References
- 1.5M Unpatched WordPress Sites Hacked,” February 2017. [Online]. Available: https://threatpost.com/1-5m-unpatched-wordpress-sites-hacked-following-vulnerability-disclosure/123691/ [Accessed 25 December 2019].
- Abu-Nimeh, S., 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, Pittsburgh, Pennsylvania, USA, 2007.
- Aburrous, M., M. A. Hossain, K. Dahal, and F. Thabtah. 2010. “Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining.” Expert Systems with Applications 37 (12): 7913–7921. doi:https://doi.org/10.1016/j.eswa.2010.04.044.
- Aleroud, A., and L. Zhou. 2017. “Phishing Environments, Techniques, and Countermeasures: A Survey.” Computers & Security 68: 160–196. doi:https://doi.org/10.1016/j.cose.2017.04.006.
- Almomani, A., B. B. Gupta, S. Atawneh, A. Meulenberg, and E. Almomani. 2013. “A Survey of Phishing Email Filtering Techniques.” IEEE Communications Surveys & Tutorials 15 (4): 2070–2090. doi:https://doi.org/10.1109/SURV.2013.030713.00020.
- Amrutkar, C., Y. S. Kim, and P. Traynor. 2017. “Detecting Mobile Malicious Webpages in Real Time.” IEEE Transactions on Mobile Computing 16 (8): 2184–2197. doi:https://doi.org/10.1109/TMC.2016.2575828.
- APWG 2H 2014 phishing report,” 2015. [Online]. Available: http://docs.apwg.org/reports/APWG_Global_Phishing_Report_2H_2014.pdf [Accessed 3 April 2020].
- APWG Phishing Report,” 2019. [Online]. Available: https://www.antiphishing.org/trendsreports/ [Accessed 4 April 2020].
- Arachchilage, N. A. G., and S. Love. 2013. “A Game Design Framework for Avoiding Phishing Attacks.” Computers in Human Behavior 29 (3): 706–714. doi:https://doi.org/10.1016/j.chb.2012.12.018.
- Arachchilage, N. A. G., and S. Love. 2014. “Security Awareness of Computer Users: A Phishing Threat Avoidance Perspective.” Computers in Human Behavior 38: 304–312. doi:https://doi.org/10.1016/j.chb.2014.05.046.
- Arachchilagea, N. A. G., S. Love, and K. Beznosov. 2016. “Phishing Threat Avoidance Behaviour: An Empirical Investigation.” Computers in Human Behavior 60: 185–197. doi:https://doi.org/10.1016/j.chb.2016.02.065.
- Baslyman, M., and S. Chiasson, “Smells Phishy?”: An Educational Game about Online Phishing Scams,” in Proceedings of APWG Symposium on Electronic Crime Research (eCrime), Toronto, ON, Canada, 2016.
- Bergholz, A., J. D. Beer, S. Glahn, F. Moens, G. Paaß, and S. Strobel. 2010. “New Filtering Approaches for Phishing Email.” Journal of Computer Security 18 (1): 7–35. doi:https://doi.org/10.3233/JCS-2010-0371.
- Best Practices for Dealing With Phishing and Ransomware,” September 2016. [Online]. Available: https://assets.barracuda.com/assets/docs/dms/Best_Practices_for_Dealing_With_Phishing_and_Ransomware_-_Barracuda.pdf [Accessed 2 April 2020].
- Bursztein, E., B. Benko, D. Margolis, T. Pietraszek, A. Archer, A. Aquino, A. Pitsillidis, and S. Savage, “Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild,” in Proceedings of the 2014 Conference on Internet Measurement Conference, Vancouver, BC, Canada, 2014.
- Chandrasekaran, M., K. Narayanan, and S. Upadhyaya, “Phishing Email Detection Based on Structural Properties,” in Proceedings of New York State Cyber Security Conference, Albany, NY, 2006.
- Chen, C.-M., D. Gua, and Q.-K. Su. 2014. “Feature Set Identification for Detecting Suspicious URLs Using Bayesian Classification in Social Networks.” Information Sciences 289: 133–147. doi:https://doi.org/10.1016/j.ins.2014.07.030.
- Chen, K.-T., J.-Y. Chen, C.-R. Huang, and C.-S. Chen. 2009. “Fighting Phishing with Discriminative Keypoint Features.” IEEE Internet Computing 13 (3): 56–63. doi:https://doi.org/10.1109/MIC.2009.59.
- Chen, T.-C., S. Dick, and J. Miller. 2010. “Detecting Visually Similar Web Pages: Application to Phishing Detection.” ACM Transactions on Internet Technology 10 (2): 1–38. doi:https://doi.org/10.1145/1754393.1754394.
- Chiew, K. L., C. L. Tan, K. Wong, K. S. C. Yong, and W. K. Tiong. 2019. “A New Hybrid Ensemble Feature Selection Framework for Machine Learning-based Phishing Detection System.” Information Sciences 484: 153–166. doi:https://doi.org/10.1016/j.ins.2019.01.064.
- Chiew, K. L., E. H. Chang, S. N. Sze, and W. K. Tiong. 2015. “Utilisation of Website Logo for Phishing Detection.” Computers & Security 54: 16–26. doi:https://doi.org/10.1016/j.cose.2015.07.006.
- Chiew, K. L., K. S. C. Yong, and C. L. Tan. 2018. “A Survey of Phishing Attacks: Their Types, Vectors and Technical Approaches.” Expert Systems with Applications 106: 1–20. doi:https://doi.org/10.1016/j.eswa.2018.03.050.
- Choudhary, N., and A. K. Jain, “Comparative Analysis of Mobile Phishing Detection and Prevention Approaches,” in Proceedings of International Conference on Information and Communication Technology for Intelligent Systems, Ahmedabad, India, 2017.
- Chu, P., A. Komlodi, and G. Rózsa. 2015. “Online Search in English as a Non-native Language.” Proceedings of the Association for Information Science and Technology 52 (1): 1–9. doi:https://doi.org/10.1002/pra2.2015.145052010040.
- Cobb, M., “Preventing Phishing Attacks: Enterprise Best Practices,” 2010 . [Online]. Available: https://www.computerweekly.com/tip/Preventing-phishing-attacks-Enterprise-best-practices [Accessed 20 September 2020]
- Corona, I., B. Biggio, M. Contini, L. Piras, R. Corda, M. Mereu, G. Mureddu, D. Ariu, and F. Roli, “DeltaPhish: Detecting Phishing Webpages in Compromised Websites,” in Proceedings of European Symposium on Research in Computer Security, Oslo, Norway, 2017.
- Cova, M., C. Kruegel, and G. Vigna, “Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild,” in Proceedings of the 2nd conference on USENIX Workshop on offensive technologies, San Jose, CA, 2008.
- Cui, Q., G.-V. Jourdan, G. V. Bochmann, R. Couturier, and I.-V. Onut, “Tracking Phishing Attacks Over Time,” in 26th International Conference on World Wide Web, Perth, Australia, 2017.
- Cybersecurity Predictions for 2015,” 17 December 2014. [Online]. Available: https://www.proofpoint.com/us/threat-insight/post/Cybersecurity-Predictions-2015 [Accessed 2 April 2020].
- D. Research, “The Risk of Social Engineering on Information Security: A Survey of IT Professionals,” September 2011. [Online]. Available: https://www.stamx.net/files/The-Risk-of-Social-Engineering-on-Information-Security.pdf [Accessed 3 April 2020
- Damopoulo, D., G. Kambourakis, and S. Gritzalis. 2013. “From Keyloggers to Touchloggers: Take the Rough with the Smooth.” Computers & Security 32: 102–114. doi:https://doi.org/10.1016/j.cose.2012.10.002.
- Dhamija, R., J. D. Tygar, and M. Hearst, “Why Phishing Works,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Montréal, Québec, Canada, 2006.
- Ding, Y., N. Luktarhan, K. Li, and W. Slamu. 2019. “A Keyword-based Combination Approach for Detecting Phishing Webpages.” Computers & Security 84: 256–275. doi:https://doi.org/10.1016/j.cose.2019.03.018.
- DNSBL Information – Spam Database Lookup,” [Online]. Available: http://www.dnsbl.info [Accessed 2 April 2020]
- Dunlop, M., S. Groat, and D. Shelly, “GoldPhish: Using Images for Content-based Phishing Analysis,” in Proceedings of International Conference on Internet Monitoring and Protection, Barcelona, Spain, 2010.
- Edwin Donald Frauenstein, R. V. S., “An Enterprise Anti-phishing Framework,” in IFIP World Conference on Information Security Education, Auckland, New Zealand, 2013.
- Egelman, S., L. F. Cranor, and J. Hong, “You’ve Been Warned: An Empirical Study of the Effectiveness of Web Browser Phishing Warnings,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy, 2008.
- El-Alfy, E.-S. M. 2017. “Detection of Phishing Websites Based on Probabilistic Neural Networks and K-Medoids Clustering.” The Computer Journal 60 (12): 1745–1759. doi:https://doi.org/10.1093/comjnl/bxx035
- Enterprise Risk Management — Integrated Framework Executive Summary,” 2004. [Online]. Available: https://www.coso.org/Documents/COSO-ERM-Executive-Summary.pdf [Accessed 20 September 2020].
- Esposito,C., M. Ficco, and B.B. Gupta. 2021. Blockchain-based authentication and authorization for smart city applications. Information Processing & Management 58 (2): 102468.
- Felt, A. P., and D. Wagner, “Phishing on Mobile Devices,” in Proceedings of IEEE Workshop on Web 2.0 Security & Privacy, 2011. San Francisco, CA, USA.
- Fortin, J., “He Tried to Bilk Google and Facebook Out of $100 Million with Fake Invoices,” 25 March 2019. [Online]. Available: https://www.nytimes.com/2019/03/25/business/facebook-google-wire-fraud.html [Accessed 21 January 2021
- Fu, A. Y., L. Wenyin, and X. Deng. 2006. “Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover’s Distance (EMD).” IEEE Transactions on Dependable and Secure Computing 3 (4): 1545–5971. doi:https://doi.org/10.1109/TDSC.2006.50.
- Garera, S., 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, Alexandria, Virginia, USA, 2007.
- GNU Wget,” 2017. [Online]. Available: https://www.gnu.org/software/wget/ [Accessed 4 April 2020].
- Goel, D., and A. K. Jain. 2018. “Mobile Phishing Attacks and Defence Mechanisms: State of Art and Open Research Challenges.” Computers & Security 73: 519–544. doi:https://doi.org/10.1016/j.cose.2017.12.006.
- Google Safe browsing API,” 2017. [Online]. Available: https://developers.google.com/safebrowsing [Accessed 2 April 2020].
- Gowtham, R., and I. Krishnamurthi. 2014. “A Comprehensive and Efficacious Architecture for Detecting Phishing Webpages.” Computers & Security 40: 23–37. doi:https://doi.org/10.1016/j.cose.2013.10.004.
- Gupta, B. B., A. Tewari, A. K. Jain, and D. P. Agrawal. 2017. “Fighting against Phishing Attacks: State of the Art and Future Challenges.” Neural Computing & Applications 28 (12): 3629–3654. doi:https://doi.org/10.1007/s00521-016-2275-y.
- Gupta, B. B., N. A. G. Arachchilage, and K. E. Psannis. 2018. “Defending against Phishing Attacks: Taxonomy of Methods, Current Issues and Future Directions.” Telecommunication Systems 67 (2): 247–267. doi:https://doi.org/10.1007/s11235-017-0334-z.
- Han, W., Y. Cao, E. Bertino, and J. Yong. 2012. “Using Automated Individual White-list to Protect Web Digital Identities.” Expert Systems with Applications 39 (15): 11861–11869. doi:https://doi.org/10.1016/j.eswa.2012.02.020.
- Han, X., N. Kheir, and D. Balzarotti, “PhishEye: Live Monitoring of Sandboxed Phishing Kits,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 2016.
- He, M., S.-J. Horng, P. Fan, M. K. Khan, R.-S. Run, J.-L. Lai, R.-J. Chen, and A. Sutanto. 2011. “An Efficient Phishing Webpage Detector.” Expert Systems with Applications 38 (10): 12018–12027. doi:https://doi.org/10.1016/j.eswa.2011.01.046.
- Hong, J. 2012. “The State of Phishing Attacks.” Communications of the ACM 55 (1): 74–81. doi:https://doi.org/10.1145/2063176.2063197.
- HTTrack Website Copier- Free Software Offline Browser (GNU GPL),” 2017. [Online]. Available: https://www.httrack.com/ [Accessed 4 April 2020].
- Huang, C. Y., S. P. Ma, W. L. Yeh, C. Y. Lin, and C. T. Liu, “Mitigate Web Phishing Using Site Signatures,” in Proceedings of TENCON 2010 IEEE Region 10 Conference, Fukuoka, Japan, 2010.
- Huang, H., J. Tan, and L. Liu, “Countermeasure Techniques for Deceptive Phishing Attack,” in Proceedings of International Conference on New Trends in Information and Service Science, Beijing, China, 2009.
- Huh, J. H., and H. Kim, “Phishing Detection with Popular Search Engines: Simple and Effective,” in Proceedings of the 4th Canada-France MITACS conference on Foundations and Practice of Security, Paris, France, 2011.
- Intrnet Crime Report,” 2018. [Online]. Available: https://pdf.ic3.gov/2018_IC3Report.pdf [Accessed 19 December 2019].
- Invernizzi, L., and P. M. Comparetti, “EvilSeed: A Guided Approach to Finding Malicious Web Pages,” in Proceedings of IEEE Symposium on Security and Privacy, San Francisco, CA, USA, 2012.
- Jagatic, T. N., N. A. Johnson, M. Jakobsson, and F. Menczer. 2007. “Social Phishing.” Communications of the ACM 50 (10): 94–100. doi:https://doi.org/10.1145/1290958.1290968.
- Jain, A. K., and B. B. Gupta. 2016. “A Novel Approach to Protect against Phishing Attacks at Client Side Using Auto-updated White-list.” EURASIP Journal on Information Security (2016 (1): 1–11.
- Jain, A. K., and B. B. Gupta. 2017. “Phishing Detection: Analysis of Visual Similarity Based Approaches.” Security and Communication Networks 2017: 1–20. doi:https://doi.org/10.1155/2017/5421046.
- Jain, A. K., and B. B. Gupta. 2018a. “Detection of Phishing Attacks in Financial and E-banking Websites Using Link and Visual Similarity Relation.” International Journal of Information and Computer Security 10 (4): 398–417. doi:https://doi.org/10.1504/IJICS.2018.095303.
- Jain, A. K., and B. B. Gupta. 2018b. “Two-level Authentication Approach to Protect from Phishing Attacks in Real Time.” Journal of Ambient Intelligence and Humanized Computing 9 (6): 1783–1796. doi:https://doi.org/10.1007/s12652-017-0616-z.
- Jain, A. K., and B. B. Gupta. 2019a. “A Machine Learning Based Approach for Phishing Detection Using Hyperlinks Information.” Journal of Ambient Intelligence and Humanized Computing 10 (5): 2015–2028. doi:https://doi.org/10.1007/s12652-018-0798-z.
- Jain, A. K., and B. B. Gupta. 2019b. “Feature Based Approach for Detection of Smishing Messages in the Mobile Environment.” Journal of Information Technology Research 12 (2): 17–35. doi:https://doi.org/10.4018/JITR.2019040102.
- Jakobsson, M., and S. Myers. 2006. Phishing and Countermeasures: Understanding the Increasing Problem of Electronic Identity Theft. New Jersey, United States: john wiley and sons.
- Joo, J. W., S. Y. Moon, S. Singh, and J. H. Park. 2017. “S-Detector: An Enhanced Security Model for Detecting Smishing Attack for Mobile Computing.” Telecommunication Systems 66 (1): 29–38. doi:https://doi.org/10.1007/s11235-016-0269-9.
- Kim, H., and J. Huh. 2011. “Detecting DNS-poisoning-based Phishing Attacks from Their Network Performance Characteristics.” Electronics Letters 47 (11): 656–658. doi:https://doi.org/10.1049/el.2011.0399.
- Kirlappos, I., and M. A. Sasse. 2012. “Security Education against Phishing: A Modest Proposal for A Major Rethink.” IEEE Security & Privacy Magazine 10 (2): 24–32. doi:https://doi.org/10.1109/MSP.2011.179.
- Kumaraguru, P., Y. Rhee, A. Acquisti, L. F. Cranor, J. Hong, and E. Nunge, “Protecting People from Phishing: The Design and Evaluation of an Embedded Training Email System,” in Proceedings of SIGCHI conference on human factors in computing systems, San Jose, California, USA, 2007a.
- Kumaraguru, P., Y. Rhee, S. Sheng, S. Hasan, A. Acquisti, and L. F. Cranor, “Getting Users to Pay Attention to Anti-phishing Education: Evaluation of Retention and Transfer,” in Proceedings of APWG eCrime Researchers Summit, Pittsburgh, PA, USA, 2007b.
- Lam, I. F., W. C. Xiao, S. C. Wang, and K. T. Chen, “Counteracting Phishing Page Polymorphism: An Image Layout Analysis Approach,” in Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance, Seoul, Korea, 2009.
- Li, Y., L. Yang, and J. Ding. 2016. “A Minimum Enclosing Ball-based Support Vector Machine Approach for Detection of Phishing Websites.” Optik - International Journal for Light and Electron Optics 127 (1): 345–351. doi:https://doi.org/10.1016/j.ijleo.2015.10.078.
- Liu, W., X. Deng, G. Huang, and A. Fu. 2006. “An Antiphishing Strategy Based on Visual Similarity Assessment.” IEEE Internet Computing 10 (2): 1089–7801.
- Lunde, R., S. Franklin, D. Lulich, and G. Pierson, “Detecting and Preventing Man-in-the-middle Phishing Attacks”. Patent US 11/923,561, 1 May 2008.
- Malik, H., and A. S. Malik. 2011. “Towards Identifying the Challenges Associated with Emerging Large Scale Social Networks.” Procedia Computer Science 5: 458–465. doi:https://doi.org/10.1016/j.procs.2011.07.059.
- Mansfield-Devine, S. 2016. “Ransomware: Taking Businesses Hostage.” Network Security 2016 (10): 8–17. doi:https://doi.org/10.1016/S1353-4858(16)30096-4.
- Mao, J., P. Li, K. Li, T. Wei, and Z. Liang, “Baitalarm: Detecting Phishing Sites Using Similarity in Fundamental Visual Features,” in Proceedings of 5th International Conference on Intelligent Networking and Collaborative Systems, Xi’an, China, 2013.
- Marchal, S., G. Armano, T. Gröndahl, K. Saari, N. Singh, and N. Asokan. 2017. “Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application.” IEEE Transactions on Computers 66 (10): 1717–1733. doi:https://doi.org/10.1109/TC.2017.2703808.
- Marchal, S., J. François, R. State, and T. Engel. 2014. “PhishStorm: Detecting Phishing with Streaming Analytics.” IEEE Transactions on Network and Service Management 11 (4): 458–471. doi:https://doi.org/10.1109/TNSM.2014.2377295.
- Medvet, E., E. Kirda, and C. Kruegel, “Visual-similarity-based Phishing Detection,” in Proceedings of the 4th international conference on Security and privacy in communication netowrks, Istanbul, Turkey, 2008.
- Moghimi, M., and A. Y. Varjani. 2016. “New Rule-based Phishing Detection Method.” Expert Systems with Applications 53: 231–242. doi:https://doi.org/10.1016/j.eswa.2016.01.028.
- Mohammad, R. M., F. Thabtah, and L. McCluskey. 2014a. “Intelligent Rule-based Phishing Websites Classification.” IET Information Security 8 (3): 153–160. doi:https://doi.org/10.1049/iet-ifs.2013.0202.
- Mohammad, R. M., F. Thabtah, and L. McCluskey. 2014b. “Predicting Phishing Websites Based on Self-structuring Neural Network.” Neural Computing & Applications 25 (2): 443–458. doi:https://doi.org/10.1007/s00521-013-1490-z.
- Mohammad, R. M., F. Thabtah, and L. McCluskey. 2015. “Tutorial and Critical Analysis of Phishing Websites Methods.” Computer Science Review 17: 1–24. doi:https://doi.org/10.1016/j.cosrev.2015.04.001.
- Montazera, G. A., and S. ArabYarmohammadi. 2015. “Detection of Phishing Attacks in Iranian E-banking Using a Fuzzy–rough Hybrid System.” Applied Soft Computing 35: 482–492. doi:https://doi.org/10.1016/j.asoc.2015.05.059.
- Moore, T., and R. Clayton, “Examining the Impact of Website Take-down on Phishing,” in Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit, Pittsburgh, Pennsylvania, USA, 2007.
- Oliveira, D., H. Rocha, H. Yang, D. Ellis, S. Dommaraju, M. Muradoglu, D. Weir, A. Soliman, T. Lin, and N. Ebne, “Dissecting Spear Phishing Emails for Older Vs Young Adults: On the Interplay of Weapons of Influence and Life Domains in Predicting Susceptibility to Phishing,” in Proceedings of 2017 CHI Conference on Human Factors in Computing Systems, Denver, Colorado, USA, 2017.
- Ollmann, G., “The Phishing Guide Understanding & Preventing Phishing Attacks,” 2007. [Online]. Available: https://www.nccgroup.trust/uk/our-research/the-phishing-guide-understanding-preventing-phishing-attacks/
- Page, S. L., G. V. Bochmann, Q. Cui, J. Flood, G. Jourdan, and I. Onu, “Using AP-TED to Detect Phishing Attack Variations,” in2018 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, 2018.
- Page, S. L., G.-V. Jourdan, G. V. Bochmann, I.-V. Onut, and J. Flood, “Domain Classifier: Compromised Machines Versus Malicious Registrations,” in International Conference on Web Engineering, Daejeon, Republic of Korea, 2019.
- Pan, Y., and X. Ding, “Anomaly Based Web Phishing Page Detection,” in Proceedings of 22nd Annual Computer Security Applications Conference, Miami Beach, FL, USA, 2006.
- Parmar, B. 2012. “Protecting against Spear-phishing.” Computer Fraud & Security 2012 (1): 8–11. doi:https://doi.org/10.1016/S1361-3723(12)70007-6.
- Pawlik, M., and N. Augsten. 2016. “And NikolausAugsten, “Tree Edit Distance: Robust and Memory-efficient.” Information Systems 56: 157–173. doi:https://doi.org/10.1016/j.is.2015.08.004.
- Perdisci, R., M. Antonakakis, X. Luo, and W. Lee, “WSEC DNS: Protecting Recursive DNS Resolvers from Poisoning Attacks,” in Proceedings of EEE/IFIP International Conference on Dependable Systems & Networks, Lisbon, Portugal, 2009.
- Phishing activity trends report - fourth quarter 2012,” April 2013. [Online]. Available: http://docs.apwg.org/reports/apwg_trends_report_Q4_2012.pdf [Accessed 2 April 2020].
- Phishing activity trends report - third quarter 2012,” February 2013. [Online]. Available: www.apwg.org/download/document/84/apwg_trends_report_q3_2012.pdf [Accessed 2 April 2020].
- Phishing attacks,” [Online]. Available: https://www.imperva.com/learn/application-security/phishing-attack-scam/ [Accessed 20 September 2020
- Phishing Simulation Platform,” Terranova Security, [Online]. Available: https://terranovasecurity.com/phishing-simulation/ [Accessed 30 December 2019
- Phishing, Vishing and Smishing: Old Threats Present New Risks,” 2009. [ Online]. Available: https://www.emc.com/collateral/white-papers/h11933-wp-phishing-vishing-smishing.pdf [Accessed 2 April 2020]
- Phishingpro, “Everyone Is a Target,” 2016. [Online]. Available: http://www.razorthorn.co.uk/wp-content/uploads/2017/01/Phishing-Stats-2016.pdf [Accessed 4 April 2020].
- Prakash, P., M. Kumar, R. R. Kompella, and M. Gupta, “Phishnet: Predictive Black-listing to Detect Phishing Attacks,” in Proceedings of IEEE INFOCOM, San Diego, CA, USA, 2010.
- Protecting your Enterprise from Phishing Attacks,” 20 June 2017a. [Online]. Available: https://www.seqrite.com/blog/protecting-your-enterprise-from-phishing-attacks/ [Accessed 14 September 2020].
- Protecting your Enterprise from Phishing Attacks,” 20 June 2017b. [Online]. Available: https://www.seqrite.com/blog/protecting-your-enterprise-from-phishing-attacks/ [Accessed 20 September 2020].
- Purkait, S. 2012. “Phishing Counter Measures and Their Effectiveness – Literature Review.” Information Management & Computer Security 20 (5): 382–420. doi:https://doi.org/10.1108/09685221211286548.
- Purkait, S. 2015. “Examining the Effectiveness of Phishing Filters against DNS Based Phishing Attacks.” Information and Computer Security 23 (3): 333–346. doi:https://doi.org/10.1108/ICS-02-2013-0009.
- Rader, M., and S. Rahman, “Exploring Historical and Emerging Phishing Techniques and Mitigating the Associated Security Risks,” 2015. [Online]. Available: arXiv:1512.00082.
- Ramesh, G., I. Krishnamurthi, and K. Kumar. 2014. “An Efficacious Method for Detecting Phishing webpages through Target Domain Identification.” Decision Support Systems 61: 12–22. doi:https://doi.org/10.1016/j.dss.2014.01.002.
- Ransomware Delivered by 97% of Phishing Emails by end of Q3 2016,” 2016. [Online]. Available: https://phishme.com/ransomware-delivered-97-phishing-emails-end-q3-2016-supporting-booming-cybercrime-industry/ [Accessed 14 April 2019]
- Rao, R. S., and A. R. Pais. 2019. “Jail-Phish: An Improved Search Engine Based Phishing Detection System.” Computers & Security 83: 246–267. doi:https://doi.org/10.1016/j.cose.2019.02.011.
- RiskIQ, Ransomware in Health Sector 2020: A Perfect Storm of New Targets and Methods,” [Online]. Available: https://www.riskiq.com/wp-content/uploads/2020/04/Ransomware-in-Health-Sector-Intelligence-Brief-RiskIQ.pdf [Accessed 6 July 2020].
- S. P. S. V. H. T. V. B. S. L, and G. Cj, “Phishy-a Serious Game to Train Enterprise Users on Phishing Awareness,” in 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, 2018. New York, NY, United States.
- Sahingoz, O. K., E. Buber, O. Demir, and B. Diri. 2019. “Machine Learning Based Phishing Detection from URLs.” Expert Systems With Applications 117: 345–357. doi:https://doi.org/10.1016/j.eswa.2018.09.029.
- Samarati, M., “Phishing Attacks: 6 Reasons Why We Keep Taking the Bait,” 4 September 2020. [Online]. Available: https://www.itgovernance.co.uk/blog/6-reasons-phishing-is-so-popular-and-so-successful [Accessed 26 September 2020
- Sheng, S., M. Holbrook, P. Kumaraguru, L. Cranor, and J. Downs, “Who Falls for Phish?: A Demographic Analysis of Phishing Susceptibility and Effectiveness of Interventions,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA, 2010.
- Silic, M., and A. Back. 2016. “The Dark Side of Social Networking sites:Understanding Phishing Risks.” Computers in Human Behavior 60: 35–43. doi:https://doi.org/10.1016/j.chb.2016.02.050.
- Simulated Phishing Attacks and Knowledge Assessments,” Proofpoint, [Online]. Available: https://www.proofpoint.com/us/products/phishing-simulations-knowledge-assessments [Accessed 23 December 2019].
- Social Networkers Beware: Facebook is a Major Phishing Portal,” 2014 . [Online]. Available: https://www.kaspersky.com/blog/1-in-5-phishing-attacks-targets-facebook/5180/ [Accessed 3 April 2020]
- Song, Y., C. Yang, and G. Gu, “Who Is Peeping at Your Passwords at Starbucks? — To Catch an Evil Twin Access Point,” in Proceedings of IEEE/IFIP International Conference on Dependable Systems & Networks, Chicago, IL, USA, 2010.
- State of the Phish 2019, wombat security phishing report,” [Online]. Available: https://www.proofpoint.com/sites/default/files/pfpt-us-tr-state-of-the-phish-2019.pdf [Accessed 15 January 2020]
- Tan, C. L., K. L. Chiew, K. Wong, and S. N. Sze. 2016. “PhishWHO: Phishing Webpage Detection via Identity Keywords Extraction and Target Domain Name Finder.” Decision Support Systems 88: 18–27. doi:https://doi.org/10.1016/j.dss.2016.05.005.
- 6 reasons why SMS is more effective than Email Marketing,” August 2016. [Online]. Available: https://callhub.io/6-reasons-sms-effective-email-marketing/ [Accessed 3 April 2020].
- Tewari, A., and B. B. Gupta. 2020. Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework. Future generation computer systems 108: 909–920
- The 2017 Human Factor Report,” 2017. [Online]. Available: https://www.proofpoint.com/us/human-factor-2017 [Accessed 14 April 2019].
- 2018 Internet Crime Report,” [Online]. Available: https://pdf.ic3.gov/2018_IC3Report.pdf [Accessed 3 July 2020].
- The Dirty Dozen: The 12 Most Costly Phishing Attack Examples,” 7 June 2019. [Online]. Available: https://www.thesslstore.com/blog/the-dirty-dozen-the-12-most-costly-phishing-attack-examples/ [Accessed 14 September 2020
- THE GROWING THREAT OF MOBILE DEVICE SECURITY BREACHES,” April 2017. [Online]. Available: https://blog.checkpoint.com/wp-content/uploads/2017/04/Dimensional_Enterprise-Mobile-Security-Survey.pdf [Accessed 14 March 2020].
- The story of a phish,” 2016. [Online]. Available: https://phishme.com/wp-content/uploads/2016/07/phishme-story-of-a-phish-1.pdf [Accessed 4 April 2020].
- Trend Micro, Phishing simulation and Security awareness training,” [Online]. Available: https://phishinsight.trendmicro.com/en/ [Accessed 23 December 2019
- Usage of content languages for websites,” 2017. [Online]. Available: https://w3techs.com/technologies/overview/content_language/all [Accessed 2 April 2020].
- Varshney, G., M. Misra, and P. K. Atrey. 2016a. “A Survey and Classification of Web Phishing Detection Schemes.” Security and Communication Networks 9 (18): 6266–6284. doi:https://doi.org/10.1002/sec.1674.
- Varshney, G., M. Misra, and P. K. Atrey. 2016b. “A Phish Detector Using Lightweight Search Features.” Computers & Security 62: 213–228. doi:https://doi.org/10.1016/j.cose.2016.08.003.
- Vishwanath, A. 2015. “Habitual Facebook Use and Its Impact on Getting Deceived on Social Media.” Journal of Computer-Mediated Communication 20 (1): 83–98. doi:https://doi.org/10.1111/jcc4.12100.
- Wang, J., T. Herath, R. Chen, A. Vishwanath, and H. R. Rao. 2012. “Research Article Phishing Susceptibility: An Investigation into the Processing of a Targeted Spear Phishing Email.” IEEE Transactions on Professional Communication 55 (4): 345–362. doi:https://doi.org/10.1109/TPC.2012.2208392.
- Wenyin, L., N. Fang, X. Quan, B. Qiu, and G. Liu. 2010. “Discovering Phishing Target Based on Semantic Link Network.” Future Generation Computer Systems 26 (3): 381–388. doi:https://doi.org/10.1016/j.future.2009.07.012.
- Whittaker, C., B. Ryner, and M. Nazif, “Large-Scale Automatic Classification of Phishing Pages,” in Proceedings of the Network and Distributed System Security Symposium, San diego, California, USA, 2010.
- Williamson, M. M., A. Parry, and A. Byde, “Virus Throttling for Instant Messaging,” 5 May 2004. [Online]. Available: http://www.hpl.hp.com/techreports/2004/HPL-2004-81.pdf?q=instantmessaging [Accessed 2 April 2020].
- Wu, L., X. Du, and J. Wu. 2016. “Effective Defense Schemes for Phishing Attacks on Mobile Computing Platforms.” IEEE Transactions on Vehicular Technology 65 (8): 6678–6691. doi:https://doi.org/10.1109/TVT.2015.2472993.
- Wu, M., R. C. Miller, and S. L. Garfinkel, “Do Security Toolbars Actually Prevent Phishing Attacks?,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Montréal, Québec, Canada, 2006.
- Xiang, G., J. Hong, C. P. Rose, and L. Cranor. 2011. “CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites.” ACM Transactions on Information and System Security 14 (2): 1–28. doi:https://doi.org/10.1145/2019599.2019606.
- Xiang, G., and J. I. Hong, “A Hybrid Phish Detection Approach by Identity Discovery and Keywords Retrieval,” in Proceedings of the 18th international conference on World wide web, Madrid, Spain, 2009.
- Yang, C. C., S. S. Tseng, T. J. Lee, J. F. Weng, and K. Chen, “Building an Anti-phishing Game to Enhance Network Security Literacy Learning,” in Proceedings of 12th International Conference on Advanced Learning Technologies (ICALT), Rome, Italy, 2012.
- Yang, W., A. Xiong, J. Chen, R. W. Proctor, and N. L, “Use of Phishing Training to Improve Security Warning Compliance: Evidence from a Field Experiment,” in Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp, Hanover, MD, USA, 2017.
- Yoon, C., C. Yoon, Y. Kim, Y. Kim, and Y. Kim, “Doppelgängers on the Dark Web: A Large-scale Assessment on Phishing Hidden Web Services,” in The World Wide Web Conference, San Francisco, CA, USA, 2019.
- Youngsun Kwak, S. L. A. D. A. V. 2020. “Why Do Users Not Report Spear Phishing Emails?” Telematics and Informatics 48: 101343.
- Yu, C., J. Li, X. Li, X. Ren, et al. 2018. Four-image encryption scheme based on quaternion Fresnel transform, chaos and computer generated hologram. Multimedia Tools and Applications, 77(4): 4585–4608
- Yu, W. D., S. Nargundkar, and N. Tiruthani, “PhishCatch - A Phishing Detection Tool,” in Proceedings of the 33rd Annual IEEE International Computer Software and Applications Conference, 2009, NW Washington, DC, United States.
- Zhang, H., G. Liu, T. W. S. Chow, and W. Liu. 2011. “Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach.” IEEE Transactions on Neural Networks 22 (10): 1532–1546. doi:https://doi.org/10.1109/TNN.2011.2161999.
- Zhang, W., Q. Jiang, L. Chen, and C. Li. 2017. “Two-stage ELM for Phishing Web Pages Detection Using Hybrid Features.” World Wide Web 20 (4): 797–813. doi:https://doi.org/10.1007/s11280-016-0418-9.
- Zhang, Y., J. I. Hong, and L. F. Cranor, “CANTINA: A Content-Based Approach to Detecting Phishing Websites,” in Proceedings of 16th International World Wide Web Conference (WWW2007), Banff, Alberta, Canada, 2007.
- Zhang, Y., S. Egelman, L. Cranor, and J. Hong, “Phinding Phish: Evaluating Anti-phishing Tools,” in Proceedings of the 14th Annual Network & Distributed System Security Symposium, San Diego, California, USA, 2007.
- Zuhair, H., A. Selamat, and M. Salleh. 2016. “Feature Selection for Phishing Detection: A Review of Research.” International Journal of Intelligent Systems Technologies and Applications 15 (2): 147–162. doi:https://doi.org/10.1504/IJISTA.2016.076495.