Relevant bibliographies by topics / Email spam

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Email spam'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Email spam"

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Anandpara, Rahul. "Secured Mail Transformation System Using Machine Learnin." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1880–86. http://dx.doi.org/10.22214/ijraset.2021.36764.

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Today, Email Spam has become a major problem, with Rapid increament of internet users, Email spams is also increasing. People are using email spam for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can damage the system and can also seek in into your system. Spammer creates a fake profile and email account which is easier for them. These spammers target those peoples who are not aware about frauds. So there is a need to identify the fraud in terms of spam emails. In this paper we will identify the spam by using machine learning algorithms.
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Shaik Mohammed Fateen, Shaik Mohammad Asif, Shaik Afroz, N Mohammed Sami, and Dr. Tippanna. "Email Spam Detection Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 248–54. https://doi.org/10.32628/cseit2511315.

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Email Spam has become a major problem nowadays, with Rapid growth of internet users, Email spams is also increasing. People are using them for illegal and unethical conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and can also seek in into your system. Creating a fake profile and email account is much easy for the spammers, they pretend like a genuine person in their spam emails, these spammers target those peoples who are not aware about these frauds. So, it is needed to Identify those spam mails which are fraud, this project will identify tho
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Kachole, Abhinav, Aniket Nagpure, Atharva Wagh, and Prof T. H. Patil. "Email Platform: Spam Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1782–89. http://dx.doi.org/10.22214/ijraset.2024.60175.

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Abstract: In practically every industry today, from business to education, emails are used. Ham and spam are the two subcategories of emails. Email spam, often known as junk email or unwelcome email, is a kind of email that can be used to hurt any user by sapping their time and computing resources and stealing important data. Spam emailvolume is rising quickly day by day. Today’s email and IoT service providers face huge and massive challenges with spam identification and filtration. Email filteringis one of the most important and well-known methods among all the methods createdfor identifying
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Nivedha, M. A., and S. Raja. "Detection of Email Spam using Natural Language Processing Based Random Forest Approach." International Journal of Computer Science and Mobile Computing 11, no. 2 (2022): 7–22. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.002.

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An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those tempora
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Low, Choon Keat, and Xuan Ying Tan. "Artificial Intelligence-Based Email Spam Filtering." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 2 (2024): 67–75. https://doi.org/10.5281/zenodo.14264139.

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<em>Email spamming has become a big issue in spreading unsolicited emails. This project </em><em>focuses on tackling the prevalent issue of email spam through the implementation of machine learning techniques, particularly emphasizing spam filtering using artificial intelligence (AI). The goal is to develop an AI-powered application for efficient identification and filtration of spam emails. Key functionalities include email content preprocessing, feature extraction using techniques like Count Vectorization and TF-IDF Vectorization, and deploying machine learning models such as Support Vector
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Hemalatha, M., Sriharsha Katta, R. Sai Santosh, and Priyanka Priyanka. "E-MAIL SPAM DETECTION." International Journal of Computer Science and Mobile Computing 11, no. 1 (2022): 36–44. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.006.

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E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam
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Sisodiya, Harshvardhan. "SPAM EMAIL CLASSIFIER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33282.

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Today, a sizable portion of people rely on freely accessible email or communications provided by strangers. Because anyone may send an email or leave a note, spammers have an excellent opportunity to write spam messages regarding our various interests. Spam overflows email inboxes with absurd emails. severely reduces the speed of our internet. stealing vital information, such as our contact information, from us. Finding these spammers and the spam content can be difficult work and a popular research area. Spam email is the act of sending many messages via postal mail. Spam is effectively posta
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Dr., Jayamurugan. "AI Based Spam Detection Using Logistic Regression Algorithm." International Research Journal of Computer Science 10, no. 06 (2023): 307–10. http://dx.doi.org/10.26562/irjcs.2023.v1006.06.

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Nowadays, a big part of people caused by unsolicited bulk email messages commonly referred to as Spam. Email has now become one of the best ways for advertisements due to which spam emails are generated. Spam emails are rely on available email or messages sent by the stranger. The possibility that anybody can leave an email or a message provides a golden opportunity for spammers to write spam message about our different interests. Spam fills inbox with number of ridiculous emails. Degrades our internet speed to a great extent .Steals useful information like our details on our contact list. Ide
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Harjot, Kaur* Er. Prince Verma. "SURVEY ON E-MAIL SPAM DETECTION USING SUPERVISED APPROACH WITH FEATURE SELECTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 120–28. https://doi.org/10.5281/zenodo.496096.

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Electronic Mail (E-mail) has established a significant place in information user’s life. Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of mails have generated various serious issues too. Emails are categorized into ham and spam emails. From past decades spam emails are spreading at tremendous rate. These
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Dupade, Prof Rupali. "Spam Email Identification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30435.

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The increasing volume of spam emails poses a significant challenge to email users, demanding efficient and accurate methods for spam detection. the hybrid model leverages the ability of SVM to identify optimal hyperplanes for class separation and the ensemble learning capability of Random Forest to make robust predictions. Through a comprehensive evaluation using a labeled dataset, the proposed hybrid model is compared against individual SVM and random forest algorithms. Experimental results demonstrate that the hybridization approach achieves superior precision recall, accuracy and F1 Score f
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Dissertations / Theses on the topic "Email spam"

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Domeniconi, Andrea. "Rilevamento automatico di email illecite (Spam Filtering)." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Oltre il 45% del traffico email prodotto nel 2021 è costituito da spam. Si indica come spam qualisasi messaggio commerciale il cui invio non è sollecitato dal ricevente. Una grande parte di questi messaggi sono spediti in massa da venditori per promuovere i propri prodotti e sono considerati una seccatura dalla maggior parte delle persone per via del tempo necessario per rimuoverle, nonché per le risorse di archiviazione sprecate su server e dispositivi vari. Tuttavia le email di spam rappresentano una vera minaccia per aziende e utenti di qualsiasi tipo perché possono essere utilizzate come v
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Nachenahalli, Bhuthegowda Bharath Kumar. "Methods for Analyzing the Evolution of Email Spam." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24213.

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Email spam has steadily grown and has become a major problem for users, email service providers, and many other organizations. Many adversarial methods have been proposed to combat spam and various studies have been made on the evolution of email spam, by finding evolution patterns and trends based on historical spam data and by incorporating spam filters. In this thesis, we try to understand the evolution of email spam and how we can build better classifiers that will remain effective against adaptive adversaries like spammers. We compare various methods for analyzing the evolution of spam em
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Kaushik, Saket. "Policy-controlled email services." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2937.

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Thesis (Ph. D.)--George Mason University, 2007.<br>Title from PDF t.p. (viewed Jan. 18, 2008). Thesis directors: Paul Amman, Duminda Wijesekera. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 198. Includes bibliographical references (p. 189-197). Also available in print.
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Husna, Husain. "Models to Combat Email Spam Botnets and Unwanted Phone Calls." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc6095/.

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With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give
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Husna, Husain Dantu Ram. "Models to combat email spam botnets and unwanted phone calls." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-6095.

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Lohr, Frank. "TRIPOLI - Empowered Email Environment." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200400407.

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Workshop "Netz- und Service-Infrastrukturen" Der Vortrag im Rahmen des Workshop "Netz- und Service-Infrastrukturen" stellt das Projekt Tripoli vor, zeigt Ziele und Prinzipien der erweiterten Emailumgebung (Empowered Email Environment, Tripple-E) für authentifizierten und zertifizierten Emailverkehr, Ablauf der Kommunikation und zu lösende Probleme auf.
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Nussbaum, Ronald. "Graph-based email prioritization." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (M.S.)--Michigan State University. Computer Science and Engineering, 2008.<br>Title from PDF t.p. (viewed on July 29, 2009) Includes bibliographical references (p. 45-47). Also issued in print.
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Rideout, Ariel Lauren. "An email spam filtering proxy using secure authentication and micro-bonds." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33348.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (p. 71-74).<br>The Apuma system described in this thesis was designed and implemented as a novel combination of existing technologies in order to give an email user full control over their incoming email. The innate uncertainty of automatic spam detection creates a tension between the desire to filter 100% of spam, and the need to avoid the loss of legitimate mail. Apuma attempts to solve this problem by combining accept-lists with payment
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Deng, Ni. "A secure, payment-based email delivery system." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2909.

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Wu, Hao. "Detecting spam relays by SMTP traffic characteristics using an autonomous detection system." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/10926.

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Spam emails are flooding the Internet. Research to prevent spam is an ongoing concern. SMTP traffic was collected from different sources in real networks and analyzed to determine the difference regarding SMTP traffic characteristics of legitimate email clients, legitimate email servers and spam relays. It is found that SMTP traffic from legitimate sites and non-legitimate sites are different and could be distinguished from each other. Some methods, which are based on analyzing SMTP traffic characteristics, were purposed to identify spam relays in the network in this thesis. An autonomous comb
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Books on the topic "Email spam"

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Duntemann, Jeff. Degunking your email, spam, and viruses. Paraglyph Press, 2004.

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Duntemann, Jeff. Degunking your email, spam, and viruses. Paraglyph Press, 2004.

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Myles, White John, ed. Machine learning for email. O'Reilly Media, 2011.

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Poteet, Jeremy. Canning Spam: You've Got Mail (That You Don't Want). Pearson Education, 2005.

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United States. Federal Trade Commission. Division of Consumer and Business Education. Unsolicited mail, telemarketing, and email: Where to go to "just say no.". Federal Trade Commission, Bureau of Consumer Protection, Division of Consumer & Business Education, 2011.

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Lodder, Arno R. Spam, spammer--: Analyse van het recht en de techniek rond elektronische ongevraagde commerciële communicatie, in het bijzonder via email. Sdu Uitgevers, 2004.

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Churchill, Winton. Email marketing for complex sales cycles: Proven ways to produce a continuous flow of prospects and profits with effective, spam-free email systems. Morgan James, 2008.

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Neto, Amaro Moraes e. Silva. Emails indesejados à luz do direito. Editora Quartier Latin do Brasil, 2002.

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Spam and Scams: Using Email Safely. Rosen Publishing Group, 2013.

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Duntemann, Jeff. Degunking Your Email, Spam, and Viruses. Paraglyph, 2004.

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Book chapters on the topic "Email spam"

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Schwenk, Jörg. "Email: Protocols and SPAM." In Guide to Internet Cryptography. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19439-9_19.

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Yegenian, Aram, and Tassos Dimitriou. "Inexpensive Email Addresses An Email Spam-Combating System." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16161-2_3.

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Shivaji Ubale, Kalyani, and Kamini Ashutosh Shirsath. "Evaluation of Classification Algorithms for Effective Spam Email Detection using Spam Email Dataset." In Transformative Applied Research in Computing, Engineering, Science and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003616368-15.

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George, Princy, and P. Vinod. "Composite Email Features for Spam Identification." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8536-9_28.

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Ma, Wanli, Dat Tran, and Dharmendra Sharma. "Filtering Spam Email with Flexible Preprocessors." In Lecture Notes in Electrical Engineering. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74938-9_16.

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Scheffler, Sarah, Sean Smith, Yossi Gilad, and Sharon Goldberg. "The Unintended Consequences of Email Spam Prevention." In Passive and Active Measurement. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76481-8_12.

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Yang, Bo, Jianguo Jiang, and Ning Li. "Towards Discovering Covert Communication Through Email Spam." In Intelligent Information Processing VIII. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48390-0_20.

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Lakshmi, H. N., Ratnam Dodda, Sanjana Reddy Vemula, Gayathri Vangala, and Sansya Natemmal. "Email Guard: Enhancing Security Through Spam Detection." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3191-6_45.

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Demertzis, I., D. Froelicher, N. Luo, and M. Norberg Hovd. "i-SEAL2: Identifying Spam EmAiL with SEAL." In Protecting Privacy through Homomorphic Encryption. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77287-1_9.

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Cortez, Paulo, André Correia, Pedro Sousa, Miguel Rocha, and Miguel Rio. "Spam Email Filtering Using Network-Level Properties." In Advances in Data Mining. Applications and Theoretical Aspects. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14400-4_37.

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Conference papers on the topic "Email spam"

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Srivastava, Mayank, Dhruv Khandelwal, and Navya Swarup. "Email Spam Monitoring System." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626718.

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Zhang, Lehan, Xiaoyu Jiang, Qinyuan Zheng, Kai Guo, Dongqi Liu, and Shuai Xie. "Email Spam Classification Using LBSVM." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743627.

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Gond, Sudhir Prakash, Sudeept Singh Yadav, Md Arfat Alam, and Satyajit Sahoo. "Email Spam Detection Ensemble Methods." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986443.

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Sajwan, Manmohan Singh, Abhinav Singh Mahar, Priyanshu Rawat, Khushi Sharma, and Purushottam Das. "Email Spam Filteration with Machine Learning." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731098.

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R, Saravanan, and Vaisshale Rathinasamy. "Email Spam Detection with Machine Learning and Vectorization." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724545.

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El Bitar, Ibrahim, Nadine Abbas, Mohamad Raad, Farah Shmouri, and Fatima El Khechen. "Ensemble Learning-Based Approach for Email Spam Detection." In 2025 5th IEEE Middle East and North Africa Communications Conference (MENACOMM). IEEE, 2025. https://doi.org/10.1109/menacomm62946.2025.10911030.

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Al-Dmour, Nidal A., Shagufta Kousar, Areeba Khan, Anaum Ihsan, Tahir Abbas, and Ali Q. Saeed. "Enhancing Email Spam Detection Using Advanced AI Techniques." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836525.

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Sarker, Sumit Kanti, Ratul Bhattacharjee, Md Abu Sufian, et al. "Email Spam Detection Using Logistic Regression and Explainable AI." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013300.

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Jain, Vinod. "Intelligent Email Spam Detection: A Machine Learning-Based Approach." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988140.

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Singh, Juhi, Prithvi Chaurasia, and Yash Karnawat. "Comparative Analysis of ML Techniques for Email Spam Detection." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11019256.

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Reports on the topic "Email spam"

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Li, K., and B. Leiba. Email Feedback Report Type Value: not-spam. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6430.

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