Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Official Statistics
Novia Amilatus Solekha
Institut Teknologi Sepuluh Nopember (ITS)
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analysis of NSL-KDD Dataset for Classification of Attacks Based on Intrusion Detection System Using Binary Logistics and Multinomial Logistics Novia Amilatus Solekha
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.087 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1138

Abstract

At present, the intrusion detection system is the most developed trend in society. The intrusion detection system acts as a defense tool to detect security attacks which has been increasing steadily in recent years. Therefore, global information security is a very serious problem. As the types of attacks that emerge are constantly changing, there is a need to develop adaptive and flexible security features. Selection feature is one of the focuses of research on data mining for datasets that have relatively many attributes. In this study, the author tries to analyze the NSL-KDD data set with the selected attributes classified in two ways, namely binary classification (attack or not attack) and five classification classes using multinomial logistics, namely Dos, R2L, U2R, Probe and Normal. The results showed that the NSL-KDD dataset for the classification of attacks on the Intrusion Detection System (IDS) using binary logistics can increase the classification accuracy to 92.3% and 91.7% for datasets with multinomial logistics.
Co-Authors