CFP last date
20 January 2025
Reseach Article

Gradient Controlled-BP Algorithm for Effective Intrusion Detection

by Priyanka, Shekhar Sengar, S.C. Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 10
Year of Publication: 2015
Authors: Priyanka, Shekhar Sengar, S.C. Gupta
10.5120/ijca2015905543

Priyanka, Shekhar Sengar, S.C. Gupta . Gradient Controlled-BP Algorithm for Effective Intrusion Detection. International Journal of Computer Applications. 123, 10 ( August 2015), 26-32. DOI=10.5120/ijca2015905543

@article{ 10.5120/ijca2015905543,
author = { Priyanka, Shekhar Sengar, S.C. Gupta },
title = { Gradient Controlled-BP Algorithm for Effective Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 10 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number10/21996-2015905543/ },
doi = { 10.5120/ijca2015905543 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:21.190226+05:30
%A Priyanka
%A Shekhar Sengar
%A S.C. Gupta
%T Gradient Controlled-BP Algorithm for Effective Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 10
%P 26-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

High level security maintenance is very important nowadays for safe and trusted communication over the internet but due to enormous interconnectivity this task has become very complex. Threat of intrusions and misuses is always present in communication over the internet and any other network. These intrusions are occurring at higher rates than before and additionally existing security products are not able to detect these. Neural networks can help in this problem and not only can the known but unknown intrusions also be detected with certain efficiency. Due to high error rate and low detection rate BP algorithm’s efficiency is not unto mark. So this research has used a Gradient based BP algorithm for detection of intrusions considering all 41 inputs from dataset. It shows how learning with Gradient-based BP algorithm and testing it in real time can improve efficiency. The desired results that are low false detection and high accuracy are achieved with this and for better results KDD99 datasets are also filtered.

References
  1. Corchado, Emilio, and Álvaro Herrero. "Neural visualization of network traffic data for intrusion detection." Applied Soft Computing 11, no. 2 (2011): 2042-2056.
  2. Modi, Chirag, Dhiren Patel, Bhavesh Borisaniya, Hiren Patel, Avi Patel, and Muttukrishnan Rajarajan. "A survey of intrusion detection techniques in cloud." Journal of Network and Computer Applications 36, no. 1 (2013): 42-57.
  3. Casas, Pedro, Johan Mazel, and Philippe Owezarski. "Unsupervised network intrusion detection systems: Detecting the unknown without knowledge." Computer Communications 35, no. 7 (2012): 772-783.
  4. Casas, Pedro, Johan Mazel, and Philippe Owezarski. "Unsupervised network intrusion detection systems: Detecting the unknown without knowledge." Computer Communications 35, no. 7 (2012): 772-783.
  5. Robert Mitchell and Ing-Ray Chen. "A survey of intrusion detection techniques for cyber-physical systems." ACM Computing Surveys (CSUR) 46, no. 4 (2014): 55.
  6. Amin Dastanpour Suhaimi Ibrahim, and Reza Mashinchi. "Using Genetic Algorithm to Supporting Artificial Neural Network for Intrusion Detection System." In The International Conference on Computer Security and Digital Investigation (ComSec2014), pp. 1-13. The Society of Digital Information and Wireless Communication, 2014.
  7. Alina Oprea Zhou Li, Ting-Fang Yen, Sang Chin, and Sumayah Alrwais. "Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data." arXiv preprint arXiv: 1411.5005 (2014).
  8. Liyuan Xiao Yetian Chen, and Carl K. Chang. "Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection." In Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International, pp. 128-133. IEEE, 2014.
  9. M .Govindarajan "Hybrid Intrusion Detection Using Ensemble of Classification Methods." IJ Computer Network and Information Security 2 (2014): 45-53.
  10. Weiming Hu Jun Gao, Yanguo Wang, Ou Wu, and Stephen Maybank. "Online adaboost-based parameterized methods for dynamic distributed network intrusion detection." Cybernetics, IEEE Transactions on 44, no. 1 (2014): 66-82.
  11. Mradul Dhakar and Akhilesh Tiwari. "A Novel Data Mining based Hybrid Intrusion Detection Framework." Journal of Information and Computing Science 9, no. 1 (2014): 037-048.
  12. Laheeb Mohammad Ibrahim, "Anomaly network intrusion detection system based on distributed time-delay neural network (DTDNN)." Journal of Engineering Science and Technology 5, no. 4 (2010): 457-471.
  13. Susan C Lee and David V. Heinbuch. "Training a neural-network based intrusion detector to recognize novel attacks." Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 31, no. 4 (2001): 294-299.
  14. KDD-CUP-99 Task Description; http://kdd.ics.uci.edu/databases/kddcup99/task.html
  15. H.G.Kayacık,A.N.Zincir-Heywood,M.I. Heywood, “Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets”, May 2005.
  16. Manoranjan Pradhan, Sateesh Kumar Pradhan, Sudhir Kumar Sahu, "Anomaly Detection Using Artificial Neural Network"International Journal of Engineering Sciences & Emerging Technologies, April 2012, Volume 2, Issue 1, pp: 29-36.
Index Terms

Computer Science
Information Sciences

Keywords

Network Security Intrusion Detection Neural Networks Back Propagation Gradient.