CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
Reseach Article

Survey on Classification, Detection and Prevention of Network Attacks using Rule based Approach

by Wrushal K. Kirnapure, Arvind R. Bhagat Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 5
Year of Publication: 2017
Authors: Wrushal K. Kirnapure, Arvind R. Bhagat Patil

Wrushal K. Kirnapure, Arvind R. Bhagat Patil . Survey on Classification, Detection and Prevention of Network Attacks using Rule based Approach. International Journal of Computer Applications. 162, 5 ( Mar 2017), 11-17. DOI=10.5120/ijca2017913291

@article{ 10.5120/ijca2017913291,
author = { Wrushal K. Kirnapure, Arvind R. Bhagat Patil },
title = { Survey on Classification, Detection and Prevention of Network Attacks using Rule based Approach },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 5 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913291 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:08:09.882362+05:30
%A Wrushal K. Kirnapure
%A Arvind R. Bhagat Patil
%T Survey on Classification, Detection and Prevention of Network Attacks using Rule based Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 5
%P 11-17
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Intrusion detection systems(IDS) has assumes an important part to protect the qualities of PC mostly into two classifications: malignant and irrelevant exercises. Intrusion detection can be accomplish by Categorization. Another machine learning based algorithm for order of information is actualized to network intrusion detection is presented in this paper. The most basic employment is to separate exercises of network are as ordinary or irrelevant while decreasing the misclassification. The goal of Intrusion detection framework (IDS) are to apply all the accessible data keeping in mind the end goal to distinguish the attacks by outcast programmers and abuse of insiders. For Network intrusion detection there are diverse arrangement models have been produced, the most regularly connected strategies are Support Vector Machine(SVM) and Ant Colony both consider their qualities and shortcomings independently. To diminishes the shortcoming, blend of the SVM technique with Ant Colony to take the advantages ofboth . A standard benchmark of information set KDD99 is assessed and actualized as another algorithm. Despite the fact that to increment both the grouping rate and runtime adequacy it is important to actualize the Combining Support Vectors with Ant Colony which beat SVM alone . An individual continuous network dataset and a notable dataset i.e. KDD99 CUP has been actualized as proposed framework. All attack sorts, detection rate, detection speed, false alert rate can be measured by execution of intrusion detection framework IDS.

  1. Cuiwei Li,Qin Tu,MaozhengZhao,JunXu,Aidong Men,Amultiscale compressed video saliency detection model based on ant colony optimization, 2015 IEEE/CIC International Conference on Communications in China (ICCC) Year: 2015
  2. Hongxin Liu; JunzhongJi; Cuicui Yang; JiaweiLv; Xiuzhen Zhang,Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks , 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
  3. Abhishek Gupta; Om JeePandey; MahendraShukla; Anjali Dadhich; Anup Ingle; Vishal Ambhore,Intelligent Perpetual Echo Attack Detection on User Datagram Protocol Port 7 Using Ant Colony Optimization , 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies
  4. Brian C. Williams; Errin W. Fulp, A Biologically Modeled Intrusion Detection System for Mobile Networks , 2010
  5. International Conference on Broadband, Wireless Computing, Communication and Applications Xiaojing Yuan; Zehang Sun; Y. Varol; G. Bebis ,A distributed visual surveillance systemProceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
  6. Zohreh Sadat Hosseini; Seyyed Javad Seyyed Mahdavi Chabok; Seyyed Reza Kamel, DOS intrusion attack detectionby using of improved SVR , 2015 International Congress on Technology, Communication and Knowledge (ICTCK)
  7. FaridLawan Bello; KiranRavulakollu; Amrita,Analysis and evaluation of hybrid intrusion detection system models , 2015 International Conference on Computers, Communications, and Systems (ICCCS .
  8. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  9. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  10. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  11. Salah EddineBenaicha; LaliaSaoudi; Salah Eddine Bouhouita Guermeche; OuardaLounis,Intrusion detection system using genetic algorithm2014 Science and Information Conference.
  12. Fan Li, Hybrid Neural Network Intrusion Detection System Using Genetic Algorithm 2010 International Conference on Multimedia Technology.
  13. YogitaDanane; ThaksenParvat, Intrusion detection system using fuzzy genetic algorithm2015 International Conference on Pervasive Computing (ICPC).
  14. FatemehBarani, A hybrid approach for dynamic intrusion detection in ad hoc networks using genetic algorithm and artificial immune system ,2014 Iranian Conference on Intelligent Systems (ICIS).
  15. AmiraSayed A. Aziz; MostafaSalama; AboulellaHas sanien; Sanaa EL-Ola Hanafi , Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system , 2012 Federated Conference on Computer Science and Information Systems (FedCSIS)
  16. K G Srinivasa; Saumya Chandra; SiddharthKajaria; Shilpita Mukherjee , IGIDS: Intelligent intrusion detection system using genetic algorithms , 2011 World Congress on Information and Communication Technologies
  17. Jungwon Kim; P. J. Bentley , ”Towards an artificial immune system for network intrusion detection: an investigation of dynamic clonal selectionEvolutionary Computation, 2002. CEC ’02.Proceedings of the 2002.
  18. TahirMehmood; Helmi B MdRais , SVM for network anomaly detection using ACO feature subset , 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC)
  19. Mohammad SanieeAbadeh; JafarHabibi; EmadSoroush, ”Induction of Fuzzy Classification Systems Using Evolutionary ACO-Based AlgorithmsFirst Asia International Conference on Modelling Simulation (AMS’07)
  20. VidhyaSathish; P. Sheik Abdul Khader , A proposed hybrid framework for improving supervised classifiersdetection aecuraev over intrusion trace2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)
  21. SakchiJaiswal; KhushbooSaxena; Amit Mishra; Shiv K. Sahu , A KNN-ACO approach for intrusion detection using KDDCUP’99 dataset2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com)
  22. Zohreh Sadat Hosseini; Seyyed Javad Seyyed Mahdavi Chabok; Seyyed Reza Kamel , DOS intrusion attack detection by using of improved SVR2015 International Congress on Technology, Communication and Knowledge (ICTCK)
  23. Xu Yang; Zhao Hui , Intrusion Detection Alarm Filtering Technology Based on Ant Colony Clustering Algorithm2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)
  24. R. Chandrasekar; R. K. Suresh; S. G. Ponnambalam, ”Evaluating an Obstacle Avoidance Strategy to Ant Colony Optimization Algorithm for Classification in Event Logs2006 International Conference on Advanced Computing and Communications
  25. Yong Feng; Zhong-Fu Wu; Kai-Gui Wu; Zhong-Yang Xiong; Ying Zhou , An unsupervised anomaly intrusion detection algorithm based on swarm intelligence2005 International Conference on Machine Learning and Cybernetics
  26. Kathleen Goeschel , Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysisSoutheastCon 2016
  27. R. Ravinder Reddy; Y. Ramadevi; K. V. N Sunitha , Effective discriminant function for intrusion detection using SVM2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
  28. Manjiri V. Kotpalliwar; RakhiWajgi , ”Classification of Attacks Using Support Vector Machine (SVM) on KDD- CUP’99 IDS Database2015 Fifth International Conference on Communication Systems and Network Technologies
  29. TheyaznHassnHadi; Manish R. Joshi , Handling ambiguous packets in intrusion detection2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN)
  30. A. S Subaira; P. Anitha , ”Efficient classification mechanism for network intrusion detection system based on data mining techniques: A survey2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)
  31. A. M. Chandrasekhar; K. Raghuveer , Confederation of FCM clustering, ANN and SVM techniques to implement hybrid NIDS using corrected KDD cup 99 dataset2014 International Conference on Communication and Signal Processing
Index Terms

Computer Science
Information Sciences


Network SVM Ant Colony KDDCUP 99 Dataset.