CIMDS: Adapting postprocessing techniques of associative classification for malware detection

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 40, Issue 3, 2010, Pages 298-307

  Ye, Yanfang ^a   Li, Tao ^b   Jiang, Qingshan ^a   Wang, Youyu ^a  

^a XIAMEN UNIVERSITY   (China)

^b Florida International University   (United States)

Author keywords

Associative classification; Malware detection; Postprocessing; Rule pruning; Rule ranking; Rule selection

Indexed keywords

ANTI VIRUS; ANTI-VIRUS LABORATORIES; ANTIVIRUS SOFTWARES; ASSOCIATIVE CLASSIFICATION; BACKDOORS; COMPUTER USERS; DECISION TREE TECHNIQUES; DETECTION SYSTEM; ERROR ESTIMATIONS; MALICIOUS EXECUTABLES; MALWARE DETECTION; MALWARES; NAIVE BAYES; NEW SYSTEM; POST-PROCESSING TECHNIQUES; RANKING RULES; RULE SELECTION; SECURITY THREATS; SPY-WARE; TROJANS;

APPLICATION PROGRAMMING INTERFACES (API); COMPUTER SOFTWARE SELECTION AND EVALUATION; COMPUTER VIRUSES; DECISION TREES; SECURITY OF DATA; VIRUSES;

COMPUTER WORMS;

EID: 77951206726     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2009.2037978     Document Type: Article

References (39)

1. M. Antonie and O. Zaiane, "An associative classifier based on positive and negative rules," in Proc. 9th ACM SIGMOD Workshop Res. Issues Data Mining Knowl. Discovery, 2004, pp. 64-69.
2. M. Antonie, O. Zaiane, and A. Coman, "Associative classifiers for medical images," in Proc. Mining Multimedia Complex Data (LNCS), 2003, pp. 68-83.
3. B. Arunasalam and S. Chawla, "CCCS: A top-down associative classifier for imbalanced class distribution," in Proc. KDD-2006, 2010, pp. 517- 522.
4. E.Baralis, S.Chiusano, and P.Graza, "On support thresholds in associative classification," in Proc. ACM Symp. Appl. Comput. 2004, pp. 553-558.
5. E. Baralis and P. Torino, "A lazy approach to pruning classification rules," in Proc. IEEE Int. Conf. Data Mining 2002, pp. 35-42.
6. R.Bayardo, R. Agrawal, andD.Gunopulos, "Constraint-based rule mining in large, dense databases," in Proc. ICDE-1999, pp. 188-197.
7. H. Cheng, X. Yan, J. Han, and P. S. Yu, "Direct discriminative pattern mining for effective classification," in Proc. ICDE-2008, pp. 169-178.
8. H. Cheng, X. Yan, J. Han, and C. Hsu, "Discriminative frequent pattern analysis for effective classification," in Proc. ICDE-2007, pp. 716-725.
9. M. Christodorescu, S. Jha, and C Kruegel, "Mining specifications of malicious behavior," in Proc. ESEC/FSE-2007, pp. 5-14.
10. F. Coenen and P. Leng, "An evaluation of approaches to classification rule selection," in Proc. 4th IEEE Int. Conf. Data Mining 2004, pp. 359-362.
11. H. Toivonen, M. Klemetinen, P. Ronkainen, K. Hatonen, and H. Mannila, "Pruning and grouping discovered association rules," in Proc. MlnetWorkshop Statist.,Mach. Learning, and DiscoveryDatabases, 1995, pp. 47-52.
12. X. Jiang and X. Zhu, "vEye: Behavioral footprinting for self-propagating worm detection and profiling," Knowl. Inf. Syst., vol.18, no.2, pp. 231- 262, 2009.
13. J. Kolter and M. Maloof, "Learning to detect malicious executables in the wild," in Proc. KDD-2004, pp. 470-478.
14. Ng. R. T. Lakshmanan and J. L. Han, "Exploratory mining and pruning optimizations of constrained association rules," in Proc. SIGMOD-1998, pp. 13-24.
15. W. Li, J. Han, and J. Pei, "CMAR: Accurate and efficient classification based on multiple class-association rules," in Proc. IEEE Int. Conf. Data Mining 2001, pp. 369-376.
16. B. Liu, W. Hsu, and Y. Ma, "Integrating classification and association rule mining," in Proc. 4th Int. Conf. Knowl. Discovery Data Mining1998, pp. 80-86.
17. B. Liu, W. Hsu, and Y. Ma, "Pruning and summarizing the discovered associations," in Proc. KDD-1999, pp. 125-134.
18. M. Mahta, R. Agrawal, and J. Rissanen, "SLIQ: A fast scalable classifier for data mining," in Proc. EDBT-1996, pp. 18-32.
19. M. Bailey, J. Oberheide, J. Andersen, Z. M. Mao, F. Jahanian, and J. Nazario, "Automated classification and analysis of internet malware," in Proc. RAID 2007, LNCS, vol.4637, pp. 178-197.
20. J. R. Quinlan, C4.5. Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
21. M. Schultz, E. Eskin, and E. Zadok, "Data mining methods for detection of new malicious executables," in Proc. Security Privacy, 2001. Proc. 2001 IEEE Symp., May 14-16, pp. 38-49.
22. W. Snedecor and W. Cochran, Statistical Methods, 8th ed. Iowa City, IA: Iowa State Univ. Press, 1989.
23. R. Srikant, Q. Vu, and R. Agrawal, "Mining association rules with item constraints," in Proc. KDD-1997, pp. 67-73.
24. A. Sung, J. Xu, P. Chavez, and S. Mukkamala, "Static analyzer of vicious executables (save)," in Proc. 20th Annu. Comput. Security Appl. Conf., 2004, pp. 326-334.
25. F. Thabtah, P. Cowling, and Y. Peng, "MCAR: Multi-class classification based on association rule approach," in Proc. 3rd IEEE Int. Conf. Comput. Syst. Appl., 2005, pp. 1-7.
26. F. Thabtah, "A review of associative classification mining," Knowl. Eng. Rev., vol.22, no.1, pp. 37-65, 2007.
27. F. Thabtah, P. Cowling, and Y. peng, "MMAC: A new multi-label, multiclass associative classification approach," in Proc. 4th IEEE Int. Conf. Data Mining 2004, pp. 217-224.
28. R. Topor and H. Shen, "Construct robust rule sets for classification," in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2001, pp. 564-569.
29. U. Bayer, A. Moser, C. Kruegel, and E. Kirda, "Dynamic analysis of malicious code," J. Comput. Virol., vol.2, pp. 67-77, May 2006.
30. J. Wang, P. Deng, Y. Fan, L. Jaw, and Y. Liu, "Virus detection using data mining techniques," in Proc. IEEE Int. Conf. Data Mining, 2003, pp. 71-76.
31. J. Wang and G. Karypis, "Harmony: Efficiently mining the best rules for classification," in Proc. SDM-2005, pp. 205-1106
32. K. Wang, S. Zhou, and Y. He, "Growing decision tree on support-less association rules," in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2000, pp. 265-269.
33. Y. J. Wang, Q. Xin, and F. Coenen, "A novel rule ordering approach in classification association rule mining," in Proc. 7th IEEE Int. Conf. Data Mining Workshops 2007, pp. 339-348.
34. I. H.Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2005.
35. Y. Ye, D. Wang, T. Li, and D. Ye, "IMDS: Intelligent malware detection system," in Proc. ACM Int. Conf. Knowl. Discovery Data Mining 2007, pp. 1043-1047.
36. Y. Ye, D. Wang, T. Li, D. Ye, and Q. Jiang, "An intelligent pe-malware detection system based on association mining," J. Comput. Virol., vol.4, pp. 323-334, Jan. 2008.
37. X. Yin and J. Han, "CPAR: Classification based on predictive association rules," in Proc. 3rd SIAM Int. Conf. Data Mining 2003, pp. 331-335.
38. X. Zou, Q. Wang, M. Xiao, and Q. Cai, "Research on post-processing of data mining for electric power dispatching," Mini-Micro Syst., vol.24, no.6, 2003.
39. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, "LIBLINEAR: A library for large linear classification," J. Mach. Learning Res., vol.9, pp. 1775-1778, 2008.