Malicious Code Classification Method Based on BiTCN-DLP

doi:10.3969/j.issn.1671-1122.2023.11.011

Abstract

Abstract:

To cope with the escalating malicious code variants, this article proposed a malicious code classification method (BiTCN-DLP) based on a bidirectional temporal convolution network (BiTCN) and double layer pooling (DLP) to address the problems of insufficient feature extraction and degradation of classification accuracy of existing malicious code classification methods. First, the method fused malicious code opcode and bytecode features to show different details, built BiTCN models to take advantage of the backward and forward dependencies of the features, and introduced a pooling fusion mechanism to further explore the deep dependencies within the malicious code data. Then, the model was validated on the Kaggle dataset. The experimental results show that the accuracy of malicious code classification based on BiTCN-DLP can reach 99.54% with fast convergence and low classification error. Finally, the effectiveness of the model was proved by comparison experiments and ablation experiments.

Key words: malicious code classification, feature fusion, BiTCN, DLP

CLC Number:

TP309

LI Sicong, WANG Jian, SONG Yafei, HUANG Wei. Malicious Code Classification Method Based on BiTCN-DLP[J]. Netinfo Security, 2023, 23(11): 104-117.

Figures/Tables 16

References 52

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家族名	类型
Ramnit	蠕虫
Lollipop	广告植入
Kelihos_ver1	后门
Kelihos_ver3	后门
Vundo	木马病毒
Simda	后门
Tracur	马下载器
Obfuscator.ACY	各种混淆代码
Gatak	后门

实验环境	具体配置
操作系统	Windows 11
CPU	Intel（R） Core（TM） i5-8300H CPU @2.30 GHz 2.30 GHz
内存	16 GB
硬盘	500 GB
显卡	NVIDIA GeForce GTX 1050 Ti
开发框架	Keras 2.9.0/TensorFlow 2.9.1
开发语言	Python 3.10

模型参数	参数设置
批量大小	256
优化器	Adam
学习率	0.003
Epoch	200
卷积核尺寸	1×1
卷积核	128×4
残差连接层数	4+4
TCN膨胀系数	（1，2，4，8）
BiTCN单元数	2
Dropout率	0.2

	Accuracy	Precision	Recall	F1
序列特征	98.19 %	97.57 %	93.10 %	94.64 %
图像特征	89.41 %	90.84 %	88.82 %	89.29 %
融合特征	99.54 %	99.61 %	99.42 %	99.49 %

	Accuracy	Precision	Recall	F1
正向TCN	90.45 %	99.12 %	90.45 %	91.82 %
反向TCN	93.21 %	99.09 %	91.56 %	93.22 %
双向TCN	99.54 %	99.61 %	99.42 %	99.49 %