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If you miss an attacker on your network, it’s probably not because you don’t have enough data. It’s more likely that you have too much data.
This happened to me a lot early in my career, particularly when writing Snort signatures or searching for things in a SIEM. Most tools have limited native matching capability, so it always came down to writing regular expressions to dig deeper. I avoided them like the plague.
Seriously, a human wrote this?
^(?:(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])\.){3}(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])$
Is this real life?
([a-zA-Z0-9_.-]+)@((\[[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.)|(([a-zA-Z0-9-]+\.)+))([a-zA-Z]{2,4}|[0-9]{1,3})
Regular expressions are confusing, hard to read, and it seems like you have to learn a whole new programming language just to make sense of them. My struggles with regex went on for years.
Eventually, I’d run into scenarios where I was at an impasse. Situations like:
Writing a signature to detect an exploit kit, but it involved matching a complex HTTP URI string — I feel like the malware authors do this just to mess with me.
Searching through authentication logs to match specifically formatted usernames, like steve7.smith.sales — seriously, why did sales hire so many Steve Smiths?!
Parsing threat intelligence feeds to match and remove problematic indicators — I’m looking at you 127.0.0.1.
I’d just brush my shortcoming off and focus on other problems or spend a lot of time manually digging through data in my terminal or Excel. When I absolutely had to figure something out I would dig around until I found a similar regex and try to modify it until it fit my use case. I didn’t understand what I was doing or what I created. It got me through, but it also resulted in a lot of wasted time and false positives.
I was in denial, but I eventually realized a harsh truth.
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