@inproceedings{3a15705cd9f648a8bdacae6e070fbf8e,
title = "Botnet detection in network system through hybrid low variance filter, correlation filter and supervised mining process",
abstract = "To date, malware caused by botnet activities is one of the most serious cybersecurity threats faced by internet communities. Researchers have proposed data-mining-based IDS as an alternative solution to misuse-based IDS and anomaly-based IDS to detect botnet activities. In this paper, we propose a new method that improves IDS performance to detect botnets. Our method combines two statistical methods, namely low variance filter and Pearson correlation filter, in the feature-selection process. To prove our method can increase the performance of a data-mining-based IDS, we use accuracy and computational time as parameters. A benchmark intrusion dataset (ISCX2017) is used to evaluate our work. Thus, our method reduces the number of features to be processed by the IDS from 77 to 15. Although the number of features decreases, it does not significantly change the accuracy. The computational time is decreased from 71 seconds to 5.6 seconds.",
keywords = "Feature selection, Intrusion detection system, ISCX2017 datasets, Low variance filter, Pearson correlation filter, Supervised mining",
author = "Saputra, {Ferry Astika} and Masputra, {Muhammad Fajar} and Iwan Syarif and Kalamullah Ramli",
year = "2018",
month = sep,
day = "1",
doi = "10.1109/ICDIM.2018.8847076",
language = "English",
series = "2018 13th International Conference on Digital Information Management, ICDIM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "112--117",
editor = "Ezendu Ariwa and Pit Pichappan and Pit Pichappan and El-Medany, {Wael M} and Asif Naeem",
booktitle = "2018 13th International Conference on Digital Information Management, ICDIM 2018",
address = "United States",
note = "13th International Conference on Digital Information Management, ICDIM 2018 ; Conference date: 24-09-2018 Through 26-09-2018",
}