Hybrid data mining to reduce false positive and false negative prediction in intrusion detection system
Document Type
Book Chapter
Publication Date
2019
Publication Title
Advances in Information and Communication Networks
Abstract
This paper proposes an approach of data mining machine learning methods for reducing the false positive and false negative predictions in existing Intrusion Detection Systems (IDS). It describes our proposal for building a confidential strong intelligent intrusion detection system which can save data and networks from potential attacks, having recognized movement or infringement regularly reported ahead or gathered midway. We have addressed different data mining methodologies and presented some recommended approaches which can be built together to enhance security of the system. The approach will reduce the overhead of administrators, who can be less concerned about the alerts as they have been already classified and filtered with less false positive and false negative alerts. Here we have made use of KDD-99 IDS dataset for details analysis of the procedures and algorithms which can be implemented.
Link to Published Version
Recommended Citation
Palanisamy, B., Panja, B., & Meharia, P. (2019). Hybrid data mining to reduce false positive and false negative prediction in intrusion detection system. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Advances in information and communication networks (Vol. 887, pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-030-03405-4_1
Comments
B. Panja is a faculty member in EMU's Department of Computer Science.
P. Meharia is a faculty member in EMU's Department of Accounting and Finance.
B. Palanisamy is an EMU student.