Relevant bibliographies by topics / Spam-Mail

Contents

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

Academic literature on the topic 'Spam-Mail'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Spam-Mail"

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Nurlina, Nurlina, and Irmayana Irmayana. "Studi Banding Spam-Assassin Mail Server Dengan dan Tanpa Filter di Sisi Mail Client." Creative Information Technology Journal 1, no. 2 (2015): 77. http://dx.doi.org/10.24076/citec.2014v1i2.12.

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Filter spam yang disediakan oleh situs penyedia layanan email seperti yahoo, gmail, aim mail, windows live hotmail, dan masih banyak lagi yang lainnya, merupakan fasilitas yang sangat bermanfaat bagi para usernya. Filter spam tidak akan berfungsi sebagaimana yang diharapkan oleh user client. Pada saat alamat email para client sudah pernah di subscribe dengan tujuan tertentu seperti misalnya untuk registrasi mailing list, newsgroup, dan lain sebagainya, maka alamat emailnya itu sudah tidak aman lagi dari para spammer. Pada dasarnya para admin mail server hanya menggunakan secara langsung filter spam yang disediakan oleh mail server yang diinstal, tanpa memberikan penyettingan tertentu yang dibutuhkan client sama sekali. Para user sendiri yang seharusnya lebih aktif dalam menyaring spam pada email yang digunakan dengan banyak cara. Penelitian ini memanfaatkan aplikasi mail client Thunderbird untuk menyaring spam dengan metode Bayesian sebagai kelanjutan dari menyaring spam yang sudah tersaring sebelumnya pada sisi mail server dan bertujuan menganalisis hasil pengklasifikasian email ham dan email spam pada mail server dan mail client. Disimpulkan bahwa nilai akurasi dan error filter spam pada mail server berhubungan dengan filter Spam-Assassin yang tidak disetting dan dikonfigurasi oleh adminnya menunjukkan hasil yang tidak memuaskan dibandingkan dengan filter spam metode bayesian pada mail client yang sudah di-training.Spam filters provided by your email service provider websites such as yahoo, gmail, AIM mail, windows live hotmail, and many others, is a very powerful feature for the user. The spam filter will not work as expected by the client user. at the time of the email address of the client has been ever subscribe to a specific purpose such as for registration, mailing lists, newsgroups, and so forth, then the email address is no longer safe from spammers. Basically the admin mail server directly using only the spam filter provided by the mail server is installed, without giving a specific setting it takes the client at all. The users themselves are supposed to be more active in the spam filter on the email that is used in many ways. This study utilizes Thunderbird mail client application to filter spam with Bayesian methods as a continuation of the spam filter that has been previously filtered on the mail server and to analyze the results of the classification of ham and spam e-mail on the mail server and mail client. It was concluded that the accuracy and error spam filter on the mail server associated with the filter Spam-Assassin is not be set and configured by the admin showed unsatisfactory results compared with the Bayesian method to filter spam mail client that is already in-training
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Shirwadkar, Aryan, and Samuel Jacob. "Spam Mail Classifier." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 862–67. http://dx.doi.org/10.22214/ijraset.2022.48051.

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Abstract: Email is the worldwide use of communication application. It is because of the ease of use and faster than other communication application. However, its inability to detect whether the mail content is either spam or ham degrade its performance. Nowadays, lot of cases have been reported regarding stealing of personal information or phishing activities via email from the user. This project will discuss how machine learning help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. Binary classifierwill be used to classify the text into two different categories: spam and ham. The algorithm will predict the score more accurately. The objectiveof developing this model is to detect and score word faster and accurately.
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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 messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.
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Avijit, Mallik, Ahmad Sabbir, Arman Arefin Md., and Hosen Sarwar. "SPAM E-MAIL CHARACTERIZATION: AN EXPERIMENTAL PERFORMANCE COMPARISON OF MACHINE LEARNING." International Journal of Advanced Engineering and Science 6, no. 2 (2017): 44–51. https://doi.org/10.5281/zenodo.1016645.

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The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters. Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful data for all the machine learning calculations considered to detect spam e-mail.
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Upadhyay, Rohitkumar R. "E-Mail Spam Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1265–69. http://dx.doi.org/10.22214/ijraset.2021.39004.

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Abstract: E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches. Keywords: e-mail spam, unsolicited bulk email, spam filtering methods.
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Saito, Takamichi, Akio Morii, Tadashi Komori, Toshiyuki Kito, and Fumio Mizoguchi. "Anti-spam mail system." Systems and Computers in Japan 37, no. 13 (2006): 99–108. http://dx.doi.org/10.1002/scj.10627.

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Journal, IJSREM. "E-MAIL SPAM DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26878.

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Emails have become a ubiquitous means of personal and professional communication, often containing sensitive and confidential information. However, they are also prime targets for cybercriminals who employ techniques such as phishing to obtain private data. This paper proposes an intelligent and efficient email spam detection system that leverages data mining algorithms for classification and association. By extracting multiple features from email content, we improve classification accuracy and efficiency. The system integrates various machine learning algorithms and achieves a 30% reduction in the error rate compared to existing methods. Our approach enhances email spam detection by combining support vector machines with multi-feature extraction and classification. Key Words: Support vector machines, Email Spam, Phishing Detection, Machine Learning, Multi-Feature Extraction.
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Gong, Song Jie, and Xue Mei Zhang. "Design and Implementation of Intelligent Spam Filtering System." Advanced Materials Research 846-847 (November 2013): 1624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1624.

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With the rapid development of Internet, E-mail has been widely applied, and along goes a great deal of useless and harmful information. In the face of todays rampant spam developments, anti-spam mechanism is the mail filtering technology has gradually become the focus of information security. While the technical performance of spam filtering is good or bad, the key lies in the amount of spam sample collection, study and analysis. Through the analyzing and processing of spam, the paper designs and implements the intelligent spam filtering system. It brings forward some new theories. Based on analyzing actuality, origin and characteristic of spam, the paper also mainly expounds several filtering technique applied in E-mail.
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Fogel, Joshua, and Viju Raghupathi. "Spam E-mail Advertisements for Cosmetics / Beauty Products and Consumer Behavior." Journal of Business Theory and Practice 1, no. 1 (2013): 28. http://dx.doi.org/10.22158/jbtp.v1n1p28.

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Consumers receive spam e-mail solicitations for cosmetics and beauty products. We analyze responses<br />from 200 college students with regard to opening and reading this spam e-mail and also clicking<br />through and purchasing the product advertised in this spam e-mail. With regard to opening and reading<br />spam email about cosmetics/beauty products, women and also increasing scores for learning more<br />information online about cosmetics/beauty products were both significantly associated with increased<br />odds for opening and reading this spam e-mail. With regard to purchasing the cosmetics/beauty product<br />advertised in the spam e-mail, increasing scores for trust in the Internet to provide accurate<br />information about cosmetics/beauty products was significantly associated with increased odds for<br />purchasing. Marketers who use ethical approaches and are interested in sending e-mail information to<br />prospective college student customers about cosmetics/beauty products should keep in mind the<br />importance of conveying trust.
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Rose, Chris. "Finding A Recipe For Spam." Review of Business Information Systems (RBIS) 8, no. 2 (2004): 19–26. http://dx.doi.org/10.19030/rbis.v8i2.4494.

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The prevalence of unsolicited e-mail, otherwise called spam, continues to haunt every user of the Internet. The overwhelming response to the governments do-not-call registry in which persons could register their telephone numbers in a database that will restrict telemarketers from calling, is an indication that people are becoming increasingly resentful of unwanted intrusions into their personal lives. It is estimated that more than a half of all e-mail, or over one trillion pieces of spam will reach the inboxes of Internet users this year but the problems of controlling spam are many since:(a) spam is virtually free for the sender (b) the SMTP protocol which governs the transmission of e-mail on the Internet was not designed to handle the complexities of deception and mistrust on a large network and (c) many major corporations are surreptitiously involved in spam. Although the development of a social conscience might keep some large corporations from engaging in spam, but spam, as we know it, would cease to exist only if either the cost of sending e-mail increased or a new secure protocol to exchange e-mail was developed. Of the two options, the quickest and easiest remedy would be to eliminate the reverse economics of sending spam by introducing a computing cost for sending e-mail.
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Dissertations / Theses on the topic "Spam-Mail"

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Hassan, Tarek. "Towards eradication of SPAM: A study on intelligent adaptive SPAM filters." Thesis, Hassan, Tarek (2006) Towards eradication of SPAM: A study on intelligent adaptive SPAM filters. Masters by Research thesis, Murdoch University, 2006. https://researchrepository.murdoch.edu.au/id/eprint/67/.

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As the massive increase of electronic mail (email) usage continues, SPAM (unsolicited bulk email), has continued to grow because it is a very inexpensive method of advertising. These unwanted emails can cause a serious problem by filling up the email inbox and thereby leaving no space for legitimate emails to pass through. Currently the only defense against SPAM is the use of SPAM filters. A novel SPAM filter GetEmail5 along with the design rationale, is described in this thesis. To test the efficacy of GetEmail5 SPAM filter, an experimental setup was created and a commercial bulk email program was used to send SPAM and non-SPAM emails to test the new SPAM filter. GetEmail5's efficiency and ability to detect SPAM was compared against two highly ranked commercial SPAM filters on different sets of emails, these included all SPAM, non-SPAM, and mixed emails, also text and HTML emails. The results showed the superiority of GetEmail5 compared to the two commercial SPAM filters in detecting SPAM emails and reducing the user's involvement in categorizing the incoming emails. This thesis demonstrates the design rationale for GetEmail5 and also its greater effectiveness in comparison with the commercial SPAM filters tested.
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Hassan, Tarek. "Towards eradication of SPAM : a study on intelligent adaptive SPAM filters /." Hassan, Tarek (2006) Towards eradication of SPAM: a study on intelligent adaptive SPAM filters. Masters by Research thesis, Murdoch University, 2006. http://researchrepository.murdoch.edu.au/67/.

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As the massive increase of electronic mail (email) usage continues, SPAM (unsolicited bulk email), has continued to grow because it is a very inexpensive method of advertising. These unwanted emails can cause a serious problem by filling up the email inbox and thereby leaving no space for legitimate emails to pass through. Currently the only defense against SPAM is the use of SPAM filters. A novel SPAM filter GetEmail5 along with the design rationale, is described in this thesis. To test the efficacy of GetEmail5 SPAM filter, an experimental setup was created and a commercial bulk email program was used to send SPAM and non-SPAM emails to test the new SPAM filter. GetEmail5's efficiency and ability to detect SPAM was compared against two highly ranked commercial SPAM filters on different sets of emails, these included all SPAM, non-SPAM, and mixed emails, also text and HTML emails. The results showed the superiority of GetEmail5 compared to the two commercial SPAM filters in detecting SPAM emails and reducing the user's involvement in categorizing the incoming emails. This thesis demonstrates the design rationale for GetEmail5 and also its greater effectiveness in comparison with the commercial SPAM filters tested.
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Wagner, Alexander. "Unerwünschte E-Mail-Werbung /." Wien : WUV-Univ.-Verl, 2003. http://www.gbv.de/dms/zbw/366320793.pdf.

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Cheung, Pak-to Patrick. "A study on combating the problem of unsolicited electronic messages in Hong Kong." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38608248.

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Tobler, Simon P. "Exploring decentralized collaborative filtering against spam mail." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, Institute of Pervasive Computing, Information and Communication Systems Research Group, 2008. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=398.

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Richter, Frank. "Neue Anti-Spam-Techniken." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200400379.

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Workshop "Netz- und Service-Infrastrukturen" Dieser Beitrag zum Workshop "Netz- und Service-Infrastrukturen" 2004 analysiert den Stand der Anti-Spam-Maßnahmen an der TU Chemnitz und zeigt neue Techniken auf.
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Eggendorfer, Tobias. "Methoden der Spambekämpfung und -vermeidung /." Norderstedt : Books on Demand, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016357555&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Ramachandran, Anirudh Vadakkedath. "Mitigating spam using network-level features." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41068.

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Spam is an increasing menace in email: 90% of email is spam, and over 90% of spam is sent by botnets---networks of compromised computers under the control of miscreants. In this dissertation, we introduce email spam filtering using network-level features of spammers. Network-level features are based on lightweight measurements that can be made in the network, often without processing or storing a message. These features stay relevant for longer periods, are harder for criminals to alter at will (e.g., a bot cannot act independently of other bots in the botnet), and afford the unique opportunity to observe the coordinated behavior of spammers. We find that widely-used IP address-based reputation systems (e.g., IP blacklists) cannot keep up with the threats of spam from previously unseen IP addresses, and from new and stealthy attacks---to thwart IP-based reputation systems, spammers are reconnoitering IP Blacklists and sending spam from hijacked IP address space. Finally, spammers are "gaming" collaborative filtering by users in Web-based email by casting fraudulent "Not Spam" votes on spam email. We present three systems that detect each attack that uses spammer behavior rather than their IP address. First, we present IP blacklist counter-intelligence, a system that can passively enumerate spammers performing IP blacklist reconnaissance. Second, we present SpamTracker, a system that distinguishes spammers from legitimate senders by applying clustering on the set of domains to which email is sent. Third, we analyze vote-gaming attacks in large Web-based email systems that pollutes user feedback on spam emails, and present an efficient clustering-based method to mitigate such attacks.
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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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Frank, Thomas. "Zur strafrechtlichen Bewältigung des Spamming /." Berlin : Logos-Verl, 2004. http://www.gbv.de/dms/spk/sbb/recht/toc/379099837.pdf.

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Books on the topic "Spam-Mail"

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Lukasiak, Janusz. Blocking spam relaying and junk mail. Joint Information Systems Committee, 1999.

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Wehr, Hendric. Nie wieder Spam!: Kampf den Werbemails. Markt und Technik, 2003.

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Land, Jonathan. The spam letters. No Starch Press, 2004.

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Kepczyk, Roman H. A CPA's guide to understanding and controlling spam. American Institute of Certified Public Accountants, 2004.

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New Zealand. Ministry of Economic Development. IT & Telecommunications Policy Group. Legislating against spam: Discussion paper. IT & Telecommunications Policy Group, Resources and Networks Branch, Ministry of Economic Development, 2004.

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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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Costa, F. Bruto da. Spam e mail-bomb: Subsídios para uma perspectiva criminal. Quid Juris, 2005.

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

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Canada. Task Force on Spam. Stopping spam: Creating a stronger, safer Internet : executive summary and recommendations. Task Force on Spam, 2005.

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

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Book chapters on the topic "Spam-Mail"

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Schrödel, Tobias. "E-Mail und SPAM." In Hacking für Manager. Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7128-9_7.

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Schrödel, Tobias. "E-Mail und SPAM." In Hacking für Manager. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6475-5_7.

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Schrödel, Tobias. "E-Mail & Spam." In Ich glaube, es hackt! Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04246-2_9.

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Schrödel, Tobias. "E-Mail & Spam." In Ich glaube, es hackt! Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-10858-8_9.

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Dong, Cailing, and Bin Zhou. "Spam Detection, E-mail/Social Network." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_294-1.

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Fiumara, Giacomo, Massimo Marchi, Rosamaria Pagano, and Alessandro Provetti. "Rule-Based Spam E-mail Annotation." In Web Reasoning and Rule Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15918-3_21.

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Dong, Cailing, and Bin Zhou. "Spam Detection, E-mail/Social Network." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_294.

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Dong, Cailing, and Bin Zhou. "Spam Detection: E-mail/Social Network." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_294.

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Kang, Sin-Jae, Sae-Bom Lee, Jong-Wan Kim, and In-Gil Nam. "Two Phase Approach for Spam-Mail Filtering." In Computational and Information Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30497-5_124.

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Kim, Hyun-Jun, Heung-Nam Kim, Jason J. Jung, and Geun-Sik Jo. "Spam Mail Filtering System Using Semantic Enrichment." In Web Information Systems – WISE 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30480-7_64.

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Conference papers on the topic "Spam-Mail"

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Erkuş, Celal, and Buket Kaya. "E-Mail Spam Detection Using BERT and LSTM." In 2024 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2024. https://doi.org/10.1109/dasa63652.2024.10836404.

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Huang, Yingchun, and Sen Xing. "Spam E-mail Recognition Method Based on Neural Network Model." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691480.

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Pavlinov, D. V., S. E. Ikonnikov, A. E. Ermakova, and D. A. Antonov. "MODERNIZATION OF THE CORPORATE MAIL SERVER PROTECTION SYSTEM." In Intelligent transport systems. Russian University of Transport, 2024. http://dx.doi.org/10.30932/9785002446094-2024-720-725.

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The article considers the task of building a system to protect a corporate mail server from spam through the use of access control lists and a contextual filter. When working with the Exim mail server, the introduction of a set of different anti-spam methods has achieved a significant reduction in spam messages reaching the user.
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Takemura, Toshihiko, and Hiroyuki Ebara. "Spam Mail Reduces Economic Effects." In Second International Conference on the Digital Society. IEEE, 2008. http://dx.doi.org/10.1109/icds.2008.15.

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Bhuleskar, Ronald, Anoop Sherlekar, and Anala Pandit. "Hybrid Spam E-mail Filtering." In 2009 First International Conference on Computational Intelligence, Communication Systems and Networks (CICSYN). IEEE, 2009. http://dx.doi.org/10.1109/cicsyn.2009.34.

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Liu, Pingchuan, and Teng-Sheng Moh. "Content Based Spam E-mail Filtering." In 2016 International Conference on Collaboration Technologies and Systems (CTS). IEEE, 2016. http://dx.doi.org/10.1109/cts.2016.0052.

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Olivo, Cleber K., Altair O. Santin, and Luiz E. S. Oliveira. "Using Huffman Trees in Features Selection to Enhance Performance in Spam Detection." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2017. http://dx.doi.org/10.5753/sbseg.2017.19506.

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Spam detection is very costly when compared to the simple task of spreading spam. Most approaches aim to reach higher accuracy percentages, leaving the classification performance in background, what may cause many problems, such as bottlenecks in the e-mail system, huge infrastructure investments and waste of resources pooling. To avoid these problems, this paper proposes a hierarchical spam features organization using Huffman Trees, where the most important features stay closer to the root. With the reduction of these trees (leaves pruning) the feature space is significantly reduced, speeding up the e-mail classification process. The experiments showed a performance 60 times faster when compared to Spam Assassin.
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Lin, Lian, Zhongwen Li, and Liang Shi. "Spam Mail Filtering Based on Network Processor." In 2008 IFIP International Conference on Network and Parallel Computing (NPC). IEEE, 2008. http://dx.doi.org/10.1109/npc.2008.50.

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9

Choudhari, Sudeep, and Suman Das. "Spam E-mail Identification Using Blockchain Technology." In 2021 International Conference on Communication, Control and Information Sciences (ICCISc). IEEE, 2021. http://dx.doi.org/10.1109/iccisc52257.2021.9485018.

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Shrivastava, Shubhi, and R. Anju. "Spam mail detection through data mining techniques." In 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT). IEEE, 2017. http://dx.doi.org/10.1109/intelcct.2017.8324021.

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