Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms

被引:90
|
作者
Selva, Deepaa [1 ]
Nagaraj, Balakrishnan [2 ]
Pelusi, Danil [3 ]
Arunkumar, Rajendran [2 ]
Nair, Ajay [2 ]
机构
[1] Karpagam Univ, Dept Elect & Commun Engn, Coimbatore 641021, Tamil Nadu, India
[2] Rathinam Grp Inst, Rathinam Tech Campus, Coimbatore 641021, Tamil Nadu, India
[3] Univ Teramo, Fac Commun Sci, I-64100 Teramo, Italy
关键词
selection techniques; intrusion detection; neural networks; fuzzy concepts; PHISHING DETECTION;
D O I
10.3390/a14080224
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Special Collection: Intelligent algorithms and optimization with applications
    Cai, Xiao-Yun
    Yin, He-Feng
    JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2020, 14
  • [32] A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement
    Wang, Yunhui
    Liu, Zifei
    Zheng, Weichu
    Wang, Jinyan
    Shi, Hongjian
    Gu, Mingyu
    APPLIED SCIENCES-BASEL, 2023, 13 (14):
  • [33] Network management system with intrusion prevention
    Chen, YC
    Wang, TC
    2005 Beijing International Conference on Imaging: Technology and Applications for the 21st Century, 2005, : 180 - 181
  • [34] Optimal Feature Selection Methods for Chronic Kidney Disease Classification using Intelligent Optimization Algorithms
    Lambert J.R.
    Perumal E.
    Recent Advances in Computer Science and Communications, 2021, 14 (09) : 2886 - 2898
  • [35] Intelligent distribution of intrusion prevention services on programmable routers
    Hess, Andreas
    Geerdes, Hans-Florian
    Wessaely, Roland
    25TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS, VOLS 1-7, PROCEEDINGS IEEE INFOCOM 2006, 2006, : 1386 - +
  • [36] Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing
    Krishnaveni, S.
    Sivamohan, S.
    Sridhar, S. S.
    Prabakaran, S.
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2021, 24 (03): : 1761 - 1779
  • [37] Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing
    S. Krishnaveni
    S. Sivamohan
    S. S. Sridhar
    S. Prabakaran
    Cluster Computing, 2021, 24 : 1761 - 1779
  • [38] An Intelligent Intrusion Prevention System for Cloud Computing (SIPSCC)
    Alqahtani, Saeed M.
    Al Balushi, Maqbool
    John, Robert
    2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 2, 2014, : 152 - 158
  • [39] Research on Intelligent Detection of Intrusion Data in Network
    Zhu, Guangjie
    Yao, Honglei
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 5 - 10
  • [40] Intelligent Bayesian classifiers in network intrusion detection
    Bosin, A
    Dessì, N
    Pes, B
    INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE, 2005, 3533 : 445 - 447