دوره 14، شماره 3 - ( 9-1396 )                   جلد 14 شماره 3 صفحات 83-96 | برگشت به فهرست نسخه ها


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کارشناسی ارشد دانشگاه الزهرا
چکیده:   (801 مشاهده)
 در پژوهش پیش رو با تمرکز روی شناسایی پیمایش‌های ناهنجار وب، سعی شده است تا از طریق مقایسه پروفایل‌های کاربرد وب با نشست فعلی کاربر رفتارهای بدخواهانه، مورد شناسایی قرار گیرند. در رویکرد پیشنهادی، ابتدا پروفایل های کاربرد وب از لاگ دسترسی وب سرور استخراج می‌شود؛ سپس با محاسبه شباهت هر نشست ورودی کاربر به پروفایل‌های اصلی و استخراج هشدارهای کنترل دسترسی متناظر با همان نشست یک شبکه عصبی فازی جهت تشخیص هنجار یا ناهنجار‌بودن پیمایش کاربر مورد استفاده قرار می‌گیرد. به دلیل فقدان داده استانداردی که هم شامل پیمایش‌های وب صفحات و هم شامل هشدارهای کنترل دسترسی متناظر با آن باشد، رویکردی نیز به منظور شبیه‌سازی پیمایش‌های یک کاربر عادی ارائه شد. ارزیابی‌های صورت گرفته نشان می‌دهد که روش ارائه‌شده در تشخیص پیمایش‌های ناهنجار توانمند عمل می‌کند.
 
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نوع مطالعه: پژوهشي | موضوع مقاله: مقالات گروه امنیت اطلاعات
دریافت: ۱۳۹۴/۶/۲۰ | پذیرش: ۱۳۹۶/۶/۵ | انتشار: ۱۳۹۶/۱۱/۹ | انتشار الکترونیک: ۱۳۹۶/۱۱/۹

فهرست منابع
1. [1] J. Daniel E. Geer. The shrinking perimeter: Makingthe case for data-level risk case management. Veradsys White Paper, January 2004.
2. [2] C.Squicciarini, Elisa Bertino. Lorenzo D.Martino.Fedrica Paci.Anna. Security for Web Services and Service-Oriented Architectures. Springer, 2010. [PMID] [PMCID]
3. [3] R. Azmi, B. Pishgoo, H. Nemati, "Hypervisor-based Intrusion Detection Using Artificial Immune Systems", 8th International Iranian ISC Conference on Information Security and Cryptology, pp. 147-153, (2011).
4. [4] S. S. Anand and B. Mobasher, "Intelligent Techniques for Web Personalization", LNAI 3169, Springer-Verlag, 2005, 1–37.
5. [5] B. Mobasher, "Web Usage Mining and Personalization", Practical Handbook of Internet Computing, Chapman Hall and CRC Press, 2004. [DOI:10.1201/9780203507223.ch15]
6. [6] Selma Elsheikh.2008. Web Usage Data for Web Access Control (WUDWAC). World Congress on Engineering, Jul 2008. [PMCID]
7. [7] Priyanka V. Patil, Dharmaraj Patil , 2013,Preprocessing Web Logs for Web Intrusion Detection, IJAIS Proceedings on International Conference and workshop on Advanced Comput-ing 2013.
8. [8] Grant panel, Helen Ashman.2010, Anomaly Detection Over User Profiles for intrusion detection, Originally published in the Proceedings of the 8th Australian Information Security Mangement Conference, Edith Cowan University, Perth Western Australia.
9. [9] Yi Xie, Shensheng Tang. 2012,online anomaly detection based on web usage minig, IEEE 26th international parallel abd Distributed Processing Symposiom.
10. [10] Hamid Bagheri,Fereidoon Shams, 2011, "An Auto-Delegation Mechanism for Role Based Access Control model" 2nd World Conference on Information Technology", Antalya.
11. [11] Suganyadevi Janani Manimozhi Mirdula, 2002, "Preprocessing in Web Usage Mining" .
12. [12] R. Kosala and H. Blockeel, "Web mining research: a survey," ACM SIGKDD Explorations Newsletter, vol. 2, no. 1, pp. 1–15, Jun. 2000. [DOI:10.1145/360402.360406]
13. [13] P. R. Kumar and A. K. Singh, "Web Structure Mining: Exploring Hyperlinks and Algorithms for Information Retrieval," American Journal of applied sciences, vol. 7, no. 6, pp. 840–845, 2010. [DOI:10.3844/ajassp.2010.840.845]
14. [14] J. Sivaramakrishnan and V. Balakrishnan, "Web Mining Functions in an Academic Search Application," Informatica, vol. 13.
15. [15] J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, "Web usage mining: discovery and applications of usage patterns from Web data," ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp. 12–23, Jan. 2000. [DOI:10.1145/846183.846188]
16. [16] L. K. Grace, V. Maheswari, and D. Nagamalai, "Analysis of Web Logs and Web User in Web Mining," International Journal of Network Security & Its Applications, Jan. 2011.
17. [17] D. Dixit and M. Kiruthika, "Preprocessing of Web Logs," International Journal on Computer Science and Engineering, vol. 2, pp. 2447-2452, 2010.
18. [18] V. Sathiya Moorthi and V. Murali Bhaskaran, "Data preparation Techniques for Web Usage Mining in World Wide Web–an approach," International Journal of Recent Trends in Engineering, vol. 2, no. 4, 2009.
19. [19] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, "Effective personalization based on association rule discovery from web usage data," in Proceedings of the 3rd international workshop on Web information and data management, Atlanta, Georgia, USA, 2001, pp. 9–15. [DOI:10.1145/502932.502935]
20. [20] H.Malek,M.M.Ebadzadeh,M.Rahmati, Threen-ew fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm,ApplIntell.35(2011)1–
21. [21] S.L. Chiu,Fuzzy model identification based on cluster estimation,J.Intell. Fuzzy S-yst.2(1994)209–219.
22. [22] R.R.Yager,D.P.Filev,Learning of fuzzy rules by mountain clustering,in: Proceeding ofSPIEConferenceonAppliedFuzzyLogicTechnology,1993, pp. 246–254.
23. [23] A. Salimi,M.M.Ebadzadeh, CFNN: Correlated fuzzy neural network, Neurocomput-ing148(2015)430–444. [DOI:10.1016/j.neucom.2014.07.021]
24. [24] G. Leng,Th.McGinnity,Design for self-organiz-ing fuzzy neural network based on geneticalgor-ithm,IEEETrans.FuzzySyst.14(2006)755–766. [DOI:10.1109/TFUZZ.2006.877361]
25. [25] B.Pizzileo,K.Li,G.W.Irwin,W.Zhao, Improved structure optimization for fuzzy-neuralnetwork-s,IEEETrans.FuzzySyst.20(2012)1076–1089. [DOI:10.1109/TFUZZ.2012.2193587]
26. [26] T. W. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal, "From user access patterns to dynamic hypertext linking," Computer Networks and
27. [27] R. Forsati, M. R. Meybodi, and A. Rahbar, "An efficient algorithm for web recommendation systems," presented at the IEEE/ACS Interna-tional Conference on Computer Systems and Application-s, AICCSA 2009, 2009, pp. 579-586. [DOI:10.1109/AICCSA.2009.5069385]
28. [28] N. C. Jones and P. Pevzner, An introduction to bioinformatics algorithms. The MIT Press, 2004.
29. [29] W. Wang and O. R. Zaïane, "Clustering Web Sessions by Sequence Alignment," in Proceedings of 13th International Workshop on Database and Expert Systems Applications, Los Alamitos, CA, USA, 2002, vol. 0, p. 394. [DOI:10.1109/DEXA.2002.1045928]
30. [30] C. Li and Y. Lu, "Similarity Measurement of Web Sessions by Sequence Alignment," presented at the IFIP International Conference on Network and Parallel Computing Workshops, NPC Workshops, 2007, pp. 716-720. https://doi.org/10.1007/s11859-007-0048-2 [DOI:10.1109/NPC.2007.66]
31. [31] B. Hay, G. Wets, and K. Vanhoof, "Segmentation of visiting patterns on web sites using a sequence alignment method," Journal of Retailing and Consumer Services, vol. 10, no. 3, pp. 145-153, May 2003. [DOI:10.1016/S0969-6989(03)00006-7]
32. [32] R.Azmi,M.Azimpour-kivi, "Applying Sequence Alignment in Tracking Evolving Clusters of Web-Sessions Data:an Artificial Immune Network Approach", 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks.
33. [33] B. Hay, G. Wets, and K. Vanhoof, "Segmenta-tion of visiting patterns on web sites using a sequence alignment method," Journal of Retailing and Consumer Services, vol. 10, no. 3, pp. 145-153, May 2003. [DOI:10.1016/S0969-6989(03)00006-7]
34. [34] B. H. Helmi and A. T. Rahmani, "An AIS algorithm for Web usage mining with directed mutation," in IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence), 2008, pp. 3122-3127. [DOI:10.1109/CEC.2008.4631220]
35. [35] T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: an efficient data clustering method for very large databases," ACM SIGMOD Record, vol. 25, no. 2, pp. 103–114, Jun. 1996. [DOI:10.1145/235968.233324]
36. [36] O. Nasraoui, C. C. Uribe, C. R. Coronel, and F. Gonzalez, "TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model," presented at the Third IEEE International Conference on Data Mining, ICDM, 2003, pp. 235- 242. [DOI:10.1109/ICDM.2003.1250925]
37. [37] S.Alam,G.Dobbie,P.Riddle,"Particle Swarm Optimization basedClustering Of Web Usage Data",2008 IEEE/WIC/ACM International Conference on web Intelligent and Intelligent Agent Technology.
38. [38] R.Azmi,M.Raji,V.Derhami," Web Anomaly Detection Using Arti_cial Immune System and Web Usage Mining Approach "2012, ICIC,Zanjan
39. [39] C. Kruegel and G. Vigna, Anomaly detection of web-based attacks, in Proceedings of the 10th ACM Conference on Com-puter and Communications Security (2003), 251-261 [DOI:10.1145/948109.948144]
40. [40] L. Guangminl, Modeling Unknown Web Attacks in Network Anomaly Detection, International Conference on Conver-gence and Hybrid Information Technology (2008).
41. [41] M. Danforth, Towards a Classifying Arti_cial Immune Sys-tem for Web Server Attacks: Department of Computer andElectrical Engineering and Computer Science, Interna-tional Conference on Machine Learning and Applications (2009).
42. [42] M. A. Rassam, M. A. Maarof, and A. Zainal, Intrusion De-tection System Using Unsupervis-ed Immune Network Cluster-ing with Reduced Features, Int. J. Advance. Soft Comput. Appl. 2/2010 (2010).
43. [43] Valeur, F., Mutz, D., Vigna, G.: A learning-based approach to the detection of SQL attacks. In: Julisch, K., Krügel, C. (eds.) DIMVA 2005. LNCS, vol. 3548, pp. 123–140. Springer, Heidelberg (2005). [DOI:10.1007/11506881_8]
44. [44] Kantardzic, M.: Data Mining Concepts, Models, Methods and Algorithm. IEEE Press, New York (2002). [PMID]
45. [45] L. Jie, S. Jianwei, H.Changzhen," A Novel Framework for Active Detection of HTTP Based Attack", Communication Systems and Information Technology,.Springer-Verlag Berlin Heidelberg 2011. [DOI:10.1007/978-3-642-21762-3_53]
46. [46] R. Anderson. Security Engineering: A Guide to Building Dependable Distributed Systems. WileyComputer Publishing, New York, New York, 2001.
47. [47] جعفریان مقدم. احمد رضا، برزین‌پور. فرناز، فتحیان. محمد، "روش نوین خوشه‌بندی ترکیبی با استفاده از سامانه ایمنی مصنوعی و سلسله مراتبی"، فصلنامه علمی پژوهشی پردازش علائم و داده‌ها دوره 13 شماره 4 (12-1395).
48. [47] A.Jafarian-Moghaddam,F. Barzinpour,M. Fath-ian, new clustering Technique us-ing Artificial Immune System and Hierarchical techni-que,Quarty journal Signal and Data Processing, Volume 13, Issue 4 (3-2017).
49. [48] B. W. Lampson. Protection. ACM SIGOPS Operating System Review, 8(1):18–24, January 1974. [DOI:10.1145/775265.775268]
50. [49] Wu, S. X., Banzhaf, W.,"The use of computat-ional intelligence in intrusion detectionsystems: A review", Applied Soft Computing, vol. 10, pp. 1–35, (2010). https://doi.org/10.1103/PhysRevA.82.014303 https://doi.org/10.1103/PhysRevA.81.061805 https://doi.org/10.1103/PhysRevA.81.042301 https://doi.org/10.1103/PhysRevA.82.032307 https://doi.org/10.1103/PhysRevA.81.033625 https://doi.org/10.1103/PhysRevA.82.052339 https://doi.org/10.1103/PhysRevA.82.053834 https://doi.org/10.1103/PhysRevA.81.044305 https://doi.org/10.1103/PhysRevA.81.053401 https://doi.org/10.1103/PhysRevA.82.043431 https://doi.org/10.1103/PhysRevA.82.034307 https://doi.org/10.1103/PhysRevA.82.053416 https://doi.org/10.1103/PhysRevA.82.052111 https://doi.org/10.1103/PhysRevA.82.013411 [DOI:10.1103/PhysRevA.82.013807] [PMID]