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A New Adaptive Learning Algorithm and Its Application to Online Malware Detection

机译：一种新的自适应学习算法及其在在线恶意软件检测中的应用

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摘要

Nowadays, the number of new malware samples discovered every day is in millions, which undermines the effectiveness of the traditional signature-based approach towards malware detection. To address this problem, machine learning methods have become an attractive and almost imperative solution. In most of the previous work, the application of machine learning to this problem is batch learning. Due to its fixed setting during the learning phase, batch learning often results in low detection accuracy when encountered zero-day samples with obfuscated appearance or unseen behavior. Therefore, in this paper, we propose the FTRL-DP online algorithm to address the problem of malware detection under concept drift when the behavior of malware changes over time. The experimental results show that online learning outperforms batch learning in all settings, either with or without retrainings.

机译：如今，每天发现的新恶意软件样本数以百万计，这破坏了传统的基于签名的恶意软件检测方法的有效性。为了解决这个问题，机器学习方法已经成为一种有吸引力且几乎势在必行的解决方案。在以前的大多数工作中，机器学习在此问题上的应用是批处理学习。由于其在学习阶段的固定设置，因此当遇到零日样本且外观模糊或看不见的行为时，批处理学习通常会导致检测精度低。因此，在本文中，我们提出了FTRL-DP在线算法，以解决当恶意软件的行为随时间变化时概念漂移下的恶意软件检测问题。实验结果表明，无论有没有再培训，在线学习在所有情况下都比批量学习好。

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来源
《Discovery science》|2017年|18-32|共15页
会议地点 Kyoto(JP)
作者
Ngoc Anh Huynh; Wee Keong Ng; Kanishka Ariyapala;
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作者单位

Nanyang Technological University, Singapore, Singapore ,University of Padua, Padua, Italy;

Nanyang Technological University, Singapore, Singapore ,University of Padua, Padua, Italy;

Nanyang Technological University, Singapore, Singapore ,University of Padua, Padua, Italy;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Malware detection; Batch learning; Online learning;

机译：恶意软件检测；批量学习；在线学习;

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1. An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware [J] . Olorunshola Oluwaseyi Ezekiel, Oluyomi Ayanfeoluwa Oluwasola, Irhebhude Martins Advances in Science, Technology and Engineering Systems . 2020,第6期

机译：用于检测Android应用程序恶意软件的机器学习算法的评估
2. Context-Aware, Adaptive, and Scalable Android Malware Detection Through Online Learning [J] . Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, IEEE Transactions on Emerging Topics in Computational Intelligence . 2017,第3期

机译：通过在线学习检测上下文，自适应和可扩展的Android恶意软件
3. A novel malware analysis for malware detection and classification using machine learning algorithms [J] . J. P. E. Hodgson Computing reviews . 2019,第2期

机译：使用机器学习算法进行恶意软件检测和分类的新型恶意软件分析
4. A New Adaptive Learning Algorithm and Its Application to Online Malware Detection [C] . Ngoc Anh Huynh, Wee Keong Ng, Kanishka Ariyapala International Conference on Discovery Science . 2017

机译：一种新的自适应学习算法及其在线恶意软件检测的应用
5. Android Malware Detection Using Machine Learning Algorithms [D] . Herron, Nathan. 2020

机译：使用机器学习算法进行Android恶意软件检测
6. An Efficient DenseNet-Based Deep Learning Model for Malware Detection [O] . Jeyaprakash Hemalatha, S. Abijah Roseline, Subbiah Geetha, 2021

机译：基于高效的恶意软件检测的深度学习模型
7. Adaptive and Scalable Android Malware Detection through Online Learning [O] . Narayanan, Annamalai, Yang, Liu, Chen, Lihui, 2016

机译：通过在线学习检测自适应和可扩展的android恶意软件

A New Adaptive Learning Algorithm and Its Application to Online Malware Detection

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