情境蜜罐:预期隐私侵犯的框架

S. Gupta, Anand Gupta, Renu Damor, Vikram Goyal, Sangeeta Sabharwal
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引用次数: 5

摘要

在过去的几年里,蜜罐在网络领域被研究用于检测和收集外部威胁的信息。它们通过模拟具有漏洞的资源并观察潜在攻击者的行为来引诱潜在攻击者,以便在破坏性攻击发生之前识别潜在攻击者。在数据库的隐私和安全方面已经做了大量的工作。尽管攻击的数量和数据库攻击的复杂性日益增加,但还没有人尝试设计蜜罐来保护数据库的隐私。对数据库使用蜜罐将有助于确认可疑用户的怀疑(恶意意图),而不会将目标信息(实现恶意意图的信息)泄露给攻击者。我们提出了一个数据库蜜罐框架,用于隐私环境中某些类型的攻击。提出的数据库蜜罐称为上下文蜜罐。
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Context Honeypot: A Framework for Anticipatory Privacy Violation
Honeypots have been studied in the network domain for detection and information collection against external threats in the past few years. They lure a potential attacker by simulating resources having vulnerabilities and observing the behavior of a potential attacker to identify him before a damaging attack takes place. A lot of work has been done in the area of privacy and security in databases. Though the number of attacks and complexity for database attacks are increasing day by day, there has been no attempt to design honeypots for privacy enforcing databases. The use of honeypots for databases would help in confirming the suspicion (malafide intention) of a suspicious user without leaking the target information (information which would fulfill the malafide intention) to the attacker. We propose a framework for database honeypots for certain types of attacks in privacy context. The proposed honeypots for databases are termed as context honeypots.
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