In a world where society is driven by information, data science has gained solid ground over the past years for its ability to separate the wheat from the chaff. Its predictive power is now being explored in the context of cybersecurity. After all, efficient threat protection requires gathering and interpreting the enormous amounts of traffic generated to and from one’s network.

But to truly makes sense of internal logs and act accordingly, an external perspective about historic and most recent threats is required. This is where data science can get the support of external threat intelligence sources to deepen the analysis.

IP addresses, for example, can be correlated with known threat databases using data science techniques and be classified according to their risk scores, which can be measured by tools such as Domain Reputation Intelligence API.

There are tons of other ways by which data science can change a security professional’s approach toward threat intelligence analysis, though. In this post, we’ll explore some of them.

How Data Scientists Can Help Cybersecurity Professionals

Data scientists have learned a few things that security service providers can benefit from over the years. This includes:

Making Sense of Big Data the Traditional Way

Data scientists are trained to handle so-called “big data.” They are able to sift through massive data sets and present their findings as easily decipherable charts and tables that anyone in the company can use to make informed decisions. These findings can also figure into actionable strategies for business development and improving profitability.

Data scientists are considered spreadsheet application experts. They can, therefore, use enterprise-level data feeds that come in the form of comma-separated values (CSV) files as additional sources of inputs for their analyses.

Targeting Abnormalities

Data science’s primary focus is about building a structure from troves of data and naming them to make comparisons between normal and abnormal patterns using machine learning (ML) algorithms. In short, data scientists are trained to spot anomalies.

One of the manners they can accomplish this from a cybersecurity standpoint is by detecting the behavioral patterns of customers. For instance, multiple failed login attempts, which deviates from the usual customer behavior, should point to a potential attack.

Data scientists can then comb through customer information such as IP geolocation data to detect whether it matches the registered IP address of the customer on record. If it is proven to come from an unauthorized IP address, then security providers should take action on blocking the said “customer.”

Compare and Integrate All Available Data

As touched on earlier, data scientists can help security professionals make sense of a variety of internal and external information through comparisons. First, they need to identify which traffic from internal sources is malicious by counterchecking with publicized attack indicators of compromise (IoCs). That rules out all known threat sources from getting network access.

A WHOIS database can then be used for a deeper investigation of domains, email addresses, and individuals connected to known threats. All of these can then be blocked to prevent even unknown threats from entering an organization’s network.

Not all data sets are created equal. Several remain unstructured and semistructured, which can be difficult to process. WhoisXML API data feeds such as WHOIS Database Download are well-parsed and well-structured, allowing for easier integration into widely used data science tools.

Automating Tasks

Most security issues come at a speed that most teams can’t keep up with. Small incident response teams would primarily have a hard time sorting through tons of data. To cope, they need to automate data management and analytics.

Many data scientists have artificial intelligence (AI) and machine learning (ML) knowledge that can help. Supervised ML, outlier detection, survival analysis, recommendation engine development, and adversarial learning are just some of the competencies that data scientists can apply to integrate data feeds and APIs into organizations’ security solutions to enhance their performance.

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Going back to the question we posed, the answer is leaning toward yes. Data scientists can help security providers spot abnormalities, make sense of data feeds, and automate tasks.

Employing them, however, is not enough. Data scientists need threat intelligence to perform their jobs. Without information to analyze beyond an organization’s own internal logs, they can’t come up with actionable threat intelligence that today’s security solutions require to combat unknown and advanced threats.