Adversarial Example Remaining Availability and Functionality

doi:10.3778/j.issn.1673-9418.2103057

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2286-2297.DOI: 10.3778/j.issn.1673-9418.2103057

• Network and Information Security • Previous Articles Next Articles

Adversarial Example Remaining Availability and Functionality

XIAO Mao¹^,², GUO Chun¹^,²^,⁺(), SHEN Guowei¹^,², JIANG Chaohui¹^,²

1. School of Computer Science and Technology, Guizhou University, Guiyang 550025, China
2. State Key Laboratory of Public Big Data, Guiyang 550025, China

Received:2021-03-17 Revised:2021-05-14 Online:2022-10-01 Published:2021-05-18
About author:XIAO Mao, born in 1996, M.S. candidate, member of CCF. His research interests include network security and malware detection.
GUO Chun, born in 1986, Ph.D., associate professor, M.S. supervisor, member of CCF. His research interests include data mining, intrusion detection, malware detection, etc.
SHEN Guowei, born in 1986, Ph.D., associate professor, M.S. supervisor, member of CCF. His research interests include network and information security and big data.
JIANG Chaohui, born in 1965, professor, M.S. supervisor. His research interests include network and information security, intrusion detection, etc.
Supported by:
National Natural Science Foundation of China(62062022);Science and Technology Foundation of Guizhou Province([2020]1Y268)

可保留可用性和功能性的对抗样本

肖茂¹^,², 郭春¹^,²^,⁺(), 申国伟¹^,², 蒋朝惠¹^,²

1.贵州大学计算机科学与技术学院,贵阳 550025
2.公共大数据国家重点实验室,贵阳 550025

通讯作者: + E-mail: gc_gzedu@163.com
作者简介:肖茂（1996—）,男,硕士研究生,CCF会员,主要研究方向为网络安全、恶意软件检测。
郭春（1986—）,男,博士,副教授,硕士生导师,CCF会员,主要研究方向为数据挖掘、入侵检测、恶意代码检测等。
申国伟（1986—）,男,博士,副教授,硕士生导师,CCF会员,主要研究方向为网络与信息安全、大数据。
蒋朝惠（1965—）,男,教授,硕士生导师,主要研究方向为网络与信息安全、入侵检测等。
基金资助:
国家自然科学基金(62062022);贵州省科学技术基金([2020]1Y268)

Abstract

Abstract:

Malware detection method based on gray images has received a lot of attention because it does not require disassembly and can obtain a high detection accuracy. There are some adversarial attacks against this type of detection method which has been put forward, but most of the current adversarial attack methods cannot ensure that the generated adversarial examples can remain the availability or functionality of the original PE file, or choose to add bytecode at the bottom of a PE file that is easy to be accurately detected through the file header information. Based on the analysis of the section alignment mechanism and file alignment mechanism of PE files, this paper proposes a bytecode attack method that can remain the availability and functionality (BARAF) of PE files. By modifying or adding bytecodes in the gap spaces generated by the file alignment mechanism and the expansion spaces derived from the section alignment mechanism, BARAF generates the adversarial example that can remain the availability and functionality to deceive the malware detection method based on gray images. Experimental results show that the adversarial examples generated by BARAF can reduce the accuracy of the malware detection method based on gray images by 31.58 percentage points at most, and it is difficult to detect the adversarial examples accurately through the file header information.

Key words: adversarial example, malware detection, gray image, PE file

摘要：

基于灰度图的恶意软件检测方法由于不需要反汇编且具有检测准确率高的特点而备受关注。现今已有一些针对该类检测方法的对抗攻击,然而当前大部分对抗攻击方法无法确保所生成的对抗样本仍保留原PE文件的可用性或功能性,或是选择在通过文件头信息便能进行准确检测的PE文件底部添加字节码。通过分析PE文件的区段对齐机制以及文件对齐机制,提出一种可保留PE文件可用性和功能性的字节码攻击方法（BARAF）。该方法通过在由文件对齐机制产生的间隙空间和源于区段对齐机制而具有的扩展空间内批量修改或添加字节码来生成可保留可用性和功能性的对抗样本,来欺骗基于灰度图像的恶意软件检测方法。实验结果表明,BARAF生成的对抗样本最多能使基于灰度图的恶意软件检测方法的准确率下降31.58个百分点,并且难以通过文件头信息对其进行准确检测。

关键词: 对抗样本, 恶意软件检测, 灰度图, PE文件

CLC Number:

TP309.5

XIAO Mao, GUO Chun, SHEN Guowei, JIANG Chaohui. Adversarial Example Remaining Availability and Functionality[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2286-2297.

肖茂, 郭春, 申国伟, 蒋朝惠. 可保留可用性和功能性的对抗样本[J]. 计算机科学与探索, 2022, 16(10): 2286-2297.

Figures/Tables 14

References 33

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序号	类别	家族	训练样本数	测试样本数
1	Benign	Benign	412	176
2	Backdoor	PcClient	299	128
3	Backdoor	Poison	180	77
4	Backdoor	Shark	135	58
5	Backdoor	Small	260	112
6	Ransomware	Ceber	83	35
7	Ransomware	Gandcrab	111	48
8	Rootkit	Agent	99	42
9	Trojan-Banker	Banker	117	50
10	Trojan-Downloader	FraudLoad	190	81
11	Trojan-Downloader	Hmir	129	55
12	Trojan-Downloader	VB	183	78
13	Trojan-Downloader	Zlob	346	149
14	Trojan-GameThief	Magania	109	47
15	Trojan-GameThief	OnLineGames	149	64
16	Trojan-Spy	Pophot	198	85
17	Trojan-Spy	Zbot	282	121

序号	类别	家族	训练样本数	测试样本数
1	Benign	Benign	412	176
2	Backdoor	PcClient	299	128
3	Backdoor	Poison	180	77
4	Backdoor	Shark	135	58
5	Backdoor	Small	260	112
6	Ransomware	Ceber	83	35
7	Ransomware	Gandcrab	111	48
8	Rootkit	Agent	99	42
9	Trojan-Banker	Banker	117	50
10	Trojan-Downloader	FraudLoad	190	81
11	Trojan-Downloader	Hmir	129	55
12	Trojan-Downloader	VB	183	78
13	Trojan-Downloader	Zlob	346	149
14	Trojan-GameThief	Magania	109	47
15	Trojan-GameThief	OnLineGames	149	64
16	Trojan-Spy	Pophot	198	85
17	Trojan-Spy	Zbot	282	121

被攻击方法	攻击方法	BCT	Accuracy/%	MR/%	AAV/Byte
Gray+ VGG16+ SVM	—	—	90.33	—	—
	GBCA	0x00	90.33	0	2 702
		0x80	88.72	1.61
		0xFF	76.76	13.57
	EBCA	0x00	72.91	17.42	7 743
		0x80	75.93	14.40
		0xFF	63.66	26.67
	MBCA	0x00	72.91	17.42	8 776
		0x80	73.25	17.08
		0xFF	58.75	31.58

被攻击方法	攻击方法	BCT	Accuracy/%	MR/%	AAV/Byte
Gray+ VGG16+ SVM	—	—	90.33	—	—
	GBCA	0x00	90.33	0	2 702
		0x80	88.72	1.61
		0xFF	76.76	13.57
	EBCA	0x00	72.91	17.42	7 743
		0x80	75.93	14.40
		0xFF	63.66	26.67
	MBCA	0x00	72.91	17.42	8 776
		0x80	73.25	17.08
		0xFF	58.75	31.58

生成成功率	攻击方法
生成成功率	EBCA	GBCA	MBCA
SR	63.02	73.76	41.04

Adversarial Example Remaining Availability and Functionality

可保留可用性和功能性的对抗样本

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Figures/Tables 14

References 33

Related Articles 3

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Metrics

序号	家族	EBCA生成成功数	GBCA生成成功数	MBCA生成成功数
1	Benign	131	145	104
2	B.PcClient	104	120	96
3	B.Poison	8	63	4
4	B.Shark	6	11	0
5	B.Small	0	112	0
6	RS.Ceber	35	35	35
7	RS.Gandcrab	48	47	47
8	R.Agent	16	38	13
9	TB.Banker	50	0	0
10	TD.FraudLoad	78	33	33
11	TD.Hmir	23	32	0
12	TD.VB	18	63	4
13	TD.Zlob	116	136	103
14	TG.Magania	47	18	18
15	TG.OnLineGames	0	64	0
16	TS.Pophot	85	85	85
17	TS.Zbot	121	35	35
总计	—	886	1 037	577

评价点	攻击方法
评价点	ATMPA^[17]	RSRC+FGSM^[18]	COPYCAT^[19]	BARAF
保留可用性	否	是	是	是
保留功能性	否	否	是	是
检测准确性	较低	较低	较高	较低

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评价指标	攻击方法
评价指标	GBCA	EBCA
TPR	100.00	100.00
FPR	17.07	59.81

评价指标	攻击方法
评价指标	GBCA	EBCA
TPR	100.00	100.00
FPR	17.07	59.81