XGBOOST as A Classifier for Security Level Detection of Cryptosystem

  • Anyam Mohan, Dr. S. Raj Anand


By now, we have most likely heard of, or used, Zoom, the video conferencing service. Due to the coronavirus pandemic, Zoom has experienced an enormous spike in use over the past few months.Unfortunately, that same ease of use seems to have led to a variety of security and privacy issues.In short, Zoom’s meeting encryption exhibited less than “end-to-end” fortitude. In line with their privacy practices, the image, video and audio content during a Zoom meeting would remain private from any outsider (i.e., hackers). On this note, we can clearly tell that, though, several proposed encryption algorithms proven failed by allowing major vulnerabilities against important data. To reduce such vulnerabilities, it’s very important that which encryption algorithm is being used to protect the data. And we’ve to know which algorithm is suitable for that specific image, video or audio encryption along with the accuracy of encryption. Here for to full fill this need, we propose as security level detection approach for finding “strong, medium and weak” image encryption algorithms by incorporating a XGBoost Classifier Machine learning technique to reduce the effort of time complexity on detecting the appropriate encryption method.