Abstract
Emails have become an integral part of our lives; be it personal or professional, emails are used everywhere. Most websites require the customers to enter their email ID, increasing the risk of exposure to spammers who attack them by sending spam messages. To overcome this problem, researchers have come up with different techniques to classify emails as legitimate or spam mails. The conducted literature survey includes 32 relevant papers out of the 47 selected papers. The paper is based on a comprehensive six research question-based approach which has been implemented to find the Machine Learning (ML) and Deep Learning (DL) techniques used, datasets used, pre-processing methods implemented, spam types, different evaluation metrics used and the strengths and weaknesses of the models in the literature. The domains analyzed in ML and DL are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), Random Forest, Neural Networks (NN), etc. This review will help the researchers in identifying the present and the future context of research in the different approaches for the classification of spam emails.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tidy J (17 April 2020) Google blocking 18m coronavirus scam emails every day. https://www.bbc.com/news/technology-52319093
Keyworth M, Wall M (8 Jan 2016) The ‘bogus boss’ email scam costing firms millions. https://www.bbc.com/news/business-35250678
Budanović N (11 Dec 2019) 20 spam statistics that shed light on the dark side of your inbox. https://dataprot.net/statistics/spam-statistics
Fang Y, Zhang C, Huang C, Liu L, Yang Y (2019) Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism. 2019 IEEE Access 7, 56329–56340
Olatunji SO (2017) Extreme learning machines and support vector machines models for email spam detection. In: 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE). Windsor, ON, pp 1–6
Tseng C, Chen M (2009) Incremental SVM model for spam detection on dynamic email social networks. In: 2009 international conference on computational science and engineering. Vancouver, BC, pp 128–135
Vyas T, Prajapati P, Gadhwal S (2015) A survey and evaluation of supervised machine learning techniques for spam e-mail filtering. In: 2015 IEEE international conference on electrical, computer and communication technologies (ICECCT). Coimbatore, pp 1–7
More S, Kulkarni SA (2013) Data mining with machine learning applied for email deception. In: 2013 international conference on optical imaging sensor and security (ICOSS). Coimbatore, pp 1–4
Nandhini S, Marseline DJKS (2020) Performance evaluation of machine learning algorithms for email spam detection. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE). Vellore, India, pp 1–4
Deshmukh P, Shelar M, Kulkarni N (2014) Detecting of targeted malicious email. In: 2014 IEEE global conference on wireless computing & networking (GCWCN). Lonavala, pp 199–202
Li Q, Cheng M, Wang J, Sun B (2020) LSTM based phishing detection for big email data. IEEE Trans Big Data 2020:1–1
Li X, Zhang D, Wu B (2020) Detection method of phishing email based on persuasion principle. In: 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC). Chongqing, China, pp 571–574
Wijaya A, Bisri A (2016) Hybrid decision tree and logistic regression classifier for email spam detection. In: 2016 8th international conference on information technology and electrical engineering (ICITEE). Yogyakarta, pp 1–4
Agarwal K, Kumar T (2018) Email spam detection using integrated approach of naïve bayes and particle swarm optimization. In: 2018 second international conference on intelligent computing and control systems (ICICCS). Madurai, India, pp 685–690
Peng W, Huang L, Jia J, Ingram E (2018) Enhancing the naive bayes spam filter through intelligent text modification detection. In: 2018 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE). New York, NY, pp 849–854
Singh AK, Bhushan S, Vij S (2019) Filtering spam messages and mails using fuzzy C means algorithm. In: 2019 4th international conference on internet of things: smart innovation and usages (IoT-SIU). Ghaziabad, India, pp 1–5
Moradpoor N, Clavie B, Buchanan B (2017) Employing machine learning techniques for detection and classification of phishing emails. In: 2017 computing conference. London, pp 149–156
Panigrahi PK (2012) A comparative study of supervised machine learning techniques for spam e-mail filtering. In: 2012 fourth international conference on computational intelligence and communication networks. Mathura, pp 506–512
Nizamani S, Memon N, Glasdam M, Nguyen DD (2014) Detection of fraudulent emails by employing advanced feature abundance, pp 169–174
Unnithan NA, Harikrishnan NB, Vinayakumar R, Soman KP, Sundarakrishna S (2018) Detecting phishing E-mail using machine learning techniques CEN-secureNLP. In: 2018 proceedings of the 1st anti-phishing shared task pilot 4th ACM IWSPA Co-Located 8th ACM conference data application security privacy (CODASPY), pp 51–54
Alotaibi R, Al-Turaiki I, Alakeel F (2020) Mitigating email phishing attacks using convolutional neural networks. In: 2020 3rd international conference on computer applications & information security (ICCAIS). Riyadh, Saudi Arabia, pp 1–6
Habib M, Faris H, Hassonah MA, Alqatawna J, Sheta AF, Al-Zoubi AM (2019) Automatic email spam detection using genetic programming with SMOTE. In: 2018 fifth HCT information technology trends (ITT). Dubai, United Arab Emirates, pp 185–190
Yu G, Fan W, Huang W, An J (2020) An explainable method of phishing emails generation and its application in machine learning. In: 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC). Chongqing, China, pp 1279–1283
Chen R, Zhang C, Guo J, Wang X (2019) Application of naive bayesian algorithms in E-mail classification. In: 2019 Chinese automation congress (CAC). Hangzhou, China, pp 3933–3938
Prilepok M, Kudelka M (2015) Spam detection based on nearest community classifier. In: 2015 international conference on intelligent networking and collaborative systems. Taipei, pp 354–359
Bagui S, Nandi D, Bagui S, White RJ (2019) Classifying phishing email using machine learning and deep learning. In: 2019 international conference on cyber security and protection of digital services (cyber security). Oxford, United Kingdom, pp 1–2
Alurkar AA, et al (2017) A proposed data science approach for email spam classification using machine learning techniques. In: 2017 internet of things business models, users, and networks. Copenhagen, pp 1–5
Niu W, Zhang X, Yang G, Ma Z, Zhuo Z (2017) Phishing emails detection using CS-SVM. In: 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC). Guangzhou, pp 1054–1059
Zaid A, Alqatawna J, Huneiti A (2016) A proposed model for malicious spam detection in email systems of educational institutes. In: 2016 cybersecurity and cyberforensics conference (CCC). Amman, pp 60–64
Bin S, Razak A, Bin Mohamad AF (2013) Identification of spam email based on information from email header. In: 2013 13th international conference on intellient systems design and applications. Bangi, pp 347–353
Sanchez F, Duan Z (2012) A sender-centric approach to detecting phishing emails. In:2012 international conference on cyber security. Washington, DC, pp 32–39
Azeez NA, Oluwatosin A (2016) CyberProtector: identifying compromised URLs in electronic mails with bayesian classification. In: 2016 international conference on computational science and computational intelligence (CSCI). Las Vegas, NV, pp 959–965
Hajgude J, Ragha L (2012) Phish mail guard: phishing mail detection technique by using textual and URL analysis. In: 2012 world congress on information and communication technologies. Trivandrum, pp 297–302
Vishagini V, Rajan AK (2018) An improved spam detection method with weighted support vector machine. In: 2018 international conference on data science and engineering (ICDSE). Kochi, pp 1–5
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Soneji, H.N., Soman, A.S., Vyas, A., Puthran, S. (2022). A Comprehensive Review of Fraudulent Email Detection Models. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-6890-6_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6889-0
Online ISBN: 978-981-16-6890-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)