Predicting the Fastest, Safest Restoration Path Post-Breach Using ML

Abstract

The study is analysing the role of Machine Learning in predicting the fastest and safest path in the context of post-breaches. The study is applying explanatory design and using qualitative and quantitative data to derive the results. The results reveal how ML is effective in identifying the patterns within the system and user behaviours. The ML is ensuring the quick discerning of the vulnerable areas of the network requiring isolation. The prediction of the fastest and most secure path is possible on account of ML. The companies have been recommended to use ML with a Decision-Tree algorithm and multi-training method for gaining accurate outcomes.