Authors :
Tamilselvan Arjunan
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/2d8e7pwh
Scribd :
https://tinyurl.com/4m8vb28v
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR127
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
NoSQL databases such as MongoDB and
Cassandra have been rapidly adopted in recent years
because of their high performance, flexibility, and
scalability. These databases present new security issues
compared to SQL databases. NoSQL databases are
vulnerable to fraud, intrusions and data breaches due to
their dynamic schemas, lack of control over access and the
focus on availability. This paper examines how advanced
machine-learning techniques can be used to enhance fraud
and intrusion detection in NoSQL databases. We examine
different machine-learning algorithms, including neural
networks and support vector machines. Random forests,
clustering, and random forests can be used to analyze large
databases activity logs in order to identify anomalous
patterns of access indicative of malicious behavior. We
examine how these models are trained online to detect
emerging threats, and we validate the techniques using
proof-of concept experiments on a prototype NoSQL based
database. Our results show high accuracy for detecting
injection attacks, unauthorized query, and abnormal
database traffic, with low false-positive rates.
Keywords :
Nosql, Mongodb, Security, Intrusion Detection, Fraud Detection, Machine Learning.
NoSQL databases such as MongoDB and
Cassandra have been rapidly adopted in recent years
because of their high performance, flexibility, and
scalability. These databases present new security issues
compared to SQL databases. NoSQL databases are
vulnerable to fraud, intrusions and data breaches due to
their dynamic schemas, lack of control over access and the
focus on availability. This paper examines how advanced
machine-learning techniques can be used to enhance fraud
and intrusion detection in NoSQL databases. We examine
different machine-learning algorithms, including neural
networks and support vector machines. Random forests,
clustering, and random forests can be used to analyze large
databases activity logs in order to identify anomalous
patterns of access indicative of malicious behavior. We
examine how these models are trained online to detect
emerging threats, and we validate the techniques using
proof-of concept experiments on a prototype NoSQL based
database. Our results show high accuracy for detecting
injection attacks, unauthorized query, and abnormal
database traffic, with low false-positive rates.
Keywords :
Nosql, Mongodb, Security, Intrusion Detection, Fraud Detection, Machine Learning.
