Top 4 Machine Learning Use Cases for Cybersecurity
Cyber and malware analysts play vital roles in organizations. They’re responsible for detecting and mitigating different kinds of attacks. These are attacks targeting company network systems. But, cyber-attacks are becoming diverse, making analysts consider technology to detect these anomalies.
Machine Learning (ML) is one of the critical technologies getting incorporated into cybersecurity. ML makes it possible for cybersecurity professionals to stay on high alert. But then, not everyone knows how ML can help them boost their online security.
This article will look into the use cases of machine learning in cybersecurity. It will help you understand why you should adopt ML solutions. By the end, you should have decided whether ML will be worth investing in for your business’s security.
Here’s how machine learning can boost your cybersecurity.
1. Security Analysis and Prediction
One of the best ways to ensure your network is safe is by analyzing your security. The analysis makes it easier to know your strengths and weaknesses in cybersecurity. This is one of the areas where machine learning plays a prominent role. It simplifies security analysis and prediction.
You can use machine learning models to do this without any problems. You need to develop models and deploy them on your network. But, deploying the models won’t guarantee the desired accuracy in predictions. It would be best if you also manage ML models to keep them functional.
ML model management is one of the most daunting tasks for businesses today. But then, it ensures consistency in security analysis and prediction. You can outsource tasks like machine learning model management. Experts can help you maintain the models you deploy.
In the end, ML models can help you get the security you desire. It will be easier to improve your cybersecurity to keep your system and network safe. This can be tricky to do if you do not leverage the power of technology. That’s why you should consider adopting machine learning.
2. Endpoint Protection
A business can have a wide range of endpoints or user entry points. These can be devices like mobile devices, computers, and laptops. In short, endpoints are the devices connected to a network. It also includes anything else people use to access a network, business, or customer data.
Cybercriminals target these endpoints when looking to attack a network. And, it is worrying that the number of endpoints is growing with tech advancements. It means cybersecurity experts will need to up their game to secure every entry point.
One way to do this is by adopting cybersecurity solutions like machine learning. They can do this by deploying machine learning models on their networks. ML models can track entry points on business networks to prevent malicious access.
They can combine machine learning with other essential endpoint security solutions. This includes antivirus software and firewalls meant to prevent malware attacks. ML helps boost the efficiency of these tools and makes it easy to secure networks.
3. Perfect Complement to Security Staff
As mentioned before, cybersecurity professionals may not protect every business aspect. The reality is that humans are susceptible to errors. And, a simple mistake can turn out to be costly to a business. That’s why you should consider having tech solutions like machine learning.
Another reason to adopt ML solutions is the existing large cybersecurity skills gap. There aren’t enough cybersecurity staff around the world today. This shortage has made securing networks daunting for organizations. They cannot provide the required level of protection to their networks.
Combining machine learning with the available workforce helps solve this shortage. Machine learning can study the nature of attacks cyber criminals use on your network. It will then store the patterns in a database then prevent the same attacks from happening in the future.
In short, machine learning helps automate this process. This is by ensuring future attacks get prevented by the ML tech in place before they happen. ML is also intelligent and can learn over time. This can help keep your systems and network safe even if attackers change their tactics.
4. Battling Bots
Bots are essential for any company that has an online presence. Bots make up the majority of internet traffic, and many of them are dangerous. Cybersecurity experts need to differentiate between good and bad bots.
But then, bots use stolen credentials which can be challenging to discover. Humans alone cannot find these credentials and differentiate between good and bad bots. This is one factor that makes machine learning vital for business security.
Machine learning makes it possible to prevent bots from entering a network. It screens the bots interacting with your network and filters out the dangerous ones. This ensures that your network is safe from various threats. This includes malware attacks that are usually difficult to avoid.
As said before, ML models deployed for this purpose are intelligent. They can learn patterns and block dangerous bots in the future. This can help ensure your network remains safe from new bots that may come up in the future.
In the end, your network remains secure from various threats. Also, you do not get exposed to risks even with the shifting threat landscape. This makes battling bots one of the major uses of ML in the cybersecurity field.
Also, Check – 5 Advantages Of Online Learning
Now you know how to use machine learning in cybersecurity. You can now use it to prevent a wide range of cyberattacks. For instance, you can use it to secure endpoints. This is especially true if your company uses many devices. The more the devices the bigger the attack surface.
Also, you can use ML to increase the efficiency and productivity of your staff. This is because there are risks of having humans alone track a network. For instance, they can feel fatigued or forget to track the network and expose it to attacks.
Security professionals may also not battle bots without the help of technology. And, they may also rely on technology like ML to make accurate analyses and predictions. It would help if you considered investing in ML for your cybersecurity needs. It will work with your staff to boost security.
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