Automatic Attack Detection with Machine Learning and Secure Log for Cloud Forensics
Chindu Mohan, Devi Dath · 2022
In recent years, people are depending more on cloud infrastructures. Organizations also depend on cloud infrastructure to store data. As the cloud provides the flexibility of having no local setup, it gained popularity. As cloud gained popularity it became prone to more attacks. Cyber forensics plays a vital role in detecting, collecting evidence and, preventing cyber-attacks. Log files are the major source of evidence for cyber forensics. In the existing system, the cloud logs are secured with encryption and it detects DDoS attacks from the log file data. In this automated attack detection scheme, the log files are secured using AES encryption and it is capable of automated detection and prevention of 22 different types of attacks with the help of machine learning. The encrypted data will provide security to log files that can be later used as evidence against the attack. The proposed model was tested with an accuracy of 99%.