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Using Machine Learning to Prevent Modern Cyberattacks

Machine learning is a process that applies advanced mathematical algorithms and powerful computing capabilities to quickly and efficiently analyze those data sets and identify patterns. In the case of IT security, correctly determining patterns helps create accurate predictions and detect behaviors that may be associated with malware or other attacks. Doing so in real time, or as near to real time as possible, can help prevent malware from infecting endpoints entirely.

Why should MSPs care about machine learning? There might not seem like there’s a direct connection, but it’s the difference between reactive and preventative security. By utilizing endpoint security solutions that work in real time (preventative) instead of signature-based security (reactive), MSPs can ensure that they reduce client infections, thereby saving time, money, and energy – and, most importantly, boosting profits.

How does machine learning work?

Algorithms determine how to interpret data, and process it to produce predictable outputs. Machine learning then helps to decipher the data to identify the patterns, make sense of them, and enable security tools and personnel to take action.

Today, the most advanced machine learning platforms incorporate human feedback loops or active feedback, and active learning. Through active learning, they can become self-improving, and, essentially self-evolving. The active learning, massive scale, and accurate classifications can also be employed to drive predictive analytics, combining or contextualizing information on different threat types across disparate systems and domains to accurately predict where new threats will originate.

This is the bottom line for MSPs. Blocking or monitoring these potential threat sources enables service providers to be more proactive than ever when anticipating attackers’ next moves.

More accurate threat detection

For security providers, sophisticated machine learning provides fast and accurate threat detection, including zero-day and previously unknown threats. Advanced heuristics and rules allow machine learning models to help determine in near real time if a file, URL, IP, or application is a threat, and then communicate that information broadly.

Despite an ever changing threat landscape, machine learning enables very high detection rates over time. Machine learning technology is now emerging as a critical component across all of the various domains of cybersecurity.

Machine learning can power next-generation endpoint protection, mobile protection, threat intelligence, web security and network anomaly detection offerings. It enables threat activity across multiple security domains to be contextually associated in real time.

The bottom line

While a lot of professionals in cybersecurity hear machine learning today and dismiss it as a buzzword that they’ll have to learn the details of in five year, they’re not seeing the big picture.

The next generation of security is already here, and it’s powered by machine learning. To put it simply, solutions that are powered by machine learning and artificial intelligence ARE the future of cybersecurity. The sooner MSPs and other service providers start leveraging the considerable powers of machine learning, the sooner they’ll be on the road to profitability.

So, why not take a free 30-day trial of Webroot SecureAnywhere® Business Endpoint Protection with the Global Site Manager? See what the lightest, fastest protection on the market that utilizes machine learning can do for you.

Blog courtesy of Webroot. Read more Webroot guest blogs here.

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