Nobody wants to spend hours configuring a product. It should just work. Flowmon’s customers, partners, and distributors are fortunate in having a number of presets at their disposal for speedy configuration, whether during initial deployment, adjusting the product to new circumstances, or a quick product demonstration. There is no laborious setup; the user just chooses a use case, follows a few guided steps, and lets the system set everything up.

My colleague Greg Zweig recently wrote a blog “Forget Videobombing – Worry About What You Can’t See” in which he outlined how Unified Communications (UC) assets are all too often left unsecured, making them an easy target.

Network Monitoring solutions, much like the diagnostic and surgical tools of a medical professional, make it easier for the IT team to discover and locate devices installed within the network or operated via the cloud. These systems make it easier for the IT operations team to understand the ongoing issues in real-time, as and when they occur. Whether it is uptime, disk space, or any other performance issues.

Pandora FMS has become well known as a reliable, flexible advanced network monitoring solution. It allows businesses of all sizes to monitor and manage network and hardware issues in real time, and integrates all of the monitored terminals, servers, and other entities into a centralized system. These features mean that Pandora FMS has become the go-to choice for small businesses looking to deploy advanced network monitoring technologies for the first time.

Artificial intelligence refers to the machine's ability to learn and think. Given that it sort of mimics how humans think and reason, AI's application is virtually endless. AI reduces human error, do a task that is risky for humans to do, help humans solve complex, and so much more. With the emergence of artificial intelligence, concerns about data privacy have been brought into the light. Artificial intelligence relies on our personal information to learn.

The HAProxy Process Manager allows you to start external programs that are managed under HAProxy. Not everything is compiled directly into HAProxy’s C code. Some components are written using other programming languages and run alongside the load balancer.

In Part 1 of this blog series, we took a look at how we could use Elastic Stack machine learning to train a supervised classification model to detect malicious domains. In this second part, we will see how we can use the model we trained to enrich network data with classifications at ingest time. This will be useful for anyone who wants to detect potential DGA activity in their packetbeat data.