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
相关论文
共 50 条
  • [31] Cloud Based Intrusion Detection and Prevention System for Industrial Control Systems Using Software Defined Networking
    Brugman, Jonathon
    Khan, Mohammed
    Kasera, Sneha
    Parvania, Masood
    2019 RESILIENCE WEEK (RWS), 2019, : 98 - 104
  • [32] Correction: Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach
    Sultan Zavrak
    Murat Iskefiyeli
    Neural Computing and Applications, 2023, 35 : 18091 - 18091
  • [33] BPNN-based flow classification and admission control for software defined IIoT
    Wang, Cheng
    Xue, Hai
    Huan, Zhan
    IET COMMUNICATIONS, 2024, 18 (15) : 882 - 896
  • [34] Feedback based Sampling for Intrusion Detection in Software Defined Network
    Shi, Jiangyong
    Zeng, Yingzhi
    Wang, Wenhao
    Yang, Yuexiang
    ICCSP 2018: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON CRYPTOGRAPHY, SECURITY AND PRIVACY, 2018, : 95 - 99
  • [35] Intrusion Detection System based on Software Defined Network Firewall
    Sayeed, Mohd Abuzar
    Sayeed, Mohd Asim
    Saxena, Sharad
    2015 1ST INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING TECHNOLOGIES (NGCT), 2015, : 379 - 382
  • [36] DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking
    Tuan Anh Tang
    Mhamdi, Lotfi
    McLernon, Des
    Zaidi, Syed Ali Raza
    Ghogho, Mounir
    El Moussa, Fadi
    ELECTRONICS, 2020, 9 (09) : 1 - 18
  • [37] A multi-layered intrusion detection system for software defined networking
    Bour, Hamideh
    Abolhasan, Mehran
    Jafarizadeh, Saber
    Lipman, Justin
    Makhdoom, Imran
    COMPUTERS & ELECTRICAL ENGINEERING, 2022, 101
  • [38] A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking Environments
    Khalid, Harman Yousif Ibrahim
    Aldabagh, Najla Badie Ibrahim
    ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2024, 14 (02) : 13190 - 13200
  • [39] Deep Learning Approach for Network Intrusion Detection in Software Defined Networking
    Tang, Tuan A.
    Mhamdi, Lotfi
    McLernon, Des
    Zaidi, Syed Ali Raza
    Ghogho, Mounir
    2016 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS (WINCOM), 2016, : P258 - P263
  • [40] Standardization progress in software defined networking/open flow
    Nakajima, Yoshihiro
    NTT Technical Review, 2013, 11 (02):