A new approach to intrusion detection in databases by using artificial neuro fuzzy inference system

被引：0
|
作者：
Department of Computer Science and Engineering and IT, Veer Surendra Sai University of Technology, Burla, Odisha, India [1 ]
机构：
[1] Department of Computer Science and Engineering and IT, Veer Surendra Sai University of Technology, Burla, Odisha
来源：
Int. J. Reasoning based Intell. Syst. | / 3-4卷 / 254-260期
关键词：
ANFIS; ANN; Artificial neural network; Artificial neuro fuzzy inference system; Database security; FIS; Fuzzy inference system; Intrusion detection;
D O I：
10.1504/IJRIS.2015.072952
中图分类号：
学科分类号：
摘要：
In this modern era of internet, security of data has become a primary concern due to exposure of databases on the web. The present study approaches the problem of database intrusion detection from a pattern recognition point of view, where artificial neuro fuzzy inference system (ANFIS) is used to capture user behavioural patterns. In this paper, we have proposed a database intrusion detection system using ANFIS as a classifier that is capable of outperforming in many ways and better suits the demands and dynamic nature of the problem. The proposed approach to intrusion detection gives a better detection rate and lowers the false positive rate compared to other traditional techniques. © 2015 Inderscience Enterprises Ltd.
引用
收藏
页码：254 / 260
页数：6
相关论文
共 50 条
  • [1] Rule-based database intrusion detection using coactive artificial neuro-fuzzy inference system and genetic algorithm
    Brahma A.
    Panigrahi S.
    Samal N.
    Gountia D.
    International Journal of Business Intelligence and Data Mining, 2022, 21 (01) : 85 - 101
  • [2] Training of Intelligent Intrusion Detection System using Neuro Fuzzy
    Panja, Biswajit
    Ogunyanwo, Olugbenga
    Meharia, Priyanka
    2014 15TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2014, : 95 - 100
  • [3] A Neural Network Approach to Edge Detection using Adaptive Neuro - Fuzzy Inference System
    Anwar, Shamama
    Raj, Sugandh
    2014 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2014, : 2432 - 2435
  • [4] Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system
    Manimurugan, S.
    Majdi, Al-qdah
    Mohmmed, Mustaffa
    Narmatha, C.
    Varatharajan, R.
    MICROPROCESSORS AND MICROSYSTEMS, 2020, 79
  • [5] An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach
    Kiran, T. Ravi
    Rajput, S. P. S.
    APPLIED SOFT COMPUTING, 2011, 11 (04) : 3525 - 3533
  • [6] Decision making support system for medical devices maintenance using artificial neuro fuzzy inference system
    AlSukker A.
    Afiouni N.
    Etier M.
    Jreissat M.
    International Journal of Industrial and Systems Engineering, 2023, 45 (04) : 484 - 500
  • [7] Fault detection and diagnosis of pneumatic valve using Adaptive Neuro-Fuzzy Inference System approach
    Subbaraj, P.
    Kannapiran, B.
    APPLIED SOFT COMPUTING, 2014, 19 : 362 - 371
  • [8] An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System
    Shyla, S. Immaculate
    Sujatha, S. S.
    INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY, 2020, 14 (04) : 22 - 41
  • [9] A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers
    Toosi, Adel Nadjaran
    Kahani, Mohsen
    COMPUTER COMMUNICATIONS, 2007, 30 (10) : 2201 - 2212
  • [10] On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection
    Liu, Jia
    Wang Yinchai
    Siong, Teh Chee
    Li, Xinjin
    Zhao, Liping
    Wei, Fengrui
    PLOS ONE, 2022, 17 (12):
12345