Ever wondered what happens when you combine one of the strongest technologies of its age with the biggest concern of our age?
With technological advances happening ever so rapidly, cybersecurity is becoming more and more important to make sure everyone’s online presence and data are full on protected.
Recap: what is machine learning?
In case you missed it, machine learning is one of the leading artificial intelligence technologies of its age.
As you can guess from the name, machine learning focuses on the use of data and algorithms to imitate the way that humans learn.
It uses these data and algorithms to be able to predict outcomes that are not necessarily within their programmed capabilities.
So… what brings machine learning here?
We know what you’re thinking: machine learning and cybersecurity don’t really sound like a match, but they turn out to be one definitely made in heaven, and all for good reason.
how machine learning and cybersecurity work together like a charm
1. Threat detection
Machine learning can be used to go through millions upon millions of files to detect and even predict any threats or hazardous files in a fairly early stage.
2. Discovering network vulnerabilities
If you can’t beat them, join them, right?
Some companies use Machine Learning to imitate a cybersecurity attack to be able to locate weak links and vulnerabilities within their networks so they can start working on them.
They can also use it to detect unusual files and/or user behaviour and process how they occur to prevent them in the future.
3. Handling threats
Not only does machine learning help detect malicious files and network vulnerabilities, but it is also capable of automatically handling the aforementioned threat without any hassle.
What can be an obstacle to the success of machine learning in cybersecurity
Oh, to live without any obstacles would be so… unrealistic.
While machine learning in cybersecurity sounds magical and dreamy, it is not that easy because of, well, obstacles.
So what is standing in machine learning’s way?
1. Accuracy requirements
With the amount of data being processed by an ML model, it is not uncommon that there are some accuracy errors to be expected.
2. Access to training data
In order to create a successful, efficient ML model, huge amounts of data are needed to feed it.
As if data availability wasn’t already an issue in the AI world, the availability of malware-related data specifically is a whole other story.
This is because such data is considered sensitive and, hence, is not available because of privacy concerns.
3. Talent scarcity
We know it’s not that easy to find ML experts, and it definitely is a pain to find security experts, so you can do the math there; finding someone who does both or a team where both coexist and work successfully together is a real challenge.
4. Machine learning security
It only makes sense that if you’re using technology to make sure your data is secure that the technology itself is secure, right?
Using machine learning for security is a no-brainer, but it definitely comes with its own challenges, and we’re gonna walk you through everything you need to know about this dynamic duo.