Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Machine-learning algorithms however use statistics to find patterns in massive* amounts of data. And though data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.
therefore, Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.
It’s impossible to deploy effective cybersecurity technology without relying heavily on machine learning. At the same time, it’s impossible to effectively deploy machine learning without a comprehensive, rich and complete approach to the underlying data.
Why ML in Cyber Security Become So Important (Critical)?
Various reasons. With ML In Cyber Security, cybersecurity systems can thus analyze patterns and learn from them to help prevent similar attacks and respond to changing behavior. thus, It can help cybersecurity teams be more proactive in preventing threats and responding to active attacks in real time. therefore, It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically.
In short, machine learning can make cybersecurity simpler, more proactive, less expensive and far more effective. But it can only do those things if the underlying data that supports the machine learning provides the complete picture of the environment. As they say, garbage in, garbage out.
Approaches to Solving ML Tasks
Past Approach
- Supervised learning. Task Driven approach. so, First of all, you should label data like feeding a model with examples of executable files and saying that this file is malware or not. though Based on this labelled data, the model can make decisions about the new data. The disadvantage is the limit of the labelled data.
- Ensemble learning. This is an extension of supervised learning while mixing different simple models to solve the task. There are different methods of combining simple models.
Present Approach
- Unsupervised Learning. Data Driven approach. The approach can be used when there is no labelled data and the model should somehow mark it by itself based on the properties. Usually it is intended to find anomalies in data and considered to be more powerful in general as it’s almost impossible to mark all data. Currently it works less precisely than supervised approaches.
- Semi-supervised learning. As the name implies, semi-supervised learning tries to combine benefits from both supervised and unsupervised approaches, when there are some labelled data.
Future Approach
- Reinforcement learning. Environment Driven approach can be used when the behavior should somehow react to the changing environment. It’s like a kid who is learning about the environment by trial and error.
- Active learning. It’s more like a subclass of Reinforcement learning that probably will grow into a separate class. Active learning resembles a teacher who can help correct errors and behavior in addition to environment changes.
Role Of ML In Cyber Security
The role of ML In Cyber Security in protecting people’s data in a digital world is growing all the time, and for good reason. Machine Learning is capable of constantly analyzing immense amounts of data in order to detect any kind of malware or virus that could indicate a security breach, then adjusting to protect against them.
Here are just some of the benefits Machine Learning offers cybersecurity for businesses and consumers:
- Thorough, hands-off system scanning for data breaches, malware, and more
- Endpoint protection through quick remediation
- Fast analysis of large amounts of data
- Adjustments without the need for expert input
These benefits of Machine Learning for business operations enhance the security of data with fast and effective monitoring and prevention of cybersecurity breaches. Without Machine Learning, cybersecurity programs take time to catch and resolve. Security experts can’t monitor systems on the scale that Machine Learning AI can.
ML And Cyber Security
- Machine learning for Network Protection.
- ML In Cyber Security for Endpoint Protection.
- ML for Application Security.
- Machine learning for User Behavior
- ML for Process Behavior
Machine Learning Cybersecurity Books
so, If you want to learn more about machine learning in cybersecurity, here are books that can help:
- AI for Cyber Security by Cylance(2017).
- ML for Penetration Tester by packt(2018).
Conclusion:
There is so much talk about machine learning and AI that business leaders can be excused if they feel like they are on hyper hype alert. When it comes to cybersecurity, however, the potential for ML In Cyber Security to have a dramatic and lasting impact is real. But only for companies that are forward-thinking enough to take care of their data first.
Written By: Deepak Rathour
Reviewed By: Sayan Chatterjee
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