Charles Herring's blog

Lessons in InfoSec Graph Theory

Charles in a suit and tie
Relationship Map

One of the areas we research heavily at WitFoo is how to reduce the number of investigations our customers have to perform each day. Internally, we call this the “n” problem. Another area of focus is how to reduce the amount of time our customers spend on each investigation. We refer to this as the “t” problem. The lower we drive and t, the more work our customers can accomplish each day.

People > Machines (Part Five)

On top of Mt. Fuji
Cyber Ghost

Better detection mechanisms through algorithms (code) & machine learning (pattern recognition) are valuable tools to the human responders. Playbook Automation can reduce the routine and certain tasks an analyst must perform so she can focus on what is important.

People > Machines (Part Three)

Smirking at the camera
Cyber Ghost

Computer scientists love the idea of artificial intelligence (AI). It is the centerpiece of many mainstream science fiction works. It’s also a preferred buzzword of lazy vendors and marketers. Until computers can convince (trick) a reasonable human being that they are living beings (Turing test) all claims of AI are misleading at best. In this installment, I won’t debunk the types of claims of AI. We will examine the difference between how computers and humans think and the implications of the differences.

People > Machines (Part Two)

Long Live and Prosper with Jon
Cyber Ghost

When I was learning how to troubleshoot and repair electronics in the Navy, I would sometimes challenge one of the instructors on how something worked. If I delved into a complicated subject I was often told it worked on “FM” which meant f***ing magic. That rarely stopped me however, and I often found the concepts were not overly complicated, just not directly relevant to my training.

There is some FM in information security that I’d like to demystify as we examine how tools can enable and not hinder the craft. We’ll examine algorithms and machine learning in this installment.

People > Machines (Part one)

Fuji torii

Cybersecurity Incident Response has only been a part of human history for a couple of decades. Over the short course of time, industry leaders, analysts and vendors have put a heavy focus on the importance of technology solving problems within the craft. In this series, we will examine the preeminent importance of the craftsman over his tools and the role tools should play in making the world safer.

Evolution of Data

Charles and Mai at Nanny's for Christmas
Evolving Data

First, the nature of evolution discards noise. Much like the concept in biology, only fit, useful facts survive the evolution process. When exposed to more complex systems, noise goes the way of the dodo bird. A “possible SQL injection attack on MySQL” event becomes irrelevant when vulnerability reports show the targeted server isn’t running MySQL. As data becomes a more mature, evolved object the irrelevant events fall away.

Failure Reports

Charles in Galena
Server Rack Frustration

When I was leading the Network Security Group at the US Naval Postgraduate School, I was overwhelmed with the degree of failure we experienced. The amount of events, complexity of investigations and immature security infrastructure created an environment of perpetual failure. After gathering the basic business metrics I discussed in Metering Incident Response 101 I decided it was time to push the problem up the chain of command.

Metering Incident Response 101

Viking at Epcot
Business Metrics

A core tenet to success in any endeavor is defining, collecting and analyzing core metrics. Incident Response teams can only develop plans that lead to success when it can be defined and metered. Understanding and collecting two key metrics can aid in defining, metering and reporting on success.

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