Machine Learning Data on The Cutting Edge of Cybersecurity Efforts

Machine learning is making a major impact on the development of cybersecurity. Here's why that's so important.

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January 17, 2019
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Cybersecurity professionals have a hard job. Not only are they tasked with developing solutions to constantly changing risks, but they cannot know what those attacks will consist of until after they’ve already been launched. Though cybersecurity experts can certainly offer insights into what digital dangers may come next, these predictions are limited and make proactive solution development challenging. Luckily, by increasing the use of machine learning, cybersecurity groups are able to take advantage of advanced pattern recognition technologies to better determine what attacks are on the horizon.

Driven By Data

On the surface, it seems like cybersecurity professionals would be focused on designing stronger barriers to attack and establishing firmer encryption standards, but at its core, the field is driven by data. In particular, large amounts of network data is sorted to differentiate between normal network traffic and threat activity. This data can help developers craft better barriers against intrusions, assess the consequences of an attack, and boost post-attack recovery. Simply put, cybersecurity development work wouldn’t be possible without an influx of data, and much of that data is collected and managed via machine learning practices.

One reason only machine learning technology is capable of assessing network traffic for threats and anomalies is that they must process months of traffic data to identify underlying patterns. If a human programmer were to do this, even with digital assistance, it would take years and then they would still need to build a solution that could identify new incoming threats based on that information. When machine learning approaches the problem, though, not only is it able to complete the pattern recognition phase swiftly, but then the machine itself is capable of identifying new risks in real time.

The Advance Guard

In addition to speed, one of the primary advantages of using machine learning for cybersecurity development is that, despite the fact that businesses generally understand the importance of proactive security practices, without AI assistance, most businesses can’t actually execute such a strategy. This isn’t for a lack of trying, of course; even the most assiduous human workers simply can’t keep pace with network traffic or interpret data as quickly as computers can. Businesses that operate with a machine learning-backed security system, then, can put basic security initiatives in place, but these will never be advanced enough to be considered truly proactive.

Introducing Infrastructure Support

To aid businesses that are invested in advancing their cybersecurity practices, machine learning experts have stepped up to the plate and are now offering Infrastructure-as-a-Service (IaaS) programs that bring AI into offices at all levels, democratizing access to such technology. Such security practices involve monitoring network activity, to be sure, but also address privileged access management (PAM) concerns, as internal breaches are a leading cause of data theft. At present about 60% of companies still manage these access credentials manually, but by upgrading their management using IaaS, companies can monitor access more closely, enhance multi-factor authentication practices, and monitor system use by privileged users to ensure best practices. Stopping internal breaches is a surprisingly challenging process.

Another key application of machine learning and infrastructure development for cybersecurity is in the area of mobile technology. With fewer workers onsite and remote access increasingly important, being able to manage how remote devices interact with primary network systems is of growing importance – and that’s why the Department of Homeland Security (DHS) is researching mobile threat detection (MTD).

Regarding AI, DHS research is interested in several different applications. These include behavioral profiling, code emulation, and intrusion protection, among others. All of the applications central to this technology, though, are designed to protect high-value data no matter where it’s accessed from or used.

Innovative Problem Solvers

While there are plenty of established machine learning solutions working in the cybersecurity space, one of the best ways to innovate in this area is by turning to hackers and other independent groups, and that’s where hackathons come into the picture. Hackathons, task-based gatherings at which independent developers and coders work to test new ideas and solve problems, are on the frontlines of cybersecurity today.

So what happens at a hackathon? One hackathon sponsored by Wallarm through the machine learning platform Kaggle, for example, wants participants to develop more nuanced solutions for identifying malicious network activity, and Wallarm is hardly alone. A growing number of hackathons offer competitive, low-cost ways for businesses to acquire new solutions, while offering coders small prizes. It’s much more cost-effective than hiring a coder, and often more innovative.

Standard computers and security experts are easily overwhelmed by the amount of data involved in cybersecurity work, making machine learning solutions vital to their success, but those solutions need to a collaboration between human developers and their machines. As computers lead the way by processing massive amounts of information, our systems are becoming safer. The next step is expanding access to these advanced systems to businesses and organizations of all sizes. This is the beginning of something new and exciting for cybersecurity.