REFACING: An autonomic approach to network security based on multidimensional trustworthiness

Computer Networks

Volumn 52, Issue 14, 2008, Pages 2745-2763

  Oliviero, F ^a   Peluso, L ^a   Romano, S P ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

Autonomic communication; Dempster; Information fusion; Network security; Shafer theory; Trustworthiness

Indexed keywords

DATA FUSION; FUSION REACTIONS; INFORMATION FUSION; INTERNET; INTRUSION DETECTION; NETWORK SECURITY; NUCLEAR PHYSICS; RELIABILITY; SENSOR NETWORKS;

A-PRIORI; AUTONOMIC COMMUNICATION; AUTONOMIC COMPUTING; BELIEF FUNCTIONS; DEMPSTER; DEMPSTER-SHAFER; DETECTION SENSORS; DISTRIBUTED ANOMALY DETECTION; GAINING MOMENTUM; INFORMATION FUSION ALGORITHMS; INTERNATIONAL RESEARCHES; MATHEMATICAL THEORIES; NETWORK INFORMATION; RESEARCH EFFORTS; SELF-ADAPTIVE; SELF-CONFIGURATION; SELF-MANAGEMENT; SELF-OPTIMIZATION; SHAFER THEORY; TRUSTWORTHINESS;

SENSOR DATA FUSION;

EID: 49949086459     PISSN: 13891286     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.comnet.2008.04.022     Document Type: Article

References (20)

1. Jean Gordon, E. Edward, H. Shortliffe, Rule-Based Expert Systems, Chapter The Dempster-Shafer Theory of Evidence.
2. Shafer G. A Mathematical Theory of Evidence (1976), Princeton University Press
3. Nong Ye, Mingning Xu, Information fusion for intrusion detection, in: Third International Conference on Information Fusion, vol. 2, July 2000, pp. 17-20.
4. F. Cuppens, A. Miege, Alert correlation in a cooperative intrusion detection framework, in: Proceedings of the IEEE Symposium on Security and Privacy, 2002, pp. 202-215.
5. Julia Allen, Alan Christie, William Fithen, John McHugh, Jed Pickel, Ed Stoner, State of the practice of intrusion detection technologies, 1999.
6. James P. Anderson, Computer security threat monitoring and surveillance, Fort Washington, Pennsylvania, April 1980.
7. Daniel J. Burroughs, Linda F. Wilson, George V. Cybenko, Analysis of distributed intrusion detection systems using bayesian methods, in: International Performance Computing and Communication Conference, April 2002.
8. Vincent Berk, Robert Gray, George Bakos, Using sensor networks and data fusion for early detection of active worms, in: SPIE Aerosense Conference, vol. 5071, April 2003, pp. 92-104.
9. Bass T. Intrusion detection systems and multisensor data fusion. Communications of the ACM 43 (2000), ACM Press, NY, USA pp. 99-105, April
10. Ruthven I., and Lalmas M. Using Dempster-Shafer's theory of evidence to combine aspects of information use. J. Int. Inf. Syst. 19 3 (2002) 267-301
11. D. Yu, D. Frincke, Alert confidence fusion in intrusion detection systems with extended Dempster-Shafer theory, in: Proceedings of the 43rd annual southeast regional conference, ACM Press, New York, NY, USA, March 2005, pp. 142-147.
12. H. Wu, M. Siegel, R. Stiefelhagen, J. Yang, Sensor fusion using Dempster-Shafer theory, in: Proceedings of IEEE Instrumentation and Measurement Technology Conference, Anchorage, AK, USA, 2002.
13. Kephart J.O., and Chess D.M. The vision of autonomic computing. Computer 36 1 (2003) 41-50
14. A. Garg, R. Battiti, R. Cascella, Reputation Management: Experiments on the Robustness of ROCQ, Technical Report DIT-05-087.
15. Valeur F., Vigna G., Kruegel C., and Kemmerer R. A comprehensive approach to intrusion detection alert correlation. IEEE Trans Dependable Secure Comput 1 3 (2004) 146-169
16. Zhang G., and Parashar M. Cooperative defence against ddos attacks. J Res Pract Inf Technol 38 1 (2006) 69-84
17. Sonja Buchegger, Jean-Yves, Le Boudec, Performance analysis of the confidant protocol (cooperation of nodes: fairness in dynamic ad-hoc networks), in: Proceedings of IEEE/ACM Symposium on Mobile Ad Hoc Networking and Computing, MobiHOC, June 2002.
18. Pietro Michiardi, Refik Molva, Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad-hoc networks, in: Proceedings of Communications and Multimedia Security Conference, CMS, September 2002.
19. Verbaeten S., and Van Assche A. Ensemble methods for noise elimination in classification problems. Lecture Notes in Computer Science vol. 2709 (2003), Springer-Verlag
20. X. Zeng, T. Martinez. A noise filtering method using neural networks, in: Proceedings of the IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement and Related Applications, May 2003, pp. 26-31.