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Hackernoon logoPractical Examples of Using ML in Cybersecurity by@eugenia-kuzmenko

Practical Examples of Using ML in Cybersecurity

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@eugenia-kuzmenkoEugenia Kuzmenko

Marketing Manager @ KitRUM

In this era, technology has become a basic necessity due to its compactness and handiness. This alleviation in the use of technology has also welcomed new problems. One of the most crucial issues is security. Devices contain personal and critical data which is usually misused if it is not secured. This is why the functioning of cybersecurity uses Machine language and Artificial Intelligence. It implements protection tools to create a wall between user and hacker.

The main function of machine learning is to tackle cyber threats. It maps up real-time cyber-crime, pattern detection and much more. Machine learning is a subcategory in Artificial Intelligence that offers systems the capacity to automatically learn and upgrade itself without regular programming. Artificial Intelligence can imitate human capabilities. It predicts and stimulates action by observing past and present situations. Machine language is the algorithm that enhances artificial intelligence and this in return tightens up cybersecurity.

How to use Artificial Intelligence for cybersecurity in real life?

Artificial Intelligence Email Monitoring

In this model, machine learning supports software to increase the accuracy of threat detection. This software prevents unethical hacking of emails by identifying the threats.

Several Artificial Intelligent technologies are being used in this case. Software such as Computer Vision is used extensively in new applications. It searches the email for any features that state threats, such as images of a certain size.

In different scenarios, natural language processing is used in reading incoming and outgoing emails. This pinpoints phrases or patterns linked to hacking. It helps in pointing out the threats by detecting present attachments or sender of the email.

Source: Irene Rinaldi

Fraud Detection

Machine learning has many aspects that help in detecting fraud. It branches out into different categories according to the need of Software application:

Supervised Learning: This scheme labels and classifies the whole chunk of data as good or bad. Supervised learning is dependent on predictive data analysis which only works on new classified data. This data is good enough if there are no hidden or visible threats.

  • Unsupervised Learning: This learning model updates itself by using information that is simultaneously processed. Then it analyzes from the updated data. This learning consists of unlabeled training data.
  • Semi-Supervised Learning: This model labels only crucial data since it is too costly and time-consuming in labeling each data. This is why it only stores critical and targeted data on essential group variables.
  • Reinforcement Learning: This algorithm helps in identifying optimal actions according to their specification automatically.
  • Botnet Detection: In this technique, malicious traffic is diagnosed by analyzing out the network traffic. The basis of this analysis is to contain network traffic activity and traffic trends. This detection has cleaved into two aspects named: active monitoring and inactive monitoring

New technologies incorporate Artificial Intelligence for solving problems. It determines analytics extracted from past and present behaviors. It uses advanced algorithms to reduce the chances of cyber threats. This has helped to encode crucial data and avoid violations in the user’s personal space.

This technique achieves optimum outcomes by designing a much more sophisticated model. It can quicken the speed of risk identification and prevent phishing. It plays a huge role in dynamic changes which will amplify according to hacker’s potential in the future.

If you're curious about Machine Learning and ways it's used in cybersecurity - read out our latest article. There I've explained functions most of the ML algorithms carry out and real-life examples.


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