When it comes to antivirus software, some vendors are hailing machine learning as the silver bullet to malware — but how much truth is there to these claims?
In today’s post, we’re going to take a look at how machine learning is used in antivirus software and whether it really is the perfect security solution.
How does machine learning work?
In the antivirus industry, machine learning is typically used to improve a product’s detection capabilities. Whereas conventional detection technology relies on coding rules for detecting malicious patterns, machine learning algorithms build a mathematical model based on sample data to predict whether a file is “good” or “bad”.
In simple terms, this involves using an algorithm to analyze the observable data points of two, manually created data sets: one that includes only malicious files, and one that includes only non-malicious files.
The algorithm then develops rules that allow it to distinguish the good files from the bad, without being given any direction about what kinds of patterns or data points to look for. A data point is any unit of information related to a file, including the internal structure of a file, the compiler that was used, text resources compiled into the file and much more.
The algorithm continues to calculate and optimize its model until it ends up with a precise detection system that (ideally) doesn’t classify any good programs as bad and any bad programs as good. It develops its model by changing the weight or importance of each data point. With each iteration, the model gets slightly better at accurately detecting malicious and non-malicious files.
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