A Comprehensive Survey of Phishing Email Detection and Protection Techniques

References

• 364998034417000eBay Toolbar. (2011). ebay toolbar. Retrieved from http://download.cnet.com/eBay-Toolbar/3000-125124-10153544.html?tag=contentMain;downloadLinks.
   Google Scholar
• Abu-Nimeh, S., Nappa, D., Wang, X., & Nair, S. (2007). 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). ACM: Pittsburgh, PA
   Google Scholar
• Adhikaryet et al (2012). Battering keyloggers and screen recording software by fabricating passwords. International Journal of Computer Network and Information Security, 4(5), 13.
   Google Scholar
• Akinyelu, A. A., & Adewumi, A. O. (2014). Classification of phishing email using random forest machine learning technique. Journal of Applied Mathematics, 2014.https://doi.org/https://doi.org/10.1155/2014/425731
   Google Scholar
• Al Halaseh, R., & Alqatawna, J. (2016). Analyzing cybercrimes strategies: the case of phishing attack. In 2016 cybersecurity and cyberforensics conference (ccc) (pp. 82–88). IEEE: Amman, Jordan
   Google Scholar
• Al-Daeef, M. M., Basir, N., & Saudi, M. M. (2016). A review of client-side toolbars as a User-Oriented Anti-Phishing Solution. In In: Sulaiman H., Othman M., Othman M., Rahim Y., Pee N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 362. Springer, Cham.(pp. 427–437). Springer.
   Google Scholar
• Aleroud, A., & Zhou, L. (2017). Phishing environments, techniques, and counter- measures: A survey. Computers & Security, 68, 160–196. https://doi.org/https://doi.org/10.1016/j.cose.2017.04.006
   Web of Science ®Google Scholar
• Almomani, A., Gupta, B. B., Atawneh, S., Meulenberg, A., & Almomani, E. (2013). A survey of phishing email filtering techniques. IEEE Communications Surveys & Tutorials, 15(4), 2070–2090. https://doi.org/https://doi.org/10.1109/SURV.2013.030713.00020
   Web of Science ®Google Scholar
• Alqatawna, J., Faris, H., Jaradat, K., Al-Zewairi, M., & Adwan, O. (2015). Improv- ing knowledge based spam detection methods: the effect of malicious related features in imbalance data distribution. International Journal of Communications, Network and System Sciences, 8(05), 118. https://doi.org/https://doi.org/10.4236/ijcns.2015.85014
   Google Scholar
• Alqatawna, J., Hadi, A., Al-Zwairi, M., & Khader, M. (2016). A preliminary analysis of drive-by email attacks in educational institutes. In 2016 cybersecurity and cyberforensics conference (ccc) (pp. 65–69). IEEE: Amman, Jordan
   Google Scholar
• Al-Saedi, K., ALnajjar, A., & Ramadass, S. (2012). A survey of learning based tech- niques of phishing email filtering. International Journal of Digital Content Technology and its Applications, 6(18)
   Google Scholar
• Alseadoon, I., Chan, T., Foo, E., & Gonzalez Nieto, J. (2012). Who is more susceptible to phishing emails?: A saudi arabian study. 23rd Australasian Conference on Information Systems. Date & Venue: 3–5 Dec 2012, Geelong
   Google Scholar
• Amayri, O., & Bouguila, N. (2010). A study of spam filtering using support vector machines. Artificial Intelligence Review, 34(1), 73–108. https://doi.org/https://doi.org/10.1007/s10462-010-9166-x
   Web of Science ®Google Scholar
• Apwg, A. (2020). Phishing activity trends report: 2nd quarter 2020. Anti-Phishing Working Group. Retrieved March. fhttps://docs.apwg.org/reports/apwg_trends_report_q2_2020.pd
   Google Scholar
• Arachchilage, N. A. G., & Cole, M. (2011 June).Design a mobile game for home computer users to prevent from “phishing attacks”. In International conference on information society (i-society 2011) (pp. 485–489). London, United Kingdom, IEEE.
   Google Scholar
• Banday, M. T., & Sheikh, S. A. (2013). Folder classification of urdu and hindi language e-mail messages.In International eConference on Computer and Knowledge Engineering (ICCKE), pp. 59-63, Mashhad - IRAN, IEEE.
   Google Scholar
• Banu, M. N., & Banu, S. M. (2013). A comprehensive study of phishing attacks. International Journal of Computer Science and Information Technologies, 4(6), 783–786.https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.643.766&rep=rep1&type=pdf
   Google Scholar
• Basnet, R., Mukkamala, S., Sung, A. H. (2008). Detection of phishing attacks: A machine learning approach. In: Prasad B. (eds) Soft computing applications in industry Studies in Fuzziness and Soft Computing, vol 226. Springer, Berlin, Heidelberg. (pp. 373–383). https://doi.org/https://doi.org/10.1007/978-3-540-77465-5_19
   Google Scholar
• Bazarganigilani, M. (2011). Phishing e-mail detection using ontology concept and naive bayes algorithm. International Journal of Research and Reviews in Computer Science, 2(2), 249.
   Google Scholar
• Bergholz, A., Paaß, G., Reichartz, F., & Strobel, S. (2008)Improved phishing detection using model-based featuresIn In Fifth Conference on Email and Anti-Spam, CEAS, Mountain View, California,.
   Google Scholar
• Bin, S., Qiaoyan, W., & Xiaoying, L. (2010). A dns based anti-phishing approach. In 2010 second international conference on networks security, wireless communications and trusted computing (Vol. 2, pp. 262–265). IEEE: Wuhan, Hubei, China
   Google Scholar
• Bouchard-coˆ Te´, A., & Sahin, F. (2009). Feature engineering and selection. CS.https://adataanalyst.com/wp-content/uploads/2016/08/slides1.pdf
   Google Scholar
• CallingID. (2011). Your protection from identity theft, fraud, scams and malware. accessed 21 july 2011. http://www.callingid.com/default.aspx. CallingID,2011.
   Google Scholar
• José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Rafael Morales-Bueno, and Albert Bifet. 2010. GNUsmail: (2011). Open framework for on-line email classification.Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence IOS Press, NLD, 1141–1142.
   Google Scholar
• Catching. (2011). Catching phish: detecting phishing attacks from rendered web- site images. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.9084&rep=rep1&type=pdf.
   Google Scholar
• Chandrasekaran, M., Narayanan, K., & Upadhyaya, S. (2006). Phishing email detec- tion based on structural properties. In Nys cyber security conference (Vol. 3). Albany, New York
   Google Scholar
• Chen, H., Zhan, Y., & Li, Y. (2010). The application of decision tree in chinese email classification. In 2010 international conference on machine learning and cybernetics (Vol. 1, pp. 305–308). IEEE: Qingdao, China
   Google Scholar
• Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications, 106, 1–20. https://doi.org/https://doi.org/10.1016/j.eswa.2018.03.050
   Web of Science ®Google Scholar
• CloudMark. (2011). accessed 26 july 2011. http://www.cloudmark.com/en/products/cloudmark-desktopone/index. CloudMark,2011.
   Google Scholar
• Coopers, P. (2015). Turnaround and transformation in cybersecurity: Key findings from the global state of information security survey 2016. Pricewaterhouse Cooper.
   Google Scholar
• Coyotes, C., Mohan, V. S., Naveen, J.R, Vinayakumar, R., Soman, K.P,& Verma, A.D. R, (2018,March). ARES: automatic rogue email spotter. In Proc. 1st AntiPhishing Shared Pilot 4th ACM Int. Workshop Secur. Privacy Anal.(IWSPA). Tempe, AZ, USA.
   Google Scholar
• Del Castillo, M. D., Iglesias, A., & Serrano, J. I. (2007). Detecting phishing e-mails by heterogeneous classification. In International conference on intelligent data en- gineering and automated learning (pp. 296–305). Springer: Birmingham, United Kingdom.
   Google Scholar
• Dewan, P., Kashyap, A., & Kumaraguru, P. (2014). Analyzing social and stylometric features to identify spear phishing emails. In 2014 apwg symposium on electronic crime research (ecrime) (pp. 1–13). IEEE: Birmingham, Alabama.
   Google Scholar
• Faris, H., Aljarah, I., & Alqatawna, J. (2015). Optimizing feedforward neural net- works using krill herd algorithm for e-mail spam detection. In 2015 ieee jordan conference on applied electrical engineering and computing technologies (aeect) (pp. 1–5). IEEE: Jordan.
   Google Scholar
• Fatima, A., Khan, F. A., Raza, A., & Kamran, A. B. (2018). Automated feature syn- thesis from relational database for data science related problems. In 2018 inter- national conference on frontiers of information technology (fit) (pp. 71–75). IEEE: Islamabad, Pakistan.
   Google Scholar
• Fette, I., Sadeh, N., & Tomasic, A. (2007). Learning to detect phishing emails. In Proceedings of the 16th international conference on world wide web (pp. 649–656). Filter, I. P. (2011). Ie phishing filter. Banff Alberta, Canada.
   Google Scholar
• FirePhish. (2011). accessed 26 july 2011. https://addons.mozilla.org/en-us/firefox/addon/firephish-antiphishing-extens/. FirePhish,2011.
   Google Scholar
• Firte, L., Lemnaru, C., & Potolea, R. (2010). Spam detection filter using knn algorithm and resampling. In Proceedings of the 2010 ieee 6th international conference on intelligent computer communication and processing (pp. 27–33). Cluj-Napoca, Romania
   Google Scholar
• Gansterer, W. N., & Po¨ Lz, D. (2009). E-mail classification for phishing defense. In European conference on information retrieval (pp. 449–460). ACM: Toulouse, France.
   Google Scholar
• Garg, A., & Maheshwari, P. (2016). Performance analysis of snort-based intrusion detection system. In 2016 3rd international conference on advanced computing and communication systems (icaccs) (Vol. 1, pp. 1–5). IEEE: Coimbatore, India.
   Google Scholar
• Gomes, S. R., Saroar, S. G., Mosfaiul, M., Telot, A., Khan, B. N., Chakrabarty, A., & Mostakim, M. (2017). A comparative approach to email classification using Naive Bayes classifier and hidden Markov model. In 2017 4th international conference on advances in Electrical Engineering (ICAEE) (pp. 482–487). Dhaka, Bangladesh, IEEE.
   Google Scholar
• Gupta, B. B., Arachchilage, N. A., & Psannis, K. E. (2018). Defending against phishing attacks: Taxonomy of methods, current issues and future directions. Telecommu- Nication Systems, 67(2), 247–267. https://doi.org/https://doi.org/10.1007/s11235-017-0334-z
   Web of Science ®Google Scholar
• Gupta, B. B., Tewari, A., Jain, A. K., & Agrawal, D. P. (2017). Fighting against phishing attacks: State of the art and future challenges. Neural Computing & Applications, 28(12), 3629–3654. https://doi.org/https://doi.org/10.1007/s00521-016-2275-y
   Web of Science ®Google Scholar
• Gutie´rrez, L. F., Abri, F., Armstrong, M., Namin, A. S., & Jones, K. S. (2020). Email embeddings for phishing detection. In 2020 ieee international conference on big data (big data) (pp. 2087–2092). IEEE: Atlanta, United States.
   Google Scholar
• Gutierrez, C. N., Kim, T., Della Corte, R., Avery, J., Goldwasser, D., Cinque, M., Bagchi, S., (2018). Learning from the ones that got away: Detecting new forms of phishing attacks. IEEE Transactions on Dependable and Secure Computing, 15(6), 988–1001.
   Web of Science ®Google Scholar
• Guzella, T. S., & Caminhas, W. M. (2009). A review of machine learning approaches to spam filtering. Expert Systems with Applications, 36(7), 10206–10222. https://doi.org/https://doi.org/10.1016/j.eswa.2009.02.037
   Web of Science ®Google Scholar
• Hamid, I. R. A., & Abawajy, J. (2011). Hybrid feature selection for phishing email detection. In International conference on algorithms and architectures for parallel processing (pp. 266–275). Melbourne Australia.
   Google Scholar
• Hamid, I. R. A., Abawajy, J., & Kim, T. (2013). Using feature selection and classifica- tion scheme for automating phishing email detection. Studies in Informatics and Control, 22(1), 61–70. https://doi.org/https://doi.org/10.24846/v22i1y201307
   Web of Science ®Google Scholar
• Harikrishnan, N., Vinayakumar, R., & Soman, K. (2018). A machine learning ap- proach towards phishing email detection. In Proceedings of the anti-phishing pi- lot at acm international workshop on security and privacy analytics (iwspa ap) (Vol. 2013, pp. 455–468).
   Google Scholar
• Harisinghaney, A., Dixit, A., Gupta, S., & Arora, A. (2014). Text and image based spam email classification using knn, na¨ıve bayes and reverse dbscan algorithm. In 2014 international conference on reliability optimization and information technol- ogy (icroit) (pp. 153–155). IEEE: Faridabad, India.
   Google Scholar
• HBa, B. G., Ra, V., Ma, A. K., & KPa, S. (n.d.). Distributed representation using target classes: Bag of tricks for security and privacy analytics.http://ceur-ws.org/Vol-2124/paper_10.pdf
   Google Scholar
• Hodzˇic´, A., Kevric´, J., & Karadag, A. (2016). Comparison of machine learning tech- niques in phishing website classification. In International conference on economic and social studies (icesos’16) (pp. 249–256). International Burch University: Sarajevo, Bosnia and Herzegovina.
   Google Scholar
• InterNIC. (2012). Whois search, internic - public information regard- ing internel domain name registration services. accessed avail- able: 16-6- 2012. http://www.internic.net/whois.html.
   Google Scholar
• Jain, A. K., & Gupta, B. B. (2016). A novel approach to protect against phishing attacks at client side using auto-updated white-list. EURASIP Journal on Infor- Mation Security, 2016(1), 9. https://doi.org/https://doi.org/10.1186/s13635-016-0034-3
   Google Scholar
• Jakobsson, M., & Myers, S. (2007). Phishing and countermeasures, microsoft’s an- tiphishing technologies and tactics. Wiley.
   Google Scholar
• Kanter, J. M., & Veeramachaneni, K. (2015). Deep feature synthesis: Towards au- tomating data science endeavors. In 2015 ieee international conference on data science and advanced analytics (dsaa) (pp. 1–10). IEEE: Paris, France.
   Google Scholar
• Karim, A., Azam, S., Shanmugam, B., Kannoorpatti, K., & Alazab, M. (2019). A comprehensive survey for intelligent spam email detection. IEEE Access, 7, 168261–168295. https://doi.org/https://doi.org/10.1109/ACCESS.2019.2954791
   Web of Science ®Google Scholar
• Khadir, R. P., & Sony, P. (2015). Efforts and methodologies used in phishing email detection and filtering: A survey. International Journal of Advanced Research in Computer Science, 6(2). https://doi.org/https://doi.org/10.26483/ijarcs.v6i2.2432.
   Google Scholar
• Khonji, M., Jones, A., & Iraqi, Y. (2011a). A novel phishing classification based on url features. In 2011 ieee gcc conference and exhibition (gcc) (pp. 221–224). IEEE: Dubai, United Arab Emirates.
   Google Scholar
• Khonji, M., Jones, A., & Iraqi, Y. (2011b). A study of feature subset evaluators and feature subset searching methods for phishing classification. In Proceedings of the 8th annual collaboration, electronic messaging, anti-abuse and spam conference (pp. 135–144). ACM: Perth Australia.
   Google Scholar
• Kim, H., Howland, P., & Park, H. (2005). Dimension reduction in text classification with support vector machines. Journal of Machine Learning Research, 6(Jan), 37–53.https://www.jmlr.org/papers/volume6/kim05a/kim05a.pdf
   Google Scholar
• Kiwanuka, F. N., Alqatawna, J., Amin, A. H. M., Paul, S., & Faris, H. (2019). Towards automated comprehensive feature engineering for spam detection. In In Proceedings of the 5th International Conference on Information Systems Security and Privacy (ICISSP 2019), (pp. 429–437). Prague, Czech Republic.
   Google Scholar
• Kumar, J., Santhanavijayan, A., Janet, B., Rajendran, B., & Bindhumadhava, B. (2020). Phishing website classification and detection using machine learning. In 2020 international conference on computer communication and informatics (iccci) (pp. 1–6). Coimbatore, India.
   Google Scholar
• Kumari, R., & Srivastava, S. K. (2017). Machine learning: A review on binary classi- fication. International Journal of Computer Applications, 160(7): 11-15. https://doi.org/https://doi.org/10.5120/ijca2017913083
   Google Scholar
• L’Huillier, G., Hevia, A., Weber, R., & Rios, S. (2010). Latent semantic analysis and keyword extraction for phishing classification. In 2010 ieee international confer- ence on intelligence and security informatics (pp. 129–131). Vancouver, BC, Canada.
   Google Scholar
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436–444. https://doi.org/https://doi.org/10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Lindeberg, T. (2012). Scale invariant feature transform.  https://www.diva-portal.org/smash/get/diva2:480321/FULLTEXT02.
   Google Scholar
• Liu, S., & Lee, I. (2018). Email sentiment analysis through k-means labeling and support vector machine classification. Cybernetics and Systems, 49(3), 181–199. https://doi.org/https://doi.org/10.1080/01969722.2018.1448242
   Web of Science ®Google Scholar
• Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Pro- ceedings of the seventh ieee international conference on computer vision (Vol. 2, pp. 1150–1157). IEEE: Kerkyra, Greece.
   Google Scholar
• Lunde, R., Franklin, S., Lulich, D., & Pierson, G. (2008, May 1). Detecting and pre- venting man-in-the-middle phishing attacks. Google Patents. (US Patent App. 11/923,561)
   Google Scholar
• Mitchell, T. M. (1997). Machine learning. McGraw hill.
   Google Scholar
• Miyamoto, D., Hazeyama, H., & Kadobayashi, Y. (2008). An evaluation of machine learning-based methods for detection of phishing sites. In International confer- ence on neural information processing (pp. 539–546). Vancouver, BC, Canada.
   Google Scholar
• Moore, T., & Clayton, R. (2012). Discovering phishing dropboxes using email meta- data. In 2012 ecrime researchers summit (pp. 1–9). IEEE: Las Croabas, PR, USA.
   Google Scholar
• Mori, T. (2002). Information gain ratio as term weight: The case of summarization of ir results. In Coling 2002: the 19th international conference on computational linguistics. ACM: Taipei, Taiwan.
   Google Scholar
• Mujtaba, G., Shuib, L., Raj, R. G., Majeed, N., & Al-Garadi, M. A. (2017). Email classification research trends: review and open issues. IEEE Access, 5, 9044–9064. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2702187
   Web of Science ®Google Scholar
• Muppavarapu, V., Rajendran, A., & Vasudevan, S. K. (2018). Phishing detection using rdf and random forests. Int. Arab J. Inf. Technol, 15(5), 817–824. http://iajit.org/PDF/September%202018,%20No.%205/10600.pdf
   Web of Science ®Google Scholar
• Parsons, K., McCormac, A., Pattinson, M., Butavicius, M., & Jerram, C. (2013). Phish- ing for the truth: A scenario-based experiment of users’ behavioural response to emails. In Ifip international information security conference (pp. 366–378). Auckland, New Zealand.
   Google Scholar
• Parsons, K., McCormac, A., Pattinson, M., Butavicius, M., & Jerram, C. (2015). The design of phishing studies: challenges for researchers. Computers & Security, 52, 194–206. https://doi.org/https://doi.org/10.1016/j.cose.2015.02.008
   Web of Science ®Google Scholar
• Pienta, D., Thatcher, J. B., & Johnston, A. C. (2018). A taxonomy of phishing: at- tack types spanning economic, temporal, breadth, and target boundaries. In Proceedings of the 13th pre-icis workshop on information security and privacy (Vol. 1), san francisco, ca, usa.
   Google Scholar
• Ra, V., HBa, B. G., Ma, A. K., KPa, S.,Poornachandran, P.,&Verma, A., (2018). DeepAnti-PhishNet: Applying deep neural networks for phishing email detectionInProc. 1st AntiPhishing Shared Pilot 4th ACM Int. Workshop Secur. Privacy Anal.(IWSPA)(pp. 1–11).Tempe, AZ, USA.
   Google Scholar
• Ramanathan, V., & Wechsler, H. (2012). phishgillnet—phishing detection method- ology using probabilistic latent semantic analysis, adaboost, and co-training. EURASIP Journal on Information Security, 2012(1), 1–22. https://doi.org/https://doi.org/10.1186/1687-417X-2012-1
   Google Scholar
• Rani, S., & Kaur, B. (2015). Email classification using enhanced adaboost algorithm (Un-published doctoral dissertation). Lovely Professional University.
   Google Scholar
• Goodman, Rehfuss, P., Goodman, J., Rounthwaite, R., Mishra, M., Hulten, G., Richards, K., Averbuch, A., Penta, &A., Deyo, R., et al. (2006, June 8). Phishing detection, prevention, and notification. Google Patents. (US Patent App. 11/129,665)
   Google Scholar
• Robila, S. A., & Ragucci, J. W. (2006). Don’t be a phish: Steps in user education. In Acm sigcse bulletin (Vol. 38, pp. 237–241). ACM.
   Google Scholar
• Sender, I. (2011). accessed 21 july 2011. http://www.microsoft.com/mscorp/safety/technologies/senderid/default.mspx.
   Google Scholar
• Shams, R., & Mercer, R. E. (2016). Supervised classification of spam emails with natural language stylometry. Neural Computing & Applications, 27(8), 2315–2331. https://doi.org/https://doi.org/10.1007/s00521-015-2069-7
   Web of Science ®Google Scholar
• Singh, L. J., & Imphal, N. (2018). A survey on phishing and anti-phishing techniques. International Journal of Computer Science Trends and Technology (IJCST), 6(2), 62–68. http://www.ijcstjournal.org/volume-6/issue-2/IJCST-V6I2P13.pdf
   Google Scholar
• Smadi, S., Aslam, N., Zhang, L., Alasem, R., & Hossain, M. A. (2015). Detection of phishing emails using data mining algorithms. In 2015 9th international confer- ence on software, knowledge, information management and applications (skima) (pp. 1–8). IEEE: Kathmandu, Nepal.
   Google Scholar
• SpoofGuard. (2011). Spoofguard.http://crypto.stanford.edu/spoofguard/. Spoof- Guard,2011.
   Google Scholar
• Srinivasan, S., Ravi, V., Alazab, M., Ketha, S., Ala’M, A.-Z., Padannayil, S. K. (2021) Spam emails detection based on distributed word embedding with deep learning In\Machine intelligence and big data analytics for cybersecurity applications (pp. 161–189). Springer.
   Google Scholar
• Sullivan, D. (2005). The definitive guide to controlling malware, spyware, phishing, and spam. Realtimepublishers.com.
   Google Scholar
• Tchakounte´, F., Molengar, D., & Ngossaha, J. M. (2020). A description logic ontology for email phishing. International Journal of Information Security Science, 9(1), 44–63. https://www.ijiss.org/ijiss/index.php/ijiss/article/view/403/pdf_83
   Google Scholar
• Toolan, F., & Carthy, J. (2010). Feature selection for spam and phishing detection. In 2010 ecrime researchers summit (pp. 1–12). IEEE: Dallas, TX.
   Google Scholar
• Tseng, -S.-S., Chen, K.-Y., Lee, T.-J., & Weng, J.-F. (2011). Automatic content generation for anti-phishing education game. In 2011 international conference on electrical and control engineering (pp. 6390–6394). IEEE: Yichang, China.
   Google Scholar
• Unnithan, N. A., Harikrishnan, N., Akarsh, S., Vinayakumar, R., & Soman, K. (2018). Machine learning based phishing e-mail detection. Security-CEN@ Amrita, 65–69.
   Google Scholar
• Unnithan, N. A., Harikrishnan, N., Vinayakumar, R., Soman, K., & Sundarakrishna, S. (2018). Detecting phishing e-mail using machine learning techniques. In Proc. 1st anti-phishing shared task pilot 4th acm iwspa co-located 8th acm conf. data appl. secur. privacy (codaspy) (pp. 51–54). Tempe, AZ.
   Google Scholar
• Vazhayil, A., Harikrishnan, N., Vinayakumar, R., Soman, K., & Verma, A. (2018). Ped-ml: phishing email detection using classical machine learning techniques. In Proc. 1st antiphishing shared pilot 4th acm int. workshop secur. privacy anal.(iwspa) (pp. 1–8). Tempe, AZ.
   Google Scholar
• Verbeek, J. (2000 December). Supervised feature extraction for text categorization In Tenth Belgian-Dutch Conference on Machine Learning (Benelearn'00), Tilburg, Netherlands.
   Google Scholar
• Verma, R., Shashidhar, N., & Hossain, N. (2012). Detecting phishing emails the natural language way. In European symposium on research in computer security (pp. 824–841). Springer: Pisa, Italy.
   Google Scholar
• Vinayakumar, R., Soman, K.P, Prabaharan Poornachandran, Akarsh, S., & Elhoseny, M. (2019). Deep learning framework for cyber threat situational awareness based on email and url data analysis. In: Hassanien A., Elhoseny M. (eds) Cybersecurity and secure information systems. Advanced Sciences and Technologies for Security Applications. Springer, Cham. (pp. 87–124). Springer.https://doi.org/https://doi.org/10.1007/978-3-030-16837-7_6
   Google Scholar
• Vinayakumar, R., Soman, K., Poornachandran, P., Mohan, V. S., & Kumar, A. D. (2018). Scalenet: Scalable and hybrid framework for cyber threat situational awareness based on dns, url, and email data analysis. Journal of Cyber Security and Mobility, 8(2), 189–240. https://doi.org/https://doi.org/10.13052/jcsm2245-1439.823
   Google Scholar
• Xia, Y., & Wong, K.-F. (2006). Binarization approaches to email categorization. In International conference on computer processing of oriental languages (pp. 474–481). Springer: Singapore.
   Google Scholar
• Yasin, A., & Abuhasan, A. (2016). An intelligent classification model for phishing email detection. arXiv Preprint arXiv:1608.02196. International Journal of Network Security & Its Applications (IJNSA), 8(4), July 2016
   Google Scholar
• Youn, S., McLeod, D. (2007). A comparative study for email classification. In Elleithy K. (eds) Advances and innovations in systems, computing sciences and software engineering (pp. 387–391). Springer , Dordrecht. https://doi.org/https://doi.org/10.1007/978-1-4020-6264-3_67
   Google Scholar
• Zareapoor, M., & Seeja, K. (2015). Feature extraction or feature selection for text classification: A case study on phishing email detection. International Journal of Information Engineering and Electronic Business, 7(2), 60. https://doi.org/https://doi.org/10.5815/ijieeb.2015.02.08
   Google Scholar