Host-based anomaly detection for pervasive medical systems

2010 5th International Conference on Risks and Security of Internet and Systems, CRiSIS 2010

Volumn , Issue , 2010, Pages

  Asfaw, Biniyam ^a   Bekele, Dawit ^b   Eshete, B ^c   Villafiorita, A ^c   Weldemariam, K ^c  

^a ADDIS ABABA UNIVERSITY   (Ethiopia)

^b African Regional Bureau   (South Africa)

^c ITC IRST   (Italy)

Author keywords

Data Mining; Intrusion Detection Systems; Pervasive Medical Systems; Security

Indexed keywords

ANOMALOUS ACTIVITY; ANOMALY DETECTION; COMPUTING INFRASTRUCTURES; DATA MINING TECHNIQUES; DEGREE OF MOBILITY; DETECTION APPROACH; EXPERIMENTAL SETUP; HEALTHCARE DOMAINS; HEALTHCARE SERVICES; HOST-BASED; INTRUSION DETECTION SYSTEMS; MEDICAL RECORD; MEDICAL SYSTEMS; NORMAL USAGE PROFILE; PERVASIVE DEVICES; PERVASIVE HEALTHCARE; PERVASIVE HEALTHCARE APPLICATION; PERVASIVE MEDICAL SYSTEMS; SAMPLE DATA; SECURITY; SECURITY PROBLEMS; WIRELESS COMMUNICATIONS;

CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; DATA MINING; HEALTH CARE; INTERNET; TEACHING; TELECOMMUNICATION NETWORKS; WIRELESS TELECOMMUNICATION SYSTEMS;

INTRUSION DETECTION;

EID: 79957620499     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRISIS.2010.5764923     Document Type: Conference Paper

References (32)

1. E. Bardram, "The Trouble with Login: On Usability and Computer Security in Ubiquitous Computing," Personal Ubiquitous Comput., vol. 9, no. 6, pp. 357-367, 2005.
2. Jürgen Bohn, Felix Gärtner and Harald Vogt, "Dependability Issues of Pervasive Computing in a Healthcare Environment," in Security in Pervasive Computing. Springer Berlin / Heidelberg, 2004.
3. K. Venkatasubramanian and S. K. Gupta, In Security in Distributed, Grid, Mobile, and Pervasive Computing. Auerbach Publications, CRC Press, 2007, ch. Security Solutions for Pervasive Healthcare, pp. 443-464.
4. D. E. Denning, "Intrusion-Detection Model," IEEE Trans. Software Eng., vol. 13, no. 2, pp. 222-232, 1987.
5. F. Stajano, "Security for Ubiquitous Computing," in ICISC, ser. Lecture Notes in Computer Science, C. Park and S. Chee, Eds., vol. 3506. Springer, 2004, p. 2.
6. A. Abimbola, Q. Shi, and M. Merabti, "NetHost-Sensor: A Novel Concept in Intrusion Detection Systems," in ISCC '03. Washington, DC, USA: IEEE Computer Society, 2003, p. 232.
7. D. E. Denning, "An Intrusion-Detection Model," IEEE Trans. Softw. Eng., vol. 13, no. 2, pp. 222-232, 1987.
8. M. Roesch, "Snort - Lightweight Intrusion Detection for Networks," in LISA. Berkeley, CA, USA: USENIX Association, 1999, pp. 229-238.
9. G. Vigna, F. Valeur, and R. A. Kemmerer, "Designing and Implementing a Family of Intrusion Detection Systems," in ESEC/FSE-11. New York, NY, USA: ACM, 2003, pp. 88-97.
10. J. E. Bardram, "Applications of Context-Aware Computing in Hospital Work - Examples and Design Principles," in Proceedings of the 2004 ACM Symposium on Applied Computing. ACM Press, 2004, pp. 1574-1579.
11. M. Baldauf, S. Dustdar, and F. Rosenberg, "A Survey on Context-Aaware Systems," Int. J. Ad Hoc Ubiquitous Comput., vol. 2, no. 4, pp. 263-277, 2007.
12. V. Stanford, "Using Pervasive Computing to Deliver Elder Care," IEEE Pervasive Computing, vol. 1, no. 1, pp. 10-13, 2002.
13. J. H. Jahnke, "Towards Context-Aware Computing in Clinical Care," OOPSLA Workshop on Building Software for Pervasive Computing, 2005.
14. U. Varshney, "Pervasive Healthcare," IEEE Computer, vol. 36, no. 12, pp. 138-140, 2003.
15. R. Kemmerer and G. Vigna, "Intrusion detection: A Brief History and Overview," IEEE Security and Privacy Magazine, 2002.
16. Ke Wang and Salvatore J. Stolfo, "Anomalous payload-based network intrusion detection," in RIAD, ser. Lecture Notes in Computer Science. Springer, 2004, pp. 203-222.
17. Salvatore J. Stolfo and Frank Apap and Eleazar Eskin and Katherine Heller and Shlomo Hershkop and Andrew Honig and Krysta Marie Svore, "A comparative evaluation of two algorithms for Windows Registry Anomaly Detection," Journal of Computer Security, vol. 13, no. 4, pp. 659-693, 2005.
18. U. Lindqvist and P. A. Porras, "Detecting Computer and Network Misuse Through the Production-Based Expert System Toolset (P-BEST)," in S & P. IEEE Computer Society, 1999.
19. Wojciech Tylman, "Misuse-Based Intrusion Detection Using Bayesian Networks," International Conference on Dependability of Computer Systems,, pp. 203-210, 2008.
20. G. Vigna and C. Kruegel, Handbook of Information Security. Wiley, 2005, ch. Host-based Intrusion Detection Systems.
21. A. A. M. Mehdi, S. Zair and M. Bensebti, "A bayesian networks in intrusion detection systems," Journal of Computer Science, vol. 3, no. 5, 2007.
22. W.-S. Yang, , and S.-Y. Hwang, "A process-mining Framework for the Detection of Healthcare Fraud and Abuse," Expert Systems with Applications, vol. 31, pp. 56-68, 2006.
23. Bezerra, Fábio and Wainer, Jacques, "Anomaly detection algorithms in logs of process aware systems," in SAC '08: Proceedings of the 2008 ACM symposium on Applied computing. ACM, 2008, pp. 951-952.
24. G. A. R. Pedro A. Ortega, Cristian J. Figueroa, "A Medical Claim Fraud/Abuse Detection System based on Data Mining: A Case Study in Chile," in DMIN'06. The 2006 World Congress in Computer Science, Computer Engineering, and Applied Computing, 2006, pp. 224-231.
25. Hipp, Jochen and Güntzer, Ulrich and Nakhaeizadeh, Gholamreza, "Algorithms for association rule mining - a general survey and comparison," SIGKDD Explor. News, vol. 2, no. 1, pp. 58-64, 2000.
26. P. L. Frans Coenen and S. Ahmed, "Data Structure for Association Rule Mining: T-Trees and P-Trees," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, 2004.
27. K. S. Mafruz Zaman Ashrafi, David Taniar, "Odam: An optimized distributed association rule mining algorithm," IEEE Distributed Systems Online, vol. 5, no. 3, March 2004.
28. W. Lee and S. J. Stolfo, "A framework for constructing features and models for intrusion detection systems," ACM Trans. Inf. Syst. Secur., vol. 3, no. 4, pp. 227-261, 2000.
29. R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data. New York, NY, USA: ACM, 1993, pp. 207-216.
30. K. Hu, Y. Lu, L. Zhou, and C. Shi, "Integrating classification and association rule mining: A concept lattice framework," in RSFDGrC'99. London, UK: Springer-Verlag, 1999, pp. 443-447.
31. S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, "A Sense of Self for Unix Processes," in SP '96. Washington, DC, USA: IEEE Computer Society, 1996, p. 120.
32. B. D. Caulkins, J. Lee, and M. Wang, "A Dynamic Data Mining Technique for Intrusion Detection Systems," in ACMSE 43. New York, NY, USA: ACM, 2005, pp. 148-153.