What I've Learned as a Part-Time Cyber Threat Analyst Using Anomali Match

A few months ago I wrote a post detailing how Anomali Match helped me to identify a malware threat to my home network. Many have since emailed me asking how they can do the same (please keep them coming!).

Since writing that post, my router has generated millions of logs that have been ingested by Anomali Match (thankfully still no major threats). As a new "threat analyst" for my family's home network I've learned a number of things along the way, especially the challenges and frustrations when it comes to performing security investigations.

In the interest of sharing my, albeit basic, knowledge to the community (I am a Product Manger by trade!), I wanted to highlight a few things I've come up against, and the what I've found most useful.

New threats <= active threats?

Knowing a new threat has been observed is good. Knowing where a threat is in the Kill Chain is much more useful. Learning where the threat is in the process of achieving its objective allows me to not only defend against it, but also to understand the activity of the threat prior to it becoming known (what has been compromised).

Threat intelligence products are great at identifying threats as they happen. For example, Anomali ThreatStream integrations with SIEM products -- Arcsight, QRadar, or Splunk to name a but few -- can match recent log data against thousands of threat indicators.

However, this only answers the first of the two questions I want to ask as an analyst once a threat has been identified;

  1. Is our network impacted/compromised? What’s our exposure? Which specific assets are impacted?
  2. How widespread is the impact? How far back does it go?

As threats, by their very nature, are reported after-the-fact, there can often be a delay (sometimes weeks) before it is shared more widely. When a threat is identified, it is vitally important to know its behaviour and what it has potentially breached in the days it was left unreported.

Big data, big numbers

Considering the data from my home network noted in the previous blog post, the calculations required for matching logs to threat intelligence result in some big numbers:

100,000 logs per day x 1 year of data x 10 indicators = 365,000,000

That's three hundred sixty-five million calculations that need to be performed for just one investigation!

At enterprise scale the 0's dramatically increase:

1 billion logs per day x 365 days x 3 years of data = 10 trillion (10,000,000,000,000) matches need to be performed, for one investigation!

Existing security log repositories (I'm using Splunk) are not designed to process queries matching such a large intel database against huge volumes of historic data. Not only are they limited by the ability to process archived data but often the cost of storing such data means much of it is filtered, and thus impossible to forensically search against.

How Anomali Match helped me (answer questions 2 & 3)

I wasn't the only one suffering some of these pains - our own security team here at Anomali experienced these problems day-in-day-out. In search of a solution we built Anomali Match. Some of the functional and design goals of the product included:

  1. The ability to store years of log data online even from highly noisy sources e.g DNS traffic — trillions of logs (without filtering what gets stored due to costs)
  2. The ability to analyse these logs against millions of threat indicators in seconds -- not minutes, hours, days, or even weeks (both in real-time and retrospectively)
  3. The ability for analysts to be more effective, more efficient, and more accurate in detecting and remeditating threats (better worflows for threat intel)

It's all about time-to-resolution

Analysts want to focus on the most serious threats, not more threats in their already never-ending workload. Anomali Match helps me to do this by comparing threat indicators -- domains, URLs, emails, file-hashes etc. -- against new and historic data from all devices in my home network, automatically. I can see what has been compromised, when it was comprimised and if the threat made any lateral movement. Within an hour of malware being identified (as in the previous post), I can assess the damage, detected affected assets, and take measures to secure them.

Learn more

This post covers Anomali Match's real-time and forensic capabilities. It can do much more. Check out the Anomali Match product page on our site to find out more about what it can do.


Anomali Cyber Threat Intelligence Malware SIEM ThreatStream

Related Content

Get the Anomali Newsletter

The latest Anomali updates and cybersecurity news, delivered straight to your inbox each month.