Volume 6, Issue 1 (Spring 2023)
Iranian Journal of Educational Sociology 2023, 6(1): 222-232 |
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Rezaei F, Afshar Kazemi M A, Keramati M A. (2023). Presenting a Model for Recognizing Phishing Sites and Privacy Violations in the Tourism Industry. Iranian Journal of Educational Sociology. 6(1), 222-232. doi:10.61186/ijes.6.1.222
URL: http://iase-idje.ir/article-1-1261-en.html
URL: http://iase-idje.ir/article-1-1261-en.html
1- PhD Student, Department of Information Technology Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2- Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author)
3- Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2- Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author)
3- Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Abstract: (349 Views)
Purpose: Electronic Tourism is one of the important components of expanding Tourism by synchronizing this industry with information technology. It has not been long since its emergence.
Methodology: this field is a combination of tourism and information technology that is one of the most common types of income-generating businesses which is producing job opportunities in the modern world. The advancement of science alongside communication and information technologies presented many opportunities and threats to this field due to tech such as smartphones and sensors, virtual and augmented reality tools, NFC, RFID, etc.
Findings: The disclosure of the tourists' information and the possible abuse of it is one such threat. Therefore privacy and non-disclosure of information should be important factors. Recognition of reputable sites is an important factor in solving this problem. In this study, we have presented a model for recognizing fake and phishing sites which use the CFS+PSO and a combination of Info+Ranger alongside their results to reduce the test dataset features so that it could present a model for categorizing and higher accuracy in recognizing phishing sites by using the Multilayer Perceptron method. The proposed model was successful in recognizing 95.5% of phishing sites.
Counclusion: The effect of information technology on the tourism industry and the usage of internet websites for selling and providing tourism services to tourists have created new security challenges. Protecting the privacy and personal information of people and tourists is one of these challenges and the disclosure of such information could lead to abuse by unqualified people and dissatisfaction and distrust of such systems.
Methodology: this field is a combination of tourism and information technology that is one of the most common types of income-generating businesses which is producing job opportunities in the modern world. The advancement of science alongside communication and information technologies presented many opportunities and threats to this field due to tech such as smartphones and sensors, virtual and augmented reality tools, NFC, RFID, etc.
Findings: The disclosure of the tourists' information and the possible abuse of it is one such threat. Therefore privacy and non-disclosure of information should be important factors. Recognition of reputable sites is an important factor in solving this problem. In this study, we have presented a model for recognizing fake and phishing sites which use the CFS+PSO and a combination of Info+Ranger alongside their results to reduce the test dataset features so that it could present a model for categorizing and higher accuracy in recognizing phishing sites by using the Multilayer Perceptron method. The proposed model was successful in recognizing 95.5% of phishing sites.
Counclusion: The effect of information technology on the tourism industry and the usage of internet websites for selling and providing tourism services to tourists have created new security challenges. Protecting the privacy and personal information of people and tourists is one of these challenges and the disclosure of such information could lead to abuse by unqualified people and dissatisfaction and distrust of such systems.
Type of Study: Research Article |
Subject:
Special
Received: 2022/11/12 | Accepted: 2023/02/15 | Published: 2023/05/31
Received: 2022/11/12 | Accepted: 2023/02/15 | Published: 2023/05/31
References
1. Alizadeh Bahrami K, Abdullahi Fard. (2016) "J48 Decision Tree in Intelligent Intrusion Detection Systems", National Conference on New Researches in Electrical, Computer and Medical Engineering, Islamic Azad University, Kazeroon Branch. [Persian]
2. Atafar A, Khazaei Pool J, Pour Mostafa Khoshkroudi M. (2012), Factors Affecting the Adoption of Information Technology in Tourism Industry, Tourism Management Studies. 7(18): 131-156. [Persian]
3. Awazu Y, Desouza KC. (2004). The Knowlege Chiefs: CKOs, CLOs and CPOs. European Management Journal. 22(3): 339-344. [DOI:10.1016/j.emj.2004.04.009]
4. Baharloo Y. (2020). "Improving the method of identifying phishing websites using data mining on web pages", Iranian Journal of Information and Communication Technology, Iranian Information and Communication Technology Association. 12(44): 27-38. [Persian]
5. Bamberger KA, Mulligan DK. (2013). Business perceptions and satisfaction with e-government Information Quarterly. 30(1): 1-9. [DOI:10.1016/j.giq.2012.06.009]
6. Buhalis D, Law R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the internet- the state of etourism research. Tourism management. 29(4): 609:623. [DOI:10.1016/j.tourman.2008.01.005]
7. Büyüközkan G, Ergün B. (2011). Intelligent System Applications in Electronic Tourism, Expert Systems with Applications. 38(6): 6586- 6598.
https://doi.org/10.1016/j.eswa.2011.01.103
https://doi.org/10.1016/j.eswa.2010.11.080 [DOI:10.1016/j.eswa.2011.04.171]
8. Chaudhry JA, Rittenhouse RG. (2015). "Phishing: Classification and Countermeasures", 7th International Conferece on Multimedia, Computer Graphics and Broadcasting. 28-31. [DOI:10.1109/MulGraB.2015.17] [PMID]
9. Doshi R, Noah A, Nick F. (2018). "Machine learning ddos detection for onsumer internet of things devices." 2018 IEEE Security and Privacy Workshops (SPW). [DOI:10.1109/SPW.2018.00013]
10. Ismaili M. (2013). Concepts and Techniques of Data Mining, Kashan: Sura. [Persian]
11. Jensen C, Potts C, Jensen C. (2005). Privacy practices of Internet users: Self-reports versus observed behavior. International Journal of Human-Computer Studies. 63(1-2): 203-227. [DOI:10.1016/j.ijhcs.2005.04.019]
12. Jutla DN, Bodonk P, Zhang Y. (2006). PeCAN: An architecture for users privacy-aware electronic commerce contexts on the semantic web. Information Systems. 31(4): 295-320. [DOI:10.1016/j.is.2005.02.004]
13. Khosravi title A, Ganjoo M, Mazarei H. (2017), Presenting a Model for Privacy Concerns in Electronic Banking, Third International Conference, Web Research.
14. Langari N, Abdolrazaqnejad M. (2015). "Identification of Phishing Website in Internet Banking Using Sloping Page Optimization Algorithm", Journal of Electronic and Cyber Defense. 1: 40-29. [Persian]
15. Latif Sh. (2020). "A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network." (8): 89337-89350. [Persian] [DOI:10.1109/ACCESS.2020.2994079]
16. Laufer RS, Wolfe M. (1977). Privacy as a Concept and a Social Issue: Amultidimensional Developmentl Developmental Theory. Journal of Social ISSues, 33(3): 22-42. [DOI:10.1111/j.1540-4560.1977.tb01880.x]
17. Ma'ouni M. (2015). "Detection of attacks in electronic banking using fuzzy-rough combination system (Fuzzy_rough)" Department of Computer, Imam Reza University (AS). [Persian]
18. Manimurugan S. (2020). "Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network." (8): 77396-77404. [DOI:10.1109/ACCESS.2020.2986013]
19. Milne GR, Gordon ME. Direct mail privacy-efficiency trade-offs within an implied social contract framework. Journal of Pulicy and Marketing. 12(2): 206-215. [DOI:10.1177/074391569101200206]
20. Mohammad RM, Thabtah F, McCluskey L. (2015). "Tutorial and critical analysis of phishing websites methods", Computer Science Review. 17: 1-24. [DOI:10.1016/j.cosrev.2015.04.001]
21. Morvati Sharifabadi A, Asadian Ardakani F. (2014), Presenting a health tourism development model with an integrated approach of fuzzy TOPSIS and interpretive structural modeling in Yazd province, Health Management. 17(55): 73-8. [Persian]
22. Pandey M, Ravi V. (2012). "Detecting phishing e-mails using Text and Data mining", IEEE International Conference on Computational Intelligence and Computing Research(ICCIC). 2012. [DOI:10.1109/ICCIC.2012.6510259]
23. Rezaei F, Afshar Kazemi MA, Keramati MA. (2021). Detection of E-commerce Attacks and Anomalies using Adaptive Neuro-Fuzzy Inference System and Firefly Optimization Algorithm . 13(1) :32-39. [Persian] [DOI:10.52547/ijict.13.1.32]
24. Shafi'i S. (2010). Civil Liability for Violation of Privacy, Master Thesis in Private Law, Kashan University. [Persian]
25. Singh P, Jain N, Maini A.(2015). "Investigating the Effect Of Feature Selection and Dimensionality Reduction On Phishing Website Classification Problem", 1st International Conference on Next Generation Computing Technologies (NGCT) Dehradun, India. 388-393. [DOI:10.1109/NGCT.2015.7375147]
26. Syed Naeem F. (2020). "Denial of service attack detection through machine learning for the IoT." Journal of Information and Telecommunication. 1-22.
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