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An anticipatory reasoning-reacting system for defending against malice anticipatorily

机译:一种预防性预防恶意行为的预期推理系统

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Today, information security of information systems is no longer about confidentiality, integrity and availability, but about ensuring that the systems are predictably dependable in the face of all sorts of malice. Although intrusion detection systems (IDS) make big progress on defending against computing malice, there is still a gap between current IDSs and ideal malice defense systems. On the other hand, anticipatory reasoning-reacting systems (ARRS) were proposed as a high secure system with the ability to defend against malice anticipatorily, however, until now, there is no concrete implementation of ARRS for security, as well as no evidence showing the practical usefulness of anticipatory computing for security. As a step towards to ideal secure systems, we designed and implemented an ARRS for malice defense, which can adapt to different application by configuring different information source, anticipatory model, and anticipatory actions. We also evaluated our system by KDD99 dataset and a case study of web server. This paper proposes what features ideal malice defense systems should have, points out the gap between current IDSs and ideal malice defense systems, shows why some advantages of ARRSs could contribute ideal malice defense systems, and presents and evaluates a practical implementation of ARRS for security
机译:如今,信息系统的信息安全已不再与机密性,完整性和可用性有关,而是与确保系统在面对各种恶意时可预测地可靠有关。尽管入侵检测系统(IDS)在抵御计算机恶意攻击方面取得了长足的进步,但当前的IDS与理想的恶意防御系统之间仍然存在差距。另一方面,人们提出了预期推理系统(ARRS),它是一种能够预先防御恶意的高安全性系统,但是,到目前为止,ARRS的安全性尚无具体实现,也没有证据表明预期计算对安全性的实际实用性。为了朝着理想的安全系统迈进,我们设计并实现了用于恶意防御的ARRS,它可以通过配置不同的信息源,预期模型和预期措施来适应不同的应用。我们还通过KDD99数据集和Web服务器案例研究对我们的系统进行了评估。本文提出了理想的恶意防御系统应具备的功能,指出了当前IDS与理想的恶意防御系统之间的差距,说明了ARRS的某些优势为何可以有助于理想的恶意防御系统,并提出并评估了ARRS在安全性方面的实际实现方式

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