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