Comparisons on Intrusion Detection and Prevention Systems in Distributed Databases

Abstract

With the use of distributed systems, different users can instantly access data from different locations and perform some operations on the data. However, the unauthorized access of multiple users to the system from different points at the same time can lead to dangerous results in terms of data security and confidentiality of the data. This study is based on intrusion detection and prevention systems built on distributed databases and classifies the methods used to analyze and evaluate successes comparatively. It is observed that the artificial immunity algorithm we have described in artificial intelligence techniques, which is one of the methods classified as three categories, gives more successful results compared to the other techniques mentioned in the data mining and statistical methods.