arrow
返回

Effective method for detecting malicious PowerShell scripts based on hybrid features

delete2021-08-01
delete14
PRE
AI
方
方勇 (Yong Fang)
X
Xiangyu Zhou
C
Cheng Huang *
DOI:10.1016/j.neucom.2021.03.117delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
At present, network attacks are rampant in the Internet world, and the attack methods of hackers are changing steadily. PowerShell is a programming language based on the command line and.NET framework, with powerful functions and good compatibility. Therefore, hackers often use PowerShell malicious scripts to attack the victims in APT attacks. When these malicious PowerShell scripts are executed, hackers can control the victim's computer or leave a backdoor on their computers. In this paper, a detection model of malicious PowerShell scripts based on hybrid features is proposed, we analyzed the differences between malicious and benign samples in text characters, functions, tokens and the nodes of the abstract syntax tree. Firstly, the script of PowerShell is embedded by FastText. Then the textual features, token features and the nodes features of PowerShell code extracted from the abstract syntax tree are added. Finally, the hybrid features of scrips will be classified by a Random Forest classifier. In the experiment, the malicious scripts are inserted into the benign scripts to weaken the features of the malicious samples in the level of abstract syntax tree nodes and tokens, which makes the scripts more complex. Even in such a complex data set, the proposed model which is based on hybrid features still achieves an accuracy of 97.76% in fivefold cross-validation. Moreover, the accuracy of this proposed model on the original scripts is 98.93%, which means that the proposed model has the ability to classify complex scripts. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Powershell
Abstract syntax tree
Scripts detection
Machine learning
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Neurocomputing 封面图
Neurocomputing
IF:
6.5
论文数:
2.5W
被引数:
6.5W

机构

S
sichuan university
学者数:
12.1W
论文数: 7.8W
被引数: 100
引用论文

引用论文

err
IF0
err
err0
PREAI
err
err分享
err收藏
err分享
err收藏
Cryptanalysis of Round-Reduced Fantomas, Robin and iSCREAM
err2019-01-10
err0
errOAAI
errAshutosh Dhar Dwivedi; Shalini Dhar; Gautam Srivastava; Rajani Singh
err分享
err收藏
On the adoption of anomaly detection for packed executable filtering
err2014-06-01
err12
PREAI
errUgarte-Pedrero, Xabier; Santos, Igor; Garcia-Ferreira, Ivan; Huerta, Sergio; Sanz, Borja; Bringas, Pablo G.
err分享
err收藏
DGCNN: A convolutional neural network over large-scale labeled graphs
err2018-12-01
err163
PREAI
errAnh Viet Phan; Minh Le Nguyen; Yen Lam Hoang Nguyen; Lam Thu Bui
err分享
err收藏
学者 查看更多内容