Few know it, but AI has been around for the better half of a century, machine learning for decades and deep learning for a few years now, but consumers and companies struggle to identify the subsets of AI and what these distinctions mean. Many are unclear about the difference between machine learning and deep learning, and the significance that this distinction has in keeping their systems’ users and data safe.

Machine learning and deep learning are neither synonyms nor competing technologies, the variation between them making all the difference for companies attempting to harness the power of data. Those interested in technology need to be mindful of incorrectly applying terms and, more importantly, focusing on implementing methods that bring value to practical situations that will benefit from the technological edge.

With all this surrounding confusion regarding machine learning versus deep learning, we bring a clear distinction between the two; we demonstrate their distinctive objectives and how it lends itself to the cybersecurity industry.

Back to the Basics: Defining Deep Learning and Machine Learning

Deep learning is a subfield of machine learning, which is itself a subset of AI. Deep learning tries  to mimic the brain’s behavior in the same way that the human brain learns (by being agnostic to the input) – taking in all the data and learning from it continuously and intuitively.

Deep learning is distinguished from machine learning in that it is the first, and currently the only, method that is capable of training directly on raw data. Traditional machine learning requires feature engineering, where a human expert effectively “guides” the machine through the learning process by extracting the features that need to be learned.

As machine learning is based on human analysis, it’s highly limited and relies solely on specific known use cases. In contrast, deep learning can dive into the raw data of the file without explicitly being told to pay attention to engineered features and analyzes all the available data.

Cybersecurity on Steroids

In its application to cybersecurity, deep learning is able to analyze millions of different possible files and attack vectors. As the training dataset gets larger and larger, the algorithms continuously improve. It’s this unique ability to pick up on patterns and nonlinear correlations in the raw data that are too complex for any human or traditional AI to pick up on that gives deep learning its inherent value.

Deep learning’s application to cybersecurity provides numerous valuable outcomes:

  • Deep learning-based model can produce a much higher detection rate and lower false alerts, effectively eliminating alert fatigue and reducing costs.
  • This is the case even for new and previously unseen cyberattacks. The broad coverage of attack vectors provides verdicts that are more accurate than the best traditional machine learning solutions available.
  • Deep learning provides the ability to scan any type of file statically in pre-execution mode. Unlike detection and response tools that only act when something is already running, which in most cases is too late.
  • A platform-agnostic solution, deep learning is able to assess for unlimited types of attack vectors, while it provides protection irrespective of the operating system or the device (be it mobile, endpoint, server, network). Doing so, from a single unified platform.

To learn more about how deep learning has been applied to cybersecurity and the profound benefits that have been gained, watch the webinar.

Nadav Maman, CTO and cofounder, Deep Instinct