Cyberspace is constantly growing and evolving, and that means our methods to quickly respond to threats need to be advancing, too. It’s no secret that conventional methods like legacy systems and SIEM solutions can’t keep up – and that’s where data science is now starting to fill the gap.
Data science is the secret to next-generation cybersecurity solutions.
Though often overlooked, data science powers next-generation cybersecurity and, more specifically, the cyber-situational awareness needed to predict and stop cyberattacks. So how do you gain that awareness? Here’s a look at five steps commonly used to leverage full cyber-situational awareness and gain the most comprehensive look at your data:
Data Fusion. In a nutshell, data fusion generates a plethora of data stored in data servers. The goal of fused data is to turn unstructured data – from routers, servers, mobile devices, etc. – into something more comprehensive.
The three levels of data fusion walk the user through connecting raw data to other data, gaining a better understanding of the data, and weighing those insights across a set of tools. VirusTotal, a commonly used free malware scanning service, is a good example of data fusion.
Data Mining. This step further breaks down the data and insights gained through data fusion. The goal here is to distinguish signals from noise and identify useful information to discover knowledge about the data. But that’s not always easy. You’ve got to take rigorous mathematical approaches to reveal valuable insights. In layman’s terms? You better dust off that old statistic book.
Feature Engineering. This is where the plethora of data starts to become manageable – at least that’s the objective. Feature engineering is the process to extract, enrich, aggregate and select significant features to represent the originally high-dimensional data set with little information loss.
Cyber-situational awareness really starts to take shape here, and you can take three approaches to gain some useful results. Take a deeper dive into all three in ThreatTrack’s recent white paper.
Predictive Learning. Now it’s all coming together, and you’re starting to get the context and insight you need to better predict and stop cyberattacks. Three different views of cyber-situational awareness are needed to truly take full advantage of this step, though, including:
- Situation recognition: Provides information about the status, attributes and dynamics of relevant elements within the network.
- Situation comprehension: Focuses on understanding network structure and critical assets, communication patterns, users’ behavioral profiles, etc.
- Situation projection: Addresses the projection of situation into the future. The insights from the first two provide a starting point for this projection.
Data science is a rich discipline that can be used to tackle and solve many cybersecurity challenges – many of which are impossible to solve with traditional methods. The next-generation of cybersecurity solutions must be built on situational awareness to proactively defend against threats.
Stay tuned for a closer look at each of the three approaches to cyber-situational awareness next week. In the meantime, check out ThreatTrack’s recent white paper to take a deeper dive into data science.
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