An Efficient Boosting-Based Windows Malware Family Classification System Using Multi-Features Fusion

Author:

Chen Zhiguo12ORCID,Ren Xuanyu12

Affiliation:

1. Engineering Research Center of Digital Forensics, Nanjing University of Information Science and Technology, Ministry of Education, Nanjing 210044, China

2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

In previous years, cybercriminals have utilized various strategies to evade identification, including obfuscation, confusion, and polymorphism technology, resulting in an exponential increase in the amount of malware that poses a serious threat to computer security. The use of techniques such as code reuse, automation, etc., also makes it more arduous to identify variant software in malware families. To effectively detect the families to which malware belongs, this paper proposed and discussed a new malware fusion feature set and classification system based on the BIG2015 dataset. We used a forward feature stepwise selection technique to combine plausible binary and assembly malware features to produce new and efficient fused features. A number of machine-learning techniques, including extreme gradient boosting (XGBoost), random forest, support vector machine (SVM), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost), are used to confirm the effectiveness of the fusion feature set and malware classification system. The experimental findings demonstrate that the XGBoost algorithm’s classification accuracy on the fusion feature set suggested in this paper can reach 99.87%. In addition, we applied tree-boosting-based LightGBM and CatBoost algorithms to the domain of malware classification for the first time. On our fusion feature set, the corresponding classification accuracy can reach 99.84% and 99.76%, respectively, and the F1-scores can achieve 99.66% and 99.28%, respectively.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference50 articles.

1. Said, V., Eelly, E., Zag, E., and Murat, O. (2022). The Dangerous Combo: Fileless Malware and Cryptojacking. J. SoutheastCon, 125–132.

2. Cybersecurity gets smart;Greengard;Commun. ACM,2016

3. PROUD-MAL: Static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable;Rizvi;Complex Intell. Syst.,2022

4. Ensemble Model Ransomware Classification: A Static Analysis-based Approach;Johnson;Inventive Comput. Inf. Technol.,2022

5. Loi, N., Borile, C., and Ucci, D. (2022, December 05). Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning. Available online: https://arxiv.org/abs/2106.05625.

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3