Within the broader discipline of dark web threat detection via open-source intelligence (OSINT) is a practice known as ‘threat actor attribution’. Cybersecurity experts willing to make use of it can do a better job identifying previously unknown threats, be they individuals, groups, or even rogue nations.
Threat actor attribution is becoming more critical to cybersecurity with each passing day. The sheer volume of potential attacks, combined with a growing number of individuals and groups willing to launch such attacks, demands that security experts learn everything they can about their adversaries.
The Basic Concept
There is nothing complicated or theoretical about threat actor attribution. In fact, the concept is fairly simple. Threat actor attribution is the process of identifying threatening individuals and groups by analyzing critical data. That data includes:
- Technical Evidence – Every activity leaves evidence behind. That includes every illicit transaction, every data dump, and every successfully launched attack. The technical evidence is valuable to security investigators.
- Behavioral Patterns – Threat actors can be partially identified by their behaviors. Tracking behaviors and linking them to outcomes creates links allowing investigators to draw correlations between known threat actors and future events.
- Broad Contextual Intelligence – Event context goes a long way toward understanding it more fully. Likewise, the contextual intelligence that comes from data correlations helps security experts better understand who they are dealing with.
Threat actor attribution is an exercise in connecting the dots. That’s really it in its simplest foreign. Fortunately, organizations like DarkOwl facilitate threat actor attribution with dynamic tools that make the most of data sources and analytics.
How They Pull It Off
Security experts utilize a number of tools to pull off consistent threat actor attribution. The first on the list is data collection and enrichment. They gather information from network traffic, logs, malware samples, incident reports, and threat intelligence feeds. The gathered data is then normalized and enriched with context.
Technical analysis is another important tool. It involves examining technical evidence left behind following an incident. Such evidence includes things like malware signatures, IP addresses, and clues to infrastructures and tool sets.
The following three tools are also important to threat actor attribution:
- Behavioral profiling utilizing TTP analysis
- Contextual intelligence considers everything from motives to timing
- Framework-based classification using models like the Diamond Model and Unit 42 Framework
Each of the tools contributes valuable data to a threat actor’s overall profile. By attributing known data points to known threat actors, security teams can begin putting together a more comprehensive picture of the current threat landscape.
Putting It All Together
Putting it all together requires combining multiple evidence types with solid human analysis. Security experts can use a combination of their own experience and AI/machine learning tools that make correlating data faster and more accurate. But in the end, it is sound human judgment that determines accuracy.
For this reason, automated threat actor attribution is no substitute for expert human analysis. Automated tools are useful, but human analysts make the final call.
Why Threat Actor Attribution Matters
It’s easy to look at threat actor attribution as just another innocuous skill set threat intelligence providers try to sell their customers. But the truth is that it is anything but. Threat actor attribution matters because hackers, cyber criminals, and rogue nations are becoming ever more sophisticated in their TTPs.
Being able to connect the dots between threat actors and events paints a clearer picture. It helps security teams better understand what they are up against so that they can be proactive in their defensive strategies. Ignoring threat actor attribution makes little sense.