What does Machine Learning Mean for Cyber Security?
Machine learning has already proven itself to be a disruptive technology. It is now used to support the likes of bank fraud identification, driverless cars, speech recognition and advanced cybersecurity threat detection.
Businesses now understand the tremendous value machines based learning can deliver. As a result, they are investing heavily in its development.
Benefits of Machine Learning within Cybersecurity
Machine learning is helping businesses to move towards a more proactive, as opposed to reactive, approach to cybersecurity. Therefore, they will no longer be one step behind attackers, reducing the risk of a data breach. This is particularly important due to the recent implementation of the GDPR.
In order to keep up with cyber threats, as much data as possible is collected in order to gain insights into their causes. However, organisations often find it difficult to keep up with the analysis of such large volumes of data. Machine learning automates this analysis so that we can get more out of the data that is collected and give organisations an advantage over attackers.
It has been reported that up to 90% of cyber attacks can be pinned on human error or inaction. By using machine learning, the likelihood of human error causing a breach is significantly minimised, improving overall security.
Challenges with Machine Learning
As all computer programmers will know- it is simply not possible to get the right answer from the wrong information. However, identifying ‘garbage’ data can be a highly complex task. This is due to the difficulty of ensuring that your machine learning model only recognises that patterns that actually matter, rather than coincidental correlations.
Machine learning models work by recognising even the most subtle correlations within training data. This, therefore, allows them to learn ways of recognising new things such as fraud or spam. However, this acute capability can backfire and cause unexpected results.
Spurious correlations can cause models to learn the wrong things – and Big Data is full of spurious correlations. This means that simple coincidences can cause machine learning systems to behave in undesirable ways. For example, filtering useful emails into your spam folder or recognising fraud where it doesn’t exist.
Machine learning technology is non-deterministic. This means that we can only say what a model is likely to do in real life scenarios, with no guarantee of the actual results we will see. Because of this, deciding whether a software is working correctly involves first deciding what correct behaviours will look like, and testing the software from there. The less thorough this testing is, the bigger the resulting flaws are likely to be in real life scenarios.
So, the issues with machine learning, in a nutshell, are: A model doesn’t always know exactly what you’ve told it, and you haven’t always told it what you think you have.
How can Your Business Benefit from Machine Learning?
In order to get the most from machine learning, whilst minimising the chance of error, we recommend implementing a cybersecurity system with deep learning built in. A great example of this is the Sophos Product Portfolio, which helps businesses to stay one step ahead of attackers by making use of machine learning to bolster security, identifying new threats far more quickly and efficiently than any human could. Click here to find out more about Sophos security solutions, or contact our team today.