Machine Learning for Cyber Security and Increasing its effectiveness

Photo by Franck V. on Unsplash

Most significant advancements include Machine Learning which is a subset of Artificial Intelligence technology that provides more innovations in the applications of cybersecurity. Machine Learning technologies can optimize the business processes by improving test procedures and developing new solutions for the detected problems and can throw back large-scale fraud. Even though it is a promising technology, but still it has some drawbacks that need to overcome.

Most of the cyber attacks are automated, and the reason behind this is, cybercriminals and malware developers are frequently relying on ML tools. Using these tools they create malware strains by targeting specific victims to extract valuable data.

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Following are the certain limitations which need to be considered for increasing its effectiveness

Extensive Training

With extensive manual work, many organizations are constantly gathering millions of samples from many years to detect malware using ML tools but they prove to be inefficient in the authentication. Human expertise and verification are required continuously for better authentication.

The mathematical approach is not an ideal solution

At some instances, a purely mathematical approach can fail to identify the fraudulent activities that may arise during business operations. Read More…

I am a technology blogger, who loves to read and write on the latest in technology.

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