Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

ISBN:

9781509063673

URL:

https://ieeexplore.ieee.org/document/8125845

Keywords:

access control mechanisms, Authentication, authorisation, classification, Classification algorithms, Clustering, Cryptography, Data mining, Data mining techniques, Data security, data theft, Database Management System, Databases, DBSCAN, encryption technologies, Intrusion detection, intrusion detection mechanisms, Organizations, Pattern classification, pattern clustering, Quiplet, relational databases, Security features, standard database security measures, Support vector machines, User profile

Abstract:

The importance of data security and confidentiality increases day by day, since for most companies and organizations data remains as the most important asset. Standard database security measures like access control mechanisms, authentication and encryption technologies are of little help when it comes to preventing data theft from insiders. By incorporating intrusion detection mechanisms, we can improve the security features of a Database Management System (DBMS). In this paper we propose a novel method for detecting intrusions in databases using data mining techniques like clustering and classification. Experiments show that our method outperforms other methods with higher accuracy and reduced false alarm rate.

Cite this Research Publication

Raji Ramachandran, Arya, P., and Jayanthy, P. G., “A Novel Method for Intrusion Detection in Relational Databases”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.