White Paper

Automating Threat Defense: Using Machine Learning To Prevent Modern Cyberattacks

Source: Webroot
Protect SMBs From Cyberattack

What does “Machine Learning” Mean?

As technology continues to advance, managing and analyzing massive data sets just can’t be accomplished by humans alone. It requires huge amounts of memory and storage, as well as the high-speed processing power of the cloud. 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 breaches from occurring successfully in the first place.

How Does Machine Learning Work?

The algorithms determine how to interpret the 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 actions.

Tuning a machine learning model is complex and challenging work, and there’s no substitute for time and experience when developing an accurate machine learning model. The key is continually refining the algorithms. Initially, data is fed into the model for training while human intelligence and analysis help to fine-tune it. This process, referred to as “supervised machine learning”, takes highly skilled machine learning experts, data scientists, and statisticians, all working to train and test the algorithms over time. While the refinement process can be very involved, it increases both speed and accuracy, and enables machine learning systems to produce truly meaningful and actionable results.