment functioning well or experiencing anomalies? What maintenance might be needed over the next month? How much traffic is on the network and how has that changed over time? Have there been security breaches?

At its best, behavioral profiling becomes completely in-tune with a network, able to work intuitively both in areas of cognitive analytics (in-depth analysis of large amounts of diverse data) and predictive analytics (using historical data and machine learning to predict future outcomes).

To reach that intuitive state, companies need to build algorithms that will be the base of pattern recognition and profiling, but only after determining what success will look like. Presumably, most companies will be interested in similar capabilities – simplifying labor and maintenance, enhancing user experience, reducing outages and increasing efficiency. Profiling uses these metrics to collect and analyze rich swatches of data and build a picture that accurately reflects user activity. Then, the predictive technology aspect communicates to IT teams how to adapt a network so that it continues to provide exemplary and superior service.

That’s exactly what we’re all about at Wyebot. Using cloud infrastructure, our big-data algorithms fuel pattern recognition and behavioral profiling. Our system constantly learns, tracking metadata as it evolves and updating patterns to ensure we stay up to date with the ever-changing capabilities and interoperability of network devices. We use our next-generation predictive visibility to keep our clients’ networks running reliably and efficiently. Our profiling technology identifies issues, analyzes the impact on users, and automatically suggests solutions to solve the issues and alleviate negative effects, such as reducing downtime. It’s machine learning at its finest, and it increases the value of IT.

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