一种自动实时的物联网在野漏洞攻击检测方法

何清林; 王丽宏; 陈艳姣; 王星

doi:10.13700/j.bh.1001-5965.2022.0592

一种自动实时的物联网在野漏洞攻击检测方法

doi: 10.13700/j.bh.1001-5965.2022.0592

何清林^{1, 2},
王丽宏^2, ,,
陈艳姣³,
王星⁴

1.
国家互联网应急中心，北京 102299
2.
北京航空航天大学计算机学院，北京 100191
3.
浙江大学电气工程学院，杭州 310007
4.
西北工业大学网络空间安全学院，西安 710072

详细信息

通讯作者:
E-mail：hql@cert.org.cn

中图分类号: TP393.4；TP312
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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-05
- 录用日期: 2022-09-02
- 网络出版日期: 2023-03-29
- 整期出版日期: 2024-07-18

An automatic and real-time detection method of IoT in-the-wild vulnerability attack

HE Qinglin^{1, 2},
WANG Lihong^{2
, ,},
CHEN Yanjiao³,
WANG Xing⁴

1.
CNCERT/CC，Beijing 102299，China
2.
School of Computer Science and Engineering，Beihang University，Beijing 100191，China
3.
College of Electrical Engineering，Zhejiang University，Hangzhou 310007，China
4.
School of Cybersecurity，Northwestern Polytechnical University，Xi’an 710072，China

More Information

Corresponding author: E-mail：hql@cert.org.cn

摘要

摘要:
与互联网相连的海量物联网（IoT）设备容易被黑客攻击和利用，进而造成关键IoT应用的瘫痪。漏洞利用是一种常用的针对IoT设备的攻击方式，然而由于在野的漏洞利用形式多样、变异性和伪装性强，如何快速自动识别针对IoT设备的在野漏洞攻击极具挑战。为此，提出一种基于混合深度学习判别和开源情报关联的IoT漏洞攻击检测方法，所提检测方法可以实时判别网络流量中的IoT在野漏洞攻击行为，并且能够精准识别漏洞攻击行为的具体类别。实验结果表明：所提检测方法在大规模数据集上的判别准确率超过99.99%。所提检测方法在真实场景中应用效果显著，在不到1个月时间内发现了13种新的在野漏洞攻击。
- 物联网 /
- 在野漏洞利用 /
- 攻击检测 /
- 混合深度学习 /
- 开源情报
Abstract:
The vast number of Internet-connected internet of things (IoT) devices are susceptible to hacking and exploitation, which can lead to the paralysis of critical IoT applications. Vulnerability exploitation is a common method of attack on IoT devices; however, due to the diverse, mutable, and highly disguised forms of in-the-wild vulnerability exploitations, it is extremely challenging to quickly and automatically identify ongoing vulnerability attacks targeting IoT devices. To address this, a detection method for IoT vulnerability attacks based on a hybrid deep learning discrimination and open-source intelligence correlation is proposed. This detection method can identify IoT in-the-wild vulnerability attack behaviors in network traffic in real-time and accurately identify the specific categories of vulnerability attack behaviors. Experimental results show that the proposed detection method achieves an accuracy rate of over 99.99% on large-scale datasets. The application of the proposed detection method in real-world scenarios has been significant, discovering 13 new in-the-wild vulnerability attacks within less than a month.
- internet of things /
- in-the-wild vulnerability exploitation /
- attack detection /
- hybrid deep learning /
- open-source intelligence

HTML全文

图 1 IoT漏洞信息披露和在野利用发现

Figure 1. IoT vulnerability information disclosure and inofficial exploits discovery

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图 2 本文检测方法框架

Figure 2. Framework of the proposed detection method

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图 3 HTTP请求报文典型格式

Figure 3. Typical format of HTTP request messages

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图 4 2个IoT漏洞攻击的报文示例

Figure 4. Request message examples of two IoT exploits

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图 5 本文模型结构

Figure 5. Structure of the proposed model

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图 6 精准率和召回率实验结果

Figure 6. Experimental results of precision and recall rate

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图 7 2种模型损失值

Figure 7. Loss of two models

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表 1 最近3年部分在野利用IoT漏洞示例

Table 1. Some inofficial IoT exploit examples in past three years

CVE编号	漏洞名称	针对设备
CVE_2021_33544	UDP_Technology_Geutebruck_IP_Cameras_Command_Injection	IP摄像头
CVE_2021_33514	HTTP_Router_NetgearGC108P	Netgear路由器
CVE_2021_31755	Tenda_AC11_Router_Stack_Buffer_Overflow_Vulnerability	腾达路由器
CVE_2021_28799on	QNAP_NAS_Hybrid_Backup_Sync_Command_Injecti	QNAP NAS设备
CVE_2021_20090	Arcadyan_Buffalo_Routers_Configuration_File_Injection	Buffalo组件的路由器
CVE_2021_1497	Cisco_HyperFlex_HX_Data_Platform_Command_Execution	Cisco管理平台
CVE_2020_9054_	Zyxel_NAS_RCE_Attempt_Inbound	Zyxel NAS设备
CVE_2020_8949	Gocloud_Router_Remote_Code_Execution	Gocloud路由器
CVE_2020_8515	DrayTek_Vigor_RCE	Vigor路由器
CVE_2020_35713	Linksys_RE6500_1_0_11_001_Remote_Code_Execution	Linksys路由器
CVE_2020_35576	TpLink_TLWR841N_Command_Injection	Tplink路由器
CVE_2020_17456	Seowon_Route	Seowon路由器
CVE_2020_13872	DLink_DIR_865L_Ax120B01_Command_Injection	Dlink路由器
CVE_2020_10987	Tenda_AC15_AC1900_goform_setUsbUnload_RCE	腾达路由器

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表 2 F₁和A_CC实验结果

Table 2. Experimental results of F₁ and A_CC

正负样本比例	F₁/%		A_CC/%
正负样本比例	Bert^[29]模型	本文模型	Bert^[29]模型	本文模型
1∶1(总数10⁶)	99.93	99.94	99.95	99.96
1∶2(总数1.5×10⁶)	99.97	99.97	99.99	99.99
1∶4(总数2.5×10⁶)	99.93	99.96	99.98	99.98
1∶8(总数4.5×10⁶)	99.96	99.97	99.96	99.97

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表 3 模型每轮训练时间

Table 3. Model training time in each round

样本数量/10⁶	训练时间/min
样本数量/10⁶	Bert^[29]模型	本文模型
1	62	71
1.5	89	102
2.5	127	146
4.5	146	167

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表 4 IoT漏洞攻击中的攻击目标及攻击参数

Table 4. Attack targets and parameters in IoT exploits

漏洞编号	攻击目标	攻击参数
CVE_2021_33544	/uapi-cgi/certmngr.cgi	action,createselfcert,local
CVE_2021_33514	cgi/setup.cgi	token
CVE_2021_31755	/goform/setmac	mac,wifien,wifissid
CVE_2021_28799	/cgi-bin/backup/hbs_mgnt.cgi	run_cmd,jisoosocoolhbsmgnt
CVE_2021_20090	/images/../apply_abstract.cgi	action,submit_button,action_params,arc_ping_ipaddress
CVE_2021_1497	/storfs-asup	action,token
CVE_2020_9054	/cgi-bin/weblogin.cgi	adv,username,password
CVE_2020_8949	/cgi-bin/webui/admin/tools/app_ping/diag_ping/	None
CVE_2020_8515	cgi-bin/mainfunction.cgi	action,keypath,loginPwd
CVE_2020_35713	/goform/setSysAdm	AuthTimeout, admpasshint
CVE_2020_35576	/cgi?	maxhopcount, numberoftries, host
CVE_2020_17456	/cgi-bin/system_log.cgi	Command, traceMode
CVE_2020_13872	/portal/__ajax_explorer.sgi	Action/path/where/en
CVE_2020_10987	/goform/setUsbUnload	setUsbUnload

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表 5 IoT漏洞攻击中常见的攻击指令

Table 5. Common attack commands in IoT exploits

攻击指令类型	关键词
样本植入	wget,curl,tftp,fetch
反弹回连	bash-i，tmp/socat exec
探测	Nc,echo,dnslog
隐藏痕迹	Rm,mv,base64,decodeHex
命令执行	chmod, system,md5sum, busybox ,exec

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