The week's pick
Random Articles
Reseach Article
A Model for Computer Worm Detection in a Computer Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 66 - Number 2 |
| Year of Publication: 2013 |
| Authors: Adebayo O. T, Alese B. K, Gabriel A. J |
10.5120/11056-5961
|
Adebayo O. T, Alese B. K, Gabriel A. J . A Model for Computer Worm Detection in a Computer Network. International Journal of Computer Applications. 66, 2 ( March 2013), 22-28. DOI=10.5120/11056-5961
Abstract
This research presents a novel approach to detecting computer worms in Computer Networks by making use of detection based on the network behavior through the collection of various parameters such as: network latency, throughput, bandwidth, response time, network utilization, packet loss and reliability. Infected hosts were tracked using an algorithm developed. Documentation of network measurements (behavior) metrics for the purpose of detecting unknown worm infection using instance-based technique was achieved by taking note of the changes in the network parameters and their values were logged in the database, as worm propagated through the network. a model for calculating network performance characteristic was developed. Network Worm Simulator (NWS) was used to perform the scanning activities of worms on the network. Jpcap was used to captured network packet. In the database model, the Packets table store network packet captured, the time of capture and every packets stored is given a unique number as id. The network_metric table store the network parameter values for the packets identified by Packet_id related to the Packets table, each set of network parameter value is identified by a unique number called ID. The simulation of the model was implemented using Java programming language.
References
- Addison, W. ,Lance S. 2003. Honeypots: Tracking Hackers.
- Alese and Falaki, 2005 Malicious Codes and Computer Network Security. Journal of Research in Physical Sciences, Volume 1, Number 1, 2005.
- Berk ,V. H. , Gray, R. S. , and Bakos G. 2003. Using sensor networks and data fusion for early detection of active worms. In Proceedings of the SPIE AeroSense,2003.
- Chen, Z. , Gao, L. , and Kwiat K 2003. Modeling the spread of active worms". In Proceedings of The IEEE INFOCOM 2003, March 2003.
- Ibidunmoye E. O 2012 Design of a Stochastic Game Model of the Attacker-Deffender Interraction in a Network Environment, M. Tech Thesis,Federal University of Technology, Akure, Nigeria.
- Jangwon, C. , Jaewook,L. , Jahwan,K. ,Byungyeon,P. , Wonhyuk L. , and Seongjin A. 2005. Efficient Method for Detecting Worm Virus based Bloomlike Connection Behavior, GESTS Int'l Trans. Computer Science and Engr. , Vol. 18, No. 1
- John, L. , Richard, L. , Henry, O. , Didier,C. , and
- Brian C. 2003. The use of honeynets to detect exploited systems across large enterprise networks". In Proceedings of the 2003 IEEE Workshop on Information Assurance.
- Moore,D. 2002. Network telescopes: Observing small or distant security events.
- Parbati,K. M 2008 Detection, Propagation
- Modeling and Designing of Advanced Internet Worms", a dissertation presented to the graduate school of the university of Florida in partial fulfillment of the requirements for the degree of doctor of philosophy university of Florida
- Suresh. R and Sieteng. S 2007 Telecommunication Systems and Technologies-volume II Telecommunication Network Reliability, 2007
- Wu J, Vangala S, Gao L, and Kwiat K 2004. An efficient architecture and algorithm for detecting worms with various scan techniques. In Proceedings of the 11th Annual Network and Distributed System
- Security Symposium (NDSS'04), February 2004.
- Zou,C. C, Towsley D,,Gong W, and Cai S 2003. Routing worm: A fast, selective attack worm based on ip address information. Technical Report TR-03-CSE-06, Umass ECE Dept. , November 200
Index Terms
Keywords