eprintid: 4690
rev_number: 5
eprint_status: archive
userid: 431
dir: disk0/00/00/46/90
datestamp: 2021-12-13 03:56:42
lastmod: 2021-12-13 03:56:42
status_changed: 2021-12-13 03:56:42
type: article
metadata_visibility: show
creators_name: Hoang, Dang Kien
creators_name: Nguyen, Dai Tho
creators_name: Nguyen, Thi Thu Trang
creators_id: kienhd1@vnu.edu.vn
creators_id: nguyendaitho@vnu.edu.vn
creators_id: trangngtt@vnu.edu.vn
title: Efficient Incremental Instance-based Learning Algorithms for Open World Malware Classification
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Malware is growing rapidly in number and become more and more sophisticated. To prevent them we need to collect samples continuously and update them to the classifier. In this paper, we will propose a method to update new labeled samples of malware to the classifier easily without re-training everything. The classifier can be updated by both labeled malware samples of an existing class or a new class. Our method also has the ability to detect samples of unknown families. Experiments are performed over the traditional computer malware dataset and the IoT malware dataset. The results have shown that our method can reach the macro F1-score almost the same re-train everything but take significantly less time.
publisher: IEEE
official_url: https://atc-conf.org/
full_text_status: none
publication: 2021 International Conference on Advanced Technologies for Communications (ATC)
refereed: TRUE
issn: 2162-1039
citation:   Hoang, Dang Kien and Nguyen, Dai Tho and Nguyen, Thi Thu Trang   Efficient Incremental Instance-based Learning Algorithms for Open World Malware Classification.  2021 International Conference on Advanced Technologies for Communications (ATC) .    ISSN 2162-1039