首页> 外文学位 >Use of double-loop learning to combat advanced persistent threat: Multiple case studies.
【24h】

Use of double-loop learning to combat advanced persistent threat: Multiple case studies.

机译:使用双循环学习来对抗持续存在的持续威胁:多个案例研究。

获取原文
获取原文并翻译 | 示例

摘要

The Advanced Persistent Threat (APT) presents an ever present and more growing threat to organizations across the globe. Traditional Information Technology (IT) incident response falls short in effectively addressing this threat. This researcher investigated the use of single-loop and double-loop learning in two organizations with internal incident response processes designed to combat the APT. Two cases were examined within organizations employing an internal incident response team. The third case was examined from an organization providing incident response as a service in addressing APT compromises. The study developed four themes: the inefficacy of single-loop learning in addressing APT, the need for better visibility within corporate infrastructure, the need for continuous improvement and bi-directional knowledge flow, and the need for effective knowledge management. Based on these themes, a conceptual model was developed modifying the traditional incident response process. Three implications were derived from the research. First, perimeter defense falls short when addressing the APT. Second, the preparation phase of incident response requires modification along with the addition of a new baseline loop phase running contiguously with the entire process. Finally, opportunistic learning needs to be encouraged in addressing the APT.
机译:高级持久威胁(APT)对全球组织提出了越来越多的威胁。传统信息技术(IT)的事件响应未能有效解决此威胁。这位研究人员调查了两个组织内部事件响应流程旨在与APT对抗的两个组织中单环和双环学习的使用。在雇用内部事件响应小组的组织内部检查了两个案例。从提供事件响应作为解决APT折衷的服务的组织检查了第三起案件。这项研究提出了四个主题:单环学习在解决APT方面的效率低下,对企业基础架构内更好的可见性的需求,持续改进和双向知识流的需求以及对有效知识管理的需求。基于这些主题,开发了一个概念模型来修改传统的事件响应过程。这项研究得出了三点启示。首先,在应对APT时,外围防御能力不足。其次,事件响应的准备阶段需要进行修改,并增加一个与整个过程连续运行的新基线循环阶段。最后,在解决APT时需要鼓励机会主义学习。

著录项

  • 作者

    Lamb, Christopher J.;

  • 作者单位

    Capella University.;

  • 授予单位 Capella University.;
  • 学科 Information Technology.;Information science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 151 p.
  • 总页数 151
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号