Visual Classification of Malware by Few-shot Learning

被引：0

作者：

Tran, Kien ^{[1
]}

Kubo, Masao ^{[1
]}

Sato, Hiroshi ^{[1
]}

机构：

[1] Natl Def Acad Japan, Dept Comp Sci, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 2398686, Japan

来源：

PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB2020) | 2020年

关键词：

Few shot Learning; Malware Classification; Matching Network; Visualization Classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The extent of damage by malware has been multiplying. Many techniques are proposed for detecting malware. However, the usual pattern matching method does not work because when the new malware appeared, many variants are created very soon. In order to catch the new malware, we have to detect and classify them from very few samples. In this paper, we propose a machine learning mechanism that can learn from very few samples of the image of the malware.

引用

页码：770 / 774

页数：5

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