.png){kind=icon}
返回
Fuzzified Cuckoo based Clustering Technique for Network Anomaly Detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2017.07.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the increasing penetration of security threats, the severity of their impact on the underlying network has increased manifold. Hence, a robust anomaly detection technique, Fuzzified Cuckoo based Clustering Technique (F-CBCT), is proposed in this paper which operates in two phases: training and detection. The training phase is supported using Decision Tree followed by an algorithm based on hybridization of Cuckoo Search Optimization and K-means clustering. In the designed algorithm, a multi-objective function based on Mean Square Error and Silhouette Index is employed to evaluate the two simultaneous distance functions namely-Classification measure and Anomaly detection measure. Once the system is trained, detection phase is initiated in which a fuzzy decisive approach is used to detect anomalies on the basis of input data and distance functions computed in the previous phase. Experimental results in terms of detection rate (96.86%), false positive rate (1.297%), accuracy (97.77%) and F-Measure (98.30%) prove the effectiveness of the proposed model. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Anomaly detection
Feature selection
Decision Tree
Nature inspired algorithm
Cuckoo-search
K-means clustering
Fuzzy theory
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
暂无机构信息
引用论文
An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
分享收藏Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data面向大数据数据流挖掘的加速PSO群搜索特征选择
PREAI
分享收藏
查看更多内容