تعداد نشریات | 36 |
تعداد شمارهها | 1,231 |
تعداد مقالات | 8,931 |
تعداد مشاهده مقاله | 7,700,987 |
تعداد دریافت فایل اصل مقاله | 4,593,413 |
شناسایی وب سایت فیشینگ در بانکداری اینترنتی با استفاده از الگوریتم بهینه سازی صفحات شیبدار | ||
پدافند الکترونیکی و سایبری | ||
مقاله 3، دوره 3، شماره 1، اردیبهشت 1394، صفحه 29-39 اصل مقاله (790.58 K) | ||
نویسندگان | ||
نفیسه لنگری* 1؛ مجید عبدالرزاق نژاد2 | ||
1کارشناسی ارشد، دانشکده فنی و مهندسی، دانشگاه بیرجند، بیرجند، ایران | ||
2استادیار، دانشکده فنی و مهندسی، دانشگاه بزرگمهر قائنات، قائنات، ایران | ||
تاریخ دریافت: 30 مهر 1393، تاریخ بازنگری: 31 خرداد 1402، تاریخ پذیرش: 28 شهریور 1397 | ||
چکیده | ||
یکی از عوامل بسیار تأثیر گذار در توسعه تجارت الکترونیک و تجارت تحت وب، امنیت آن میباشد. اما متناسب با توسعه تجارت الکترونیک، مقوله فیشینگ و سرقت اطلاعات بانکی افراد به تهدید بسیار جدی در این حوزه بدل شده است. روشهای متنوعی در شناسایی وب سایت فیشینگ مورد بررسی و تحلیل قرار گرفتهاند. در اکثر روشها توجهی به طول عمر کوتاه وب سایت فیشینگ و تلاش برای کاهش حجم محاسباتی صورت نگرفته است. از این جهت، در این پژوهش سعی شده تا ویژگیهای پراهمیت را جهت ارزیابی وب سایت فیشینگ استخراج کرده و سپس با استفاده از الگوریتم بهینه سازی صفحات شیبدار فرآیند طبقه بندی انجام گیرد. مقایسه نتایج حاصله از این رویکرد جدید با بهترین روشهای موجود، اثبات کننده توانایی این رویکرد در شناسایی وب سایت-های فیشینگ میباشد. | ||
کلیدواژهها | ||
تشخیص وب سایت فیشینگ؛ بانکداری اینترنتی؛ الگوریتم بهینه سازی صفحات شیبدار؛ استخراج ویژگی؛ طبقهبندی | ||
عنوان مقاله [English] | ||
Phishing Website Detection for e-Banking by Inclined Planes Optimization Algorithm | ||
نویسندگان [English] | ||
Nafiseh Langhari1؛ Majid Abdolrazzagh Nejad2 | ||
1Master's degree, Technical and Engineering Faculty, Birjand University, Birjand, Iran | ||
2Assistant Professor, Technical and Engineering Faculty, Bozormehr Qaenat University, Qaenat, Iran | ||
چکیده [English] | ||
One of the most important factors influencing the development of e-commerce and web-based commerce is security. However development of e-commerce leads to phishing and steal the customer information. So the various methods have been designed to detect phishing websites in the literature. Lacks of attention to the short lifetime of phishing website, and to reduce the amount of computation are the main gaps of these methods. In this paper, a new intelligent approach is proposed to detect phishing websites, in e-banking by extracting sensitive features of websites on phishing attacks and classifying candidate websites in three classes such as phishing, legitimate and suspicious websites based on inclined planes optimization algorithm. The comparison results of the new intelligent approach with the best available techniques, demonstrate the ability of this approach to detect phishing websites. | ||
کلیدواژهها [English] | ||
Network Security, Service Security, Port Security, Authentication, Port-Knocking | ||
مراجع | ||
[1] Y. Zhang, S. Egelman, L. Cranor, and J. Hong, “Phinding phish: Evaluating anti-phishing tools,” 2006. [2] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, “Intelligent phishing detection system for e-banking using fuzzy data mining,” Expert systems with applications, vol. 37, pp. 7913-7921, 2010. [3] M. D. I. A. Ajlouni, W. E. Hadi, and J. Alwedyan, “Detecting Phishing Websites Using Associative Classification,” European Journal of Business and Management, vol. 5, pp. 36-40, 2013. [4] L. F. Cranor, S. Egelman, J. I. Hong, and Y. Zhang, “Phinding Phish: An Evaluation of Anti-Phishing Toolbars,” in NDSS, 2007. [5] M. Sirajuddin, “Data Mining Approach for Deceptive Phishing Detection System,” ijsret, vol. 2, pp. 337-334, 2013. [6] E. Medvet, E. Kirda, and C. Kruegel, “Visual-similarity-based phishing detection,” in Proceedings of the 4th international conference on Security and privacy in communication netowrks, p. 22, 2008. [7] W. Zhang, H. Lu, B. Xu, and H. Yang, “Web phishing detection based on page spatial layout similarity,” Informatica, vol. 37, pp. 231-244, 2013. [8] L. Wenyin, G. Huang, L. Xiaoyue, Z. Min, and X. Deng, “Detection of phishing webpages based on visual similarity,” in Special interest tracks and posters of the 14th international conference on world wide web, pp. 1060-1061, 2005. [9] S. T. Kumar, V. Kumar, and A. Kumar, “Detection and Prevention of Phishing Attacks Using Linkguard Algorithm,” 2008. [10] J. S. White, J. N. Matthews, and J. L. Stacy, “A method for the automated detection phishing websites through both site characteristics and image analysis,” in SPIE Defense, Security and Sensing, pp. 84080B-84080B-11, 2012. [11] A. P. Rosiello, E. Kirda, C. Kruegel, and F. Ferrandi, “A layout-similarity-based approach for detecting phishing pages,” in Security and Privacy in Communications Networks and the Workshops, 2007. Secure Comm 2007. Third International Conference on, pp. 454-463, 2007. [12] N. R. T. Guhan, “Analyzing and Detecting Phishing Webpages with Visual Similarity Assessment Based on Earth Movers Distance with Linear Programming Model,” International Journal of Advanced Engineering Technology, vol. III, pp. 327-330, 2012. [13] P. Barraclough, M. Hossain, M. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions,” Expert systems with applications, vol. 40, pp. 4697-4706, 2013. [14] A. Demaris and S. H. Selman, “Logistic regression,” in Converting Data into Evidence, ed: Springer, pp.115-136, 2013. [15] S. Garera, N. Provos, M. Chew, and A. D. Rubin, “A framework for detection and measurement of phishing attacks,” in Proceedings of the 2007 ACM workshop on Recurring malcode, pp. 1-8, 2007. [16] P. Sengar and V. Kumar, “Client-side defense against phishing with pagesafe,” International Journal of Computer Applications, vol. 4, pp. 6-10, 2010. [17] SS. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair, “A comparison of machine learning techniques for phishing detection,” in Proceedings of the anti-phishing working groups 2nd annual ecrime researchers summit, pp. 60-69, 2007. [18] J. M. De-Sa, “Pattern recognition: concepts, methods, and applications,” Springer, 2001. [19] H. M. Deylami and Y. P. Singh, “Cybercrime detection techniques based on support vector machines,” Artificial Intelligence Research, vol. 2, 2013. [20] L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5-32, 2001. [21] D. M. L. V. Radha Damodaram, “Experimental Study on Meta Heuristic Optimization Algorithms for Fake Website Detection,” International Association of Scientific Innovation and Research (IASIR), vol. 2, pp. 43-53, 2012. [22] M. Radha Damodaram and M. Valarmathi, “Phishing Website Detection and Optimization Using Particle Swarm Optimization Technique,” International Journal of Computer Science and Security (IJCSS), vol. 5, p. 477, 2011. [23] M. R. Damodaram and M. Valarmathi, “Bacterial Foraging Optimization for Fake Website Detection,” International Journal of Computer Science & Applications (TIJCSA), vol. 1, 2013. [24] M. H. Mozaffari, H. Abdy, and S. H. Zahiri, “Application of inclined planes system optimization on data clustering,” in Pattern Recognition and Image Analysis (PRIA), 2013 First Iranian Conference on, pp. 1-3, 2013. [25] M. Aburrous, M. Hossain, K. Dahal, and F. Thabtah, “Associative classification techniques for predicting e-banking phishing websites,” in Multimedia Computing and Information Technology (MCIT), 2010 International Conference on, pp. 9-12, 2010. [26] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabatah, “Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining,” in Cyber Worlds, 2009. CW'09. International Conference on, pp. 265-272, 2009. [27] P. Barraclough, M. Hossain, M. Tahir, G. Sexton, and N. Aslam, “Intelligent phishing detection and protection scheme for online transactions,” Expert Systems with Applications, 2013. [28] S. H. Zahiri and S. A. Seyedin, “Intelligent Particle Swarm Classifiers,” Iranian journal of electrical and computer engineering, vol. 4, p. 63, 2015. [29] M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, “Experimental case studies for investigating e-banking phishing techniques and attack strategies,” Cognitive Computation, vol. 2, pp. 242-253, 2010. [30] A. Y. Fu, L. Wenyin, and X. Deng, “Detecting phishing web pages with visual similarity assessment based on earth mover's distance (EMD),” Dependable and Secure Computing, IEEE Transactions on, vol. 3, pp. 301-311, 2006. [31] R. Mohammad, T. McCluskey, and F. A. Thabtah, “Intelligent Rule based Phishing Websites Classification,” IET Information Security, 2013. | ||
آمار تعداد مشاهده مقاله: 706 تعداد دریافت فایل اصل مقاله: 463 |