Combat Cyber Threats with Machine Learning

Cybercrime is evolving as quickly as technology is. To counteract cyber threats and attacks, it’s imperative for businesses to stay one step ahead.

  • 69% of cybersecurity leaders say staying ahead of attackers is a constant battle and the cost is unsustainable.
  • Data breaches exposed 36 billion records in the first half of 2020
  • Ransomware remediation costs, including business downtime, lost orders, operational costs, and more, grew from an average of $761,106 in 2020 to $1.85 million in 2021
  • Research published by Varonis says that on average, only 5% of companies’ folders are properly protected against cybercrime

There are multiple technologies that are aiming to keep-up to support the fight against cyber-attacks, one of the most rapidly growing in use and advancements is machine learning.

In the last few years we have seen machine learning becoming a crucial part of the cybersecurity landscape. Find out why in this Whitepaper.

This Whitepaper covers:

  • What Is Machine Learning?
  • The most common use cases of ML in Cybersecurity
  • How Does Machine Learning Work?
  • Data collection and exploration– Observability Of The Environment
  • Data features extraction and data preparation – Machine Learning Analytics
  • Training the model – Machine Learning Implementation And Interpretability
  • How to Improve Analytics Capabilities

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