基于诱捕的软件异常检测综述

doi:10.11959/j.issn.2096-109x.2022003

Abstract

Abstract:

Advanced persistent threats (APT) will use vulnerabilities to automatically load attack code and hide attack behavior, and exploits code reuse to bypass the non-executable stack ＆amp; heap protection, which is an essential threat to network security.Traditional control flow integrity and address space randomization technologies have effectively prevented the pace of APT.However, the complexity of the software and the evolution of attacks make the software still being vulnerable.For this reason, deception defense with resources as bait is an indispensable supplement for network security.The trapping mechanism consists of bait design and attack detection, which infer possible unauthorized access or malicious attacks by sensing the interaction behavior with the bait.According to the three types of bait, which are file, data and code, the automatic construction scheme of bait is designed and deployed, and the effectiveness of bait is measured from the aspects of believability, detectability and enticement, etc.Ransom ware detection based on deception defense focuses on the deployment location of bait files, and in the area of vulnerability detection, code reuse attacks are detected by injecting bait code.Research work related to the implementation of deception defense in each phase of APT attacks was introduced, and the mechanism of deception defense from bait type, bait generation, bait deployment, and bait measurement was described.Simultaneously, deception defense applications in ransom ware detection, vulnerability detection, and Web security were analyzed.In response to the shortcomings of existing ransom ware detection research in terms of bait file design and deployment, a dynamic update method of bait for ransom ware detection was proposed.The deception defense challenges were discussed and hoped that deception defense can provide theoretical and technical support for discovering unknown attacks and attack attribution.

Key words: advanced persistent threat, code reuse attack, control flow integrity, address randomization, deception defense

CLC Number:

TP393

Jianming FU, Chang LIU, Mengfei XIE, Chenke LUO. Survey of software anomaly detection based on deception[J]. Chinese Journal of Network and Information Security, 2022, 8(1): 15-29.

Figures/Tables 6

References 73

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诱饵	诱饵类型	使用方式	用途	备注
E-mail地址、登录凭证	Data	Remote	异常告警
HoneyFile	File+Data	Local+Remote	文件和数据非授权访问
Decoy-document	File+Data	Local+Remote	文件和数据非授权访问	Beacon
honeytable	Data	Remote	DB的非授权访问
Bogus-program	File+Data	Local+Remote	软件盗版	Beacon
HoneyWord	Data	Remote	口令猜测	口令
Honey-patches	Data	Remote	漏洞攻击重定向
Honey-pot、Mobipot	Data	Remote	钓鱼/推销电话	电话号码
R-locker	File	Local	勒索软件	管道
Trap、Codearmor	Code	Local	漏洞检测	ROP gadget虚假指针

诱饵文件	真实性	动态性	诱惑性	醒目性	无干扰	可检测性
RWGuard^[31]	√	×	√	√	×	√
DcyFS^[32]	√	√	√	√	×	√
UNVEIL^[44]	√	×	√	√	×	√
R-Locker^[45]	×	√	×	√	×	√
Decoy Files^[46]	√	×	×	√	√	√
IRDS^[47]	√	×	×	√	×	√
DDT^[48]	√	×	√	√	×	√
注：“√”表示工具满足该项指标，“×”表示不满足该项指标。

勒索软件家族（样本量）	平均已加密文件数
Reveton(102)	4.8
CoinVault(87)	0.0
Onion(115)	5.2
WannaCry(204)	0.0
Ryuk(48)	11.3
Sodinokibi(90)	0.6

Survey of software anomaly detection based on deception

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