An attacker already inside your network is like a burglar who’s slipped past the front door and now walks your hallways. They move slowly, testing doors, probing access, hunting for the “safe” where real value lives. That quiet wandering, from…
You detect anomalous endpoint behavior by teaching a machine what normal looks like, then watching for the slightest deviation. It’s not about known viruses, it’s about spotting the weird. The process that spawns from a temp folder, the sudden spike…
You can’t defend what you don’t understand, and that starts with knowing what “normal” looks like in your own environment. When you map everyday user behavior, the logins, access patterns, data usage, you turn random events into a baseline you…
You’re looking for something that finds the threats your other tools can’t see. The ones that don’t trigger a signature. User and entity behavior analytics, or UEBA, does exactly that. It learns what normal looks like for every user and…
We watched a security team scramble for days, reacting to alerts that came too late. The problem wasn’t a lack of data; it was a lack of understanding. They were looking for known bad things, instead of spotting abnormal behaviors. …
Unsupervised learning network anomaly detection watches your traffic by first learning what “normal” really looks like. Think of your network as a pattern of habits, where data moves with a familiar tempo between users, apps, and servers. Over time, the…
Threshold-based alerting is failing because it treats living, changing systems like they’re frozen in time. Your CPU hits 95% at 3 AM, the pager screams, and you already know it’s that same batch job again, yet the rules don’t care…
Seeing a network threat early starts with knowing what “normal” really looks like. Instead of chasing every packet, you study the usual rhythm of your traffic, so even a small wrong note stands out. That means building a baseline of…
Some networks start to feel alive once you’ve watched them long enough. Traffic ebbs in the morning, spikes before lunch, slows in the late afternoon. There’s a rhythm there, almost like breathing, and after a while you notice when it’s…
The pager screams at 3 a.m., and you already know there’s a good chance the “anomaly” is nothing. Then, hours later, security reviews the logs and realizes a real threat slipped past while everyone was drowning in noise. That kind…