通过自我保护自治规则生成对抗以网络为中心的内部威胁

Faisal M. Sibai, D. Menascé
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引用次数: 7

摘要

在当今的组织中,内部威胁是一个日益严重的问题。检测此类攻击尤其具有挑战性,因为大多数系统所有者和系统管理员使用网络远程管理他们负责的系统。在之前的工作中,我们介绍了自主违章预防系统(AVPS),该系统具有可扩展的架构来处理此类威胁。该系统使用低级的人工指定和手动输入的规则来保护网络应用程序免受心怀不满的特权用户的攻击。然而,当规则数量太大时,基于规则的系统通常难以维护。本文通过允许人们输入较少数量的高级规则来解决这个问题,这些高级规则会根据对传入网络流量的分析自动转换为一个或多个低级规则。本文讨论了各种高级规则(HLR)如何在不需要任何用户干预的情况下检测新的不想要的行为。在FTP、数据库和Web三种类型的应用程序上进行的实验表明,增强的AVPS可以通过高级规则和过程自动化检测已知和未知的内部攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Countering Network-Centric Insider Threats through Self-Protective Autonomic Rule Generation
Insider threats are a growing problem in today's organizations. Detecting such attacks is especially challenging because most system owners and system administrators use networks to remotely manage the systems they are responsible for. In previous work, we introduced the Autonomic Violation Prevention System (AVPS) that has a scalable architecture to deal with such threats. This system uses low level human-specified and manually-entered rules to protect networked applications from disgruntled privileged users. However, rule-based systems are generally difficult to maintain when the number of rules is too large. This paper addresses this problem by allowing human beings to enter a smaller number of high-level rules that are automatically translated into one or more low-level rules based on an analysis of the incoming network traffic. The paper discusses how various high level rules (HLR) can detect new unwanted behaviors without any user intervention. Experiments conducted on three types of applications -- FTP, database, and Web -- show that the enhanced AVPS can detect known and unknown insider attacks through high level rules and process automation.
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