命名数据网络中基于增强隔离林的Interest包洪泛攻击检测方法
Extended Isolation Forest-Based Method to Detection against Interest Flooding Attacks in Named Data Networking
投稿时间:2022-01-15  修订日期:2022-01-15
DOI:
中文关键词: 命名数据网络  兴趣包洪泛攻击  增强隔离林  异常分数
英文关键词: Named Data Networking  Interest Flooding Attack  Extended Isolation Forest  Abnormal scores
基金项目:中南民族大学2021年研究生学术创新基金项目,国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位邮编
邢光林 中南民族大学计算机科学学院 
霍红 中南民族大学计算机科学学院 430074
侯睿 中南民族大学计算机科学学院 
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中文摘要:
      IFA是NDN中的恶意攻击之一,危害较大。针对IFA,本文提出基于增强隔离林的IFA检测方法,通过在构造增强隔离林的过程中区分Interest包所携带的正常前缀与异常前缀,再对异常前缀进行进一步判断,从而准确的检测出恶意前缀。该方法将检测出的恶意前缀列入黑名单中,限制携带恶意前缀的Interest包转发。仿真结果表明,该方法能够有效地缓解IFA。
英文摘要:
      Interest Flooding Attack (IFA) is one of the malicious attacks in Named Data Networking (NDN), which is more harmful. For IFA, this paper proposes an IFA detection method based on Extended Isolation Forest, which distinguishes normal prefixes and abnormal prefixes carried by Interest packets in the process of constructing Extended Isolation Forest, and then makes further judgments on abnormal prefixes to accurately detect malicious prefixes. The method includes the detected malicious prefixes in the blacklist and restricts the forwarding of Interest packets carrying malicious prefixes. Simulation results show that the method can effectively mitigate IFA.
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