Scholarly Research Excellence

Digital Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29050


Select areas to restrict search in scientific publication database:
10007929
A Distributed Mobile Agent Based on Intrusion Detection System for MANET
Abstract:
This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).
Digital Object Identifier (DOI):

References:

[1] Tiranuch. Anantvalee. and Jie. Wu, “A Survey on Intrusion Detection in Mobile Ad Hoc Networks, Wireless/Mobile Network Security,” Springer, 2006.
[2] Ovais. Ahmad. Khan, "A Survey of Secure Routing Techniques for MANET",http://ovais.khan.tripod.com/papers/Secure_Routing_MANET.pdf, 2010.
[3] Ernesto. Jiménez. Caballero,"Vulnerabilities of Intrusion Detection Systems in Mobile Ad-hoc Networks -The routing problem}," http://www.tml.tkk.fi/Publications/C/22/papers/Jimenez_final.pdf, 2006.
[4] Yanchao. Zhang, Wenjing. Louy, Wei. Liu and Yuguang. Fang, “A Secure Incentive Protocol for Mobile Ad Hoc Networks}, Wireless Networks,” springer 2006.
[5] Satria. Mandala, Md. Asri. Ngadi and A. Hanan. Abdullah, “A Survey on MANET Intrusion Detection, International Journal of Computer Science and Security,” August 2007.
[6] Mirza. Cilimkovic, “Neural Networks and Back Propagation Algorithm, Institute of Technology Blanchardstown,” Ireland, 2014.
[7] Arjita. Ghosh and Sandip. Sen, "Agent-Based Distributed Intrusion Alert System," Springer, 2005.
[8] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over - Sampling Technique," Journal of Artificial Intelligent Research, Vol. 16, pp. 321-357, 2002. Cited on page 126.
[9] S. Madhavi and Tai. Hoon Kim, "An Intrusion Detection System in Mobile Adhoc Networks,” International Journal of Security and Its Applications, Vol. 2, No.3, July, 2008.
[10] Angelo. Rossi and Samuel. Pierre, “Collusion-resistant reputation-based intrusion detection system for MANETs" International Journal of Computer Science and Network Security, VOL.9 No.11, November 2009.
[11] O. Depren, M. Topallar, E. Anarim, and M. k. Ciliz, “An Intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks," Expert Syst. Appl, vol. 29, no. 4, pp. 713-722, 2005. Cited on pages 47 and 123.
[12] Bo. Sun, Kui. Wu and Udo. W. Pooch, “Zone-Based Intrusion Detection for Mobile Ad Hoc Networks," http://webhome.cs.uvic.ca/~wkui/research/IDS.pdf, 2010,
[13] S. T. Powers and J. He, " A hybrid artificial immune system and self organizing map for network intrusion detection,” Information Sciences, vol. 178, no. 15, pp. 3024-3042, 2008. Cited on page 48.
[14] M. Hall and G. Holmes, "Benchmarking attribute selection techniques for discrete class data mining," IEEE Transaction on Knowledge and Data Engineering, vol. 15, no.6, pp.1437-1447, 2003. Cited on page 88 and 93.
Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007