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A Dynamic Intrusion Detection System Based on Multivariate Hotelling’s T2Statistics Approach for Network Environments

机译：一种基于多变量热灵T2Statistics方法的网络环境动态入侵检测系统

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摘要

The ever expanding communication requirements in today’s world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling’s T2method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling’s T2statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup’99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.
机译：在当今世界的沟通要求中，在当今的世界需求广泛而高效的网络系统中，具有同样有效可靠的安全功能，用于安全，自信和安全的通信和数据传输。因此，为任何网络环境提供有效的安全协议，假设至关重要。尝试持续设计更高效和动态的网络入侵检测模型。在这项工作中，基于Hotelling的T2Method的方法，多变量统计分析技术已经用于入侵检测，尤其是网络环境。诸如预处理，多变量统计分析和攻击检测的组分已被纳入开发多元热的T2Statistic模型，并且基于T范围距离度量生成必要的轮廓。使用使用中央限位定理获得的阈值范围，观察到的流量配置文件已被分类为正常或攻击类型。模型的性能，通过使用KDD Cup'99数据集进行验证和测试评估，为所有类的验证速率显示出非常高的检测率，具有低误报率。与现有模型相比，这项工作中呈现的模型的准确性已经被发现更好。

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