Leveraging Advanced Machine Learning Techniques for Enhanced Intrusion and Fraud Detection in NoSQL Database Systems

Abstract

NoSQL database systems such as MongoDB, Cassandra, and Redis have seen rapid adoption in recent years due to their flexibility, scalability, and high performance. However, these databases also introduce new security challenges compared to traditional SQL databases. The dynamic schema, lack of access control, and focus on availability over consistency can make NoSQL databases vulnerable to intrusions, data breaches, and fraud. This paper explores how advanced machine learning techniques can be leveraged to enhance intrusion and fraud detection in NoSQL database systems. We survey different machine learning algorithms, including neural networks, support vector machines, random forests, and clustering, that can analyze large volumes of database activity logs to identify anomalous access patterns indicative of malicious behavior. We also examine how these models can be trained in an online manner to detect emerging threats and validate the techniques through proof-of-concept experiments on a prototype NoSQL database modeled after MongoDB. Our results demonstrate high accuracy in detecting injection attacks, unauthorized queries, and abnormal database traffic with low false-positive rates. This research highlights the promise of machine learning for robust intrusion and fraud detection in NoSQL databases. The techniques presented provide a proactive security layer to mitigate the risks introduced by the NoSQL model.