An efficient classification model for detecting advanced persistent threat
An efficient classification model for detecting advanced persistent threat
复制标题
用于检测高级持续威胁的有效分类模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Vidyapeetham
中科院分区：
文献类型：
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作者：
Saranya Chandran;Hrudya P;P. Poornachandran;Amrita Vishwa;Vidyapeetham
在发生的大多数网络攻击中，最激烈的是高级持续威胁。APT与其他攻击不同，因为它们有多个阶段，通常会在很长一段时间内保持沉默，并由坚定、资金充足的对手发起。这些有针对性的攻击主要集中在政府机构和行业组织，以及参与国际贸易和拥有敏感数据的组织。APT逃避防病毒解决方案、入侵检测和入侵防御系统以及防火墙的检测。在本文中，我们提出了一个分类模型，具有99.8%的准确性，检测APT。
Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.