Machine Learning for Cyber Security and Increasing its effectiveness

By CIOReview | Thursday, April 25, 2019

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.

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.  Discontinuing overprotective stance, which can block safe traffic and let go off the potentially lousy traffic, can avoid the happening of such scenarios.

Adapting intelligent adversaries

 Generally, attackers will hack the system and extract data of million customers without giving any information even though the systems are being equipped with the ML tools. To surpass this issue, there is an essential requirement of intelligent adversaries which will detect and block all future threats.

False Positives can be more dangerous

Compared to malware, a false positive can be more destructive for some business which can disrupt the production by deleting the essential data. It's vital to remember ML can be used to create more effective malware too which can lead to massive delays and can damage the reputation of an organization.