A significant amount of resources goes into monitoring the security of an organization. The data that needs to be analyzed is incomplete, unstructured, and messy. On top of that, attacks are becoming more intricate and stealthy. It can be very easy to miss a tiny detail that could have been quickly discovered by utilizing machine learning.

Automating Tasks

One of the biggest benefits machine learning gives to security analysts is its ability to automate repetitive tasks. As security grows more and more complex over time, it gets more difficult to maintain with the standard IT team with the rapid expansion of mobile devices, cloud services, and larger networks. Hackers are getting more intelligent as well, meaning that businesses are in jeopardy of data breaches unless they ramp up exponentially.

Many businesses struggle with keeping up because they continue to add more and more people to their workforce to try to remedy the problem, but it isn't helping the root of the issue. Instead, they should consider to relinquish some of their control and utilize big data and machine learning automation to alleviate some of their stress. Machines are capable of searching through thousands of queries to identify any anomalies much faster than a human could, and with higher accuracy. It can also pinpoint where any weaknesses are and at times, automatically repair them.

The idea is to use analytics to observe behavior and establish what is ‘normal’. Normal of course is an evolving state, so continuous machine learning is involved to understand the path from anomalous behaviors to genuine threats. Using normal as a base, the focus shifts to behavioral analysis combined with anomaly detection, with the goal of spotting and predicting potential events at an early stage.”

- Rob Rich, Managing Director, Insights Research, TM Forum

Filling in Talent Gaps

You would be hard pressed to find a security analyst that isn't entirely overwhelmed with mounds of data to sift through. Pair that with a gross lack of available talent in the sector makes it even more difficult for those that are watching, predicting, and protecting their security environments. 

A single slip-up or missed piece of data can cause a cataclysmic security issue that could take hours, days, or weeks to remedy without the use of machine learning. A well-trained machine is capable of many things: detecting unusual activity on the network and immediately taking action, finding malware in downloads and putting it in quarantine before it even has the opportunity to execute, or anomaly detection, which is explained in the section below.

Anomaly Detection

Nearly all machine learning applications that are utilized by security experts have some types of anomaly detection. Anomaly detection is exactly what it sounds like - when the machine recognizes a standard pattern and grows comfortable with it -- suddenly the pattern changes and it sets off an alarm within the system.

Take for example someone's gait. While it can vary depending on height, age, gender, weight, and physical differences, you typically notice when someone is limping or "walking funny". It's an anomaly. Much alike a human would notice something strange about an individual, the software knows when something is wrong.

An example of anomaly detection for security is when a phone gets stolen. The thief that has taken the phone will have a distinct difference in usage. They aren't familiar with the phone's setup, and it takes an average of fifteen swipes or taps before they are able to get where they want when the phone's owner would only need two to three.

Despite its usefulness, machine learning isn't a one-size-fits-all solution. Utilizing it requires a deep understanding of the underlying systems that run it. Machine learning's use in security is a bit niche, but the information it offers is tremendous.