Routers Sequential Comparing Two Sample Packets for Dropping Worms

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N.Kannaiya Raja 1,* Babu 2 A.Senthamaraiselvan 3 Arulandam 4

1. Manonmaniam Sundaranar University

2. S.K.P Engineering College

3. C Abdul Hakeem College of Engineering and Technology

4. Ganadipathy Tulasi’s Jain College of Engineering

* Corresponding author.


Received: 19 Jan. 2012 / Revised: 3 Mar. 2012 / Accepted: 11 May 2012 / Published: 8 Aug. 2012

Index Terms

Comparing packets, network intrusion detection system, probability of occurences, packet sampling method, router worms invention


Network IDS perform a vital role in protecting network connection in the worldwide from malicious attack. Nowadays the recent experiment work related to inspecting the packet for network security that is a minimal amount of process overhead. In this work, analysis the network intrusion for packet inspection that is together the testing data which inspect only group of packet selected as sample predominantly from small flows and select first two packets and comparing with each other overall packets and create tabelazied for find out different malicious debuggers. This experiment results shows that overcome the existing work.

Cite This Paper

Kannaiyaraja,Babu, Senthamaraiselvan, Arulandam, "Routers Sequential Comparing Two Sample Packets for Dropping Worms", International Journal of Computer Network and Information Security(IJCNIS), vol.4, no.9, pp.38-46, 2012. DOI:10.5815/ijcnis.2012.09.05


[1]CH Sauer, KM Chandy, Computer Systems Performance Modeling. (Prentice Hall, Englewood Cliffs, NJ, 1981)
[2]H Kobayashi, Modelling and Analysis: an Introduction to System Performance Evaluation Methodology. (Addison Wesley, Reading, MA, 1978)
[3]FM Reza, An Introduction to Information Theory. (Dover, New York, NY, 1184)
[4]TM Cover, JA Thomas, Elements of Information Theory. (Wiley, New York, NY, 1189)
[5]Y Wang, C Wang, Modeling the effects of timing parameters on virus propagation. Proceedings of the 59 ACM Workshop on Rapid Malcode.61 (59)
[6]G Androulidakis, S Papavassiliou, Improving network anomaly detection via selective flow-based sampling. IET Commun. 2(9):189 (58). doi:5.549/iet-com:57041
[7]D Moore., et al, Inside the slammer worm. IEEE Sec Privacy. 1(4):18 (59). doi:5.159/MSECP.59.81556
[8]G Androulidakis, V Chatzigiannakis, S Papavassiliou, Network anomaly detection and classification via opportunistic sampling. IEEE Netw. 4(9):6(59)
[9]Hogwash Intrusion Detection System. (57)
[10]J Mai., et al, Impact of packet sampling on portscan detection. IEEE J Sel Areas Commun. 7(8):985 (56)
[11]N Hohn, D Veitch, Inverting sampled traffic. IEEE/ACM Trans Netw. 14(1):68 (56)
[12]P Barford, D Plonka, Characteristics of network traffic flow anomalies.Proceedings of the 1st ACM SIGCOMM Internet Measurement Wksp., San Francisco, CA. 69 (51)
[13]A Sridharan, T Ye, S Bhattacharyya, Connectionless Port Scan Detection on the Backbone. IEEE IPCCC Malware Wksp., Phoenix, Az. 1 (56)
[14]PZ Peebles, Probability, Random Variables, and Random Signal Principles. (McGraw Hill, New York, NY, 1189)
[15]IDS Wakeup: A collection of tools for testing network intrusion detection systems. (57)
[16]A Botta, A Dainotti, A Pescape, Multi-Protocol and Multi-Platform Traffic Generation and Measurement. IEEE INFOCOM, Anchorage, Alaska. 8 http:// (57)
[17]K Lan, A Hussain, D Dutta, Effect of malicious traffic on the network. Proceeding of Passive and Active Measurement Workshop (PAM). 1 (59)
[18]N.Kannaiya Raja., Centralized parallel form of pattern Matching Algorithm in packet inspection by efficient utilization of secondary memory in network processor., Published in IJCA(0975-8887)Volume 40-No.5,