Research on performance optimizations for TCM-KNN network anomaly detection algorithm

LI Yang1; GUO Li1; LU Tian-bo3; TIAN Zhi-hong1

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Research on performance optimizations for TCM-KNN network anomaly detection algorithm

更新时间：2024-10-14

- Research on performance optimizations for TCM-KNN network anomaly detection algorithm
- Vol. 30, Issue 7, Pages: 13-19(2009)
- 作者机构：
  
  1. 中国科学院计算技术研究所
  2. 中国科学院研究生院
  3. 国家计算机网络应急技术处理协调中心
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393.08
- Published：2009
- 稿件说明：
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LI Yang1, GUO Li1, LU Tian-bo3, et al. Research on performance optimizations for TCM-KNN network anomaly detection algorithm[J]. 2009, 30(7): 13-19.
DOI：

LI Yang1, GUO Li1, LU Tian-bo3, et al. Research on performance optimizations for TCM-KNN network anomaly detection algorithm[J]. 2009, 30(7): 13-19. DOI：

摘要

基于TCM-KNN(transductive confidence machine for K-nearest neighbors)网络异常检测方法

采用过滤器模式的特征选择方法和基于聚类的样本选择方法分别从精简异常检测的特征空间以及选择使用少量高质量的训练样本进行训练

从而高效地对网络异常进行检测。基于著名的KDD Cup1999数据集的实验表明:这2种优化方法在保证TCM-KNN异常检测算法高检测率和低误报率的前提下

极大地减少了该算法的训练开销和检测开销

因而该轻量级检测方法适用于现实的网络应用环境。

Abstract

Based on TCM-KNN（transductive confidence machine for K-nearest neighbors） algorithm

the filter-based feature selection and cluster-based instance selection methods were used towards optimizing it as a lightweight network anomaly detection scheme

which not only reduced its complex feature space

but also acquired high quality instances for training.A series of experimental results demonstrate the two methods for optimizations are actually effective in greatly reducing the computational costs while ensuring high detection performances for TCM-KNN algorithm.Therefore

the two methods make TCM-KNN be a good scheme for a lightweight network anomaly detection in practice.

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Related Author

LI Yang1

FANG Bin-xing1

GUO Li1

TIAN Zhi-hong1

ZHANG Yong-zheng1

JIANG Wei1

YANG Wu1

ZHANG Bing2

Related Institution

Graduate School of Chinese Academy of Sciences

中国科学院研究生院

Information Security Research Center, Harbin Engineering University

School of Computer,National University of Defense Technology

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