Flow Based Classification for Specification Based Intrusion Detection in Software Defined Networking: Flow Classify

被引:0
|
作者
Sampath, Nithya [1 ]
Dinakaran, M. [1 ]
机构
[1] Vellore Inst Technol, Vellore, Tamil Nadu, India
关键词
Fuzzy-Association; Intrusion Detection; Network Management; Rule Mining; Software Defined Networking; TAXONOMY;
D O I
10.4018/IJSI.2019040101
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Software defined networking assures the space for network management, SDNs will possibly replace traditional networks by decoupling the data plane and control plane which provides security by means of a global visibility of the network state. This separation provides a solution for developing secure framework efficiently. Open flow protocol provides a programmatic control over the network traffic by writing rules, which acts as a network attack defence. A robust framework is proposed for intrusion detection systems by integrating the feature ranking using information gain for minimizing the irrelevant features for SDN, writing fuzzy-association flow rules and supervised learning techniques for effective classification of intruders. The experimental results obtained on the KDD dataset shows that the proposed model performs with a higher accuracy, and generates an effective intrusion detection system and reduces the ratio of attack traffic.
引用
收藏
页码:1 / 8
页数:8
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