Relevant bibliographies by topics / Bayesian spam filtering

Contents

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

Academic literature on the topic 'Bayesian spam filtering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Bayesian spam filtering"

1

Jiang, Xue, and Jun Kai Yi. "Improved Bayesian-Based Spam Filtering Approach." Applied Mechanics and Materials 401-403 (September 2013): 1885–91. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1885.

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Bayesian filtering approach is widely used in the field of anti-spam now. However, the two assumptions of this algorithm are significantly different with the actual situation so as to reduce the accuracy of the algorithm. This paper proposes a detailed improvement on researching of Bayesian Filtering Algorithm principle and implement method. It changes the priori probability of spam from constant figure to the actual probability, improves selection and selection rules of the token, and also adds URL and pictures to the detection content. Finally it designs a spam filter based on improved Bayesian filter approach. The experimental result of this improved Bayesian Filter approach indicates that it has a beneficial effect in the spam filter application.
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Al-Alwani, Abdulkareem, and Majdi Beseiso. "Arabic Spam filtering using Bayesian Model." International Journal of Computer Applications 79, no. 7 (October 18, 2013): 11–14. http://dx.doi.org/10.5120/13752-1582.

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Zhang, Zhiying. "A Bayesian Topic Model for Spam Filtering." Journal of Information and Computational Science 10, no. 12 (August 10, 2013): 3719–27. http://dx.doi.org/10.12733/jics20102279.

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Srivastava, Anushka. "Junk Filtering through Naive Bayesian Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 1999–2004. http://dx.doi.org/10.22214/ijraset.2021.36801.

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As the world is seamlessly developing at a very high pace, we have been seeing enormous growth in various sectors of Technology. Networking has played a crucial part in the exchange of technological culture around the globe, and the Internet being the sole medium of Network enhancement has taken over every aspect of our society. Today, most of the professional communications are done through emailing. As far as email has proven to be an efficient, professional and easy way of communication, it also comes with the disadvantage of unwanted bulk bombarding of spam content. This has been a critical concern for email users. Consequently, it has become very difficult for spam filters to efficiently filter the unwanted emails, since nowadays emails are written in such a manner that any existing algorithm cannot give 100% accuracy in predicting spam. This paper deals with Naive Bayesian Classifier that is a Machine Learning algorithm for antispam filtering, which gives satisfactory results by automatically constructing anti-spam filters with extended conduct. The review over the researched performance of Naive Bayes algorithm is done by the investigations of Spam ham csv datasets. The performance of the algorithm is evaluated based on the accuracy, recall and precision it shows on the mentioned datasets. This technique gives 96-97% accuracy and 89% precision on the investigated dataset. The result also highlights that the content of the email and the number of instances of the dataset has an apparent effect on the performance of the algorithm.
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Krishna, Mr B. "E-Mail Spam Classification using Naive Bayesian Classifier." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5209–14. http://dx.doi.org/10.22214/ijraset.2021.36153.

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— E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it’s an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naïve Bayesian Classifier for spam classification. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the Lingspam dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature. The result is to increase the accuracy of the system.
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Liu, Xin, Pingjun Zou, Weishan Zhang, Jiehan Zhou, Changying Dai, Feng Wang, and Xiaomiao Zhang. "CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing." Wireless Communications and Mobile Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1457870.

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Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.
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Kim, Bum-Bae, and Hyoung-Kee Choi. "Spam Message Filtering with Bayesian Approach for Internet Communities." KIPS Transactions:PartC 13C, no. 6 (October 30, 2006): 733–40. http://dx.doi.org/10.3745/kipstc.2006.13c.6.733.

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赵, 彩迪. "Research on Naive Bayesian Spam SMS Filtering Based on MapReduce." Computer Science and Application 06, no. 07 (2016): 443–50. http://dx.doi.org/10.12677/csa.2016.67054.

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Haseeb, Saima, Mahak Motwani, and Amit Saxena. "Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC." International Journal of Computer Applications 74, no. 17 (July 26, 2013): 9–14. http://dx.doi.org/10.5120/12975-9567.

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Priyanka, T. "Bayesian Decision Framework for an Efficient Spam Filtering in Social Network." International Journal of Computer & Organization Trends 7, no. 1 (April 25, 2014): 20–24. http://dx.doi.org/10.14445/22492593/ijcot-v7p304.

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Dissertations / Theses on the topic "Bayesian spam filtering"

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Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.

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This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dissertation critically discusses the advantages of having using the three tier model to combat spamming Botnets.
Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
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Trevino, Alberto. "Improving Filtering of Email Phishing Attacks by Using Three-Way Text Classifiers." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3103.

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The Internet has been plagued with endless spam for over 15 years. However, in the last five years spam has morphed from an annoying advertising tool to a social engineering attack vector. Much of today's unwanted email tries to deceive users into replying with passwords, bank account information, or to visit malicious sites which steal login credentials and spread malware. These email-based attacks are known as phishing attacks. Much has been published about these attacks which try to appear real not only to users and subsequently, spam filters. Several sources indicate traditional content filters have a hard time detecting phishing attacks because the emails lack the traditional features and characteristics of spam messages. This thesis tests the hypothesis that by separating the messages into three categories (ham, spam and phish) content filters will yield better filtering performance. Even though experimentation showed three-way classification did not improve performance, several additional premises were tested, including the validity of the claim that phishing emails are too much like legitimate emails and the ability of Naive Bayes classifiers to properly classify emails.
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Lin, Chia-shyang, and 林嘉翔. "An Enhanced Naïve Bayesian Classifier on Spam Filtering." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26244185086550229633.

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碩士
國立雲林科技大學
資訊管理系碩士班
93
Spam problem has been viewed as a serious threat to the Internet, flooding users’ inboxes and costing businesses billions of dollars through the waste of bandwidth, storage, and office work forces. To the worse and worse spam problem, several studies have been made, ranging from technical to regulatory. Naïve Bayes classifier is a widely used classifier in text categorization task. It also enjoys a blaze of popularity in anti-spam researchers. In this study, we analysis the Naïve Bayes classifier and the modification of Robinson (2003), then proposed three ways of enhancement. The experiment result shows that two of the proposed methods have better performance in most cases than traditional Naïve Bayes model while holding good detection rate and eliminating the false positive problem.
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Yi, Te-Ming, and 易德銘. "Study of Spam Mail Filtering Technique Band on Bayesian Classification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/u7d3q2.

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碩士
國立高雄第一科技大學
電腦與通訊工程所
95
In the advanced information technology life today, we all have enjoyed much of the benefit brought forth from technology, at the same time, we have, as well, been exposed to much of its harm, primarily the problem of spam mail. I believe a great many people would feel as much as I do. Among several of the spam mail detection methods, the measure by content filtering is considered to be one of the most popular one, while the use of technique Bayes algorithm is found to be most often among all. This study would make use of Naïve Bayes algorithm to design a two-tiered filtering mechanism to filter out spam mail in steps. This study has first established various types of learning samplings, which are respectively as advertisement mail, pornographic mail, and regular mail. Then, different combinations upon these samplings are found, working in conjunction with advance probability adjustment and designation of threshold value so as to filter out a portion of the spam mail in the first stage, and the rest of the others in the second stage. As of such, the objective to filter spam mail can be achieved.
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Books on the topic "Bayesian spam filtering"

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Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification. No Starch Press, 2005.

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Book chapters on the topic "Bayesian spam filtering"

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Wang, Hongling, Gang Zheng, and Yueshun He. "The Improved Bayesian Algorithm to Spam Filtering." In Proceedings of the 4th International Conference on Computer Engineering and Networks, 37–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_5.

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Wrótniak, Karol, and Michał Woźniak. "Combined Bayesian Classifiers Applied to Spam Filtering Problem." In Advances in Intelligent Systems and Computing, 253–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33018-6_26.

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Ezpeleta, Enaitz, Urko Zurutuza, and José María Gómez Hidalgo. "Does Sentiment Analysis Help in Bayesian Spam Filtering?" In Lecture Notes in Computer Science, 79–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32034-2_7.

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Iwanaga, Manabu, Toshihiro Tabata, and Kouichi Sakurai. "Some Fitting of Naive Bayesian Spam Filtering for Japanese Environment." In Information Security Applications, 135–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31815-6_12.

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Shrestha, Raju, and Yaping Lin. "Improved Bayesian Spam Filtering Based on Co-weighted Multi-area Information." In Advances in Knowledge Discovery and Data Mining, 650–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_75.

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Kim, Hyun-Jun, Jenu Shrestha, Heung-Nam Kim, and Geun-Sik Jo. "User Action Based Adaptive Learning with Weighted Bayesian Classification for Filtering Spam Mail." In Lecture Notes in Computer Science, 790–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_83.

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Mallik, Ritik, and Abhaya Kumar Sahoo. "A Novel Approach to Spam Filtering Using Semantic Based Naive Bayesian Classifier in Text Analytics." In Advances in Intelligent Systems and Computing, 301–9. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_27.

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Hamou, Reda Mohamed, and Abdelmalek Amine. "Using Data Mining Techniques and the Choice of Mode of Text Representation for Improving the Detection and Filtering of Spam." In Advances in Business Information Systems and Analytics, 300–319. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch018.

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This chapter studies a boosting algorithm based, first, on Bayesian filters that work by establishing a correlation between the presence of certain elements in a message and the fact that they appear in general unsolicited messages (spam) or in legitimate email (ham) to calculate the probability that the message is spam and, second, on an unsupervised learning algorithm: in this case the K-means. A probabilistic technique is used to weight the terms of the matrix term-category, and K-means are used to filter the two classes (spam and ham). To determine the sensitive parameters that improve the classifications, the authors study the content of the messages by using a representation of messages by the n-gram words and characters independent of languages to later decide what representation ought to get a good classification. The work was validated by several validation measures based on recall and precision.
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Whitworth, Brian. "Spam as a Symptom of Electronic Communication Technologies that Ignore Social Requirements." In Encyclopedia of Human Computer Interaction, 559–66. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-562-7.ch083.

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Spam, undesired and usually unsolicited e-mail, has been a growing problem for some time. A 2003 Sunbelt Software poll found spam (or junk mail) has surpassed viruses as the number-one unwanted network intrusion (Townsend & Taphouse, 2003). Time magazine reports that for major e-mail providers, 40 to 70% of all incoming mail is deleted at the server (Taylor, 2003), and AOL reports that 80% of its inbound e-mail, 1.5 to 1.9 billion messages a day, is spam the company blocks. Spam is the e-mail consumer’s number-one complaint (Davidson, 2003). Despite Internet service provider (ISP) filtering, up to 30% of in-box messages are spam. While each of us may only take seconds (or minutes) to deal with such mail, over billions of cases the losses are significant. A Ferris Research report estimates spam 2003 costs for U.S. companies at $10 billion (Bekker, 2003). While improved filters send more spam to trash cans, ever more spam is sent, consuming an increasing proportion of network resources. Users shielded behind spam filters may notice little change, but the Internet transmitted-spam percentage has been steadily growing. It was 8% in 2001, grew from 20% to 40% in 6 months over 2002 to 2003, and continues to grow (Weiss, 2003). In May 2003, the amount of spam e-mail exceeded nonspam for the first time, that is, over 50% of transmitted e-mail is now spam (Vaughan-Nichols, 2003). Informal estimates for 2004 are over 60%, with some as high as 80%. In practical terms, an ISP needing one server for customers must buy another just for spam almost no one reads. This cost passes on to users in increased connection fees. Pretransmission filtering could reduce this waste, but creates another problem: spam false positives, that is, valid e-mail filtered as spam. If you accidentally use spam words, like enlarge, your e-mail may be filtered. Currently, receivers can recover false rejects from their spam filter’s quarantine area, but filtering before transmission means the message never arrives at all, so neither sender nor receiver knows there is an error. Imagine if the postal mail system shredded unwanted mail and lost mail in the process. People could lose confidence that the mail will get through. If a communication environment cannot be trusted, confidence in it can collapse. Electronic communication systems sit on the horns of a dilemma. Reducing spam increases delivery failure rate, while guaranteeing delivery increases spam rates. Either way, by social failure of confidence or technical failure of capability, spam threatens the transmission system itself (Weinstein, 2003). As the percentage of transmitted spam increases, both problems increase. If spam were 99% of sent mail, a small false-positive percentage becomes a much higher percentage of valid e-mail that failed. The growing spam problem is recognized ambivalently by IT writers who espouse new Bayesian spam filters but note, “The problem with spam is that it is almost impossible to define” (Vaughan-Nichols, 2003, p. 142), or who advocate legal solutions but say none have worked so far. The technical community seems to be in a state of denial regarding spam. Despite some successes, transmitted spam is increasing. Moral outrage, spam blockers, spamming the spammers, black and white lists, and legal responses have slowed but not stopped it. Spam blockers, by hiding the problem from users, may be making it worse, as a Band-Aid covers but does not cure a systemic sore. Asking for a technical tool to stop spam may be asking the wrong question. If spam is a social problem, it may require a social solution, which in cyberspace means technical support for social requirements (Whitworth & Whitworth, 2004).
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Whitworth, Brian. "Spam as a Symptom of Electronic Communication Technologies that Ignore Social Requirements." In E-Collaboration, 1464–73. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-652-5.ch107.

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Spam, undesired and usually unsolicited e-mail, has been a growing problem for some time. A 2003 Sunbelt Software poll found spam (or junk mail) has surpassed viruses as the number-one unwanted network intrusion (Townsend & Taphouse, 2003). Time magazine reports that for major e-mail providers, 40 to 70% of all incoming mail is deleted at the server (Taylor, 2003), and AOL reports that 80% of its inbound e-mail, 1.5 to 1.9 billion messages a day, is spam the company blocks. Spam is the e-mail consumer’s number-one complaint (Davidson, 2003). Despite Internet service provider (ISP) filtering, up to 30% of in-box messages are spam. While each of us may only take seconds (or minutes) to deal with such mail, over billions of cases the losses are significant. A Ferris Research report estimates spam 2003 costs for U.S. companies at $10 billion (Bekker, 2003). While improved filters send more spam to trash cans, ever more spam is sent, consuming an increasing proportion of network resources. Users shielded behind spam filters may notice little change, but the Internet transmitted-spam percentage has been steadily growing. It was 8% in 2001, grew from 20% to 40% in 6 months over 2002 to 2003, and continues to grow (Weiss, 2003). In May 2003, the amount of spam e-mail exceeded nonspam for the first time, that is, over 50% of transmitted e-mail is now spam (Vaughan-Nichols, 2003). Informal estimates for 2004 are over 60%, with some as high as 80%. In practical terms, an ISP needing one server for customers must buy another just for spam almost no one reads. This cost passes on to users in increased connection fees. Pretransmission filtering could reduce this waste, but creates another problem: spam false positives, that is, valid e-mail filtered as spam. If you accidentally use spam words, like enlarge, your e-mail may be filtered. Currently, receivers can recover false rejects from their spam filter’s quarantine area, but filtering before transmission means the message never arrives at all, so neither sender nor receiver knows there is an error. Imagine if the postal mail system shredded unwanted mail and lost mail in the process. People could lose confidence that the mail will get through. If a communication environment cannot be trusted, confidence in it can collapse. Electronic communication systems sit on the horns of a dilemma. Reducing spam increases delivery failure rate, while guaranteeing delivery increases spam rates. Either way, by social failure of confidence or technical failure of capability, spam threatens the transmission system itself (Weinstein, 2003). As the percentage of transmitted spam increases, both problems increase. If spam were 99% of sent mail, a small false-positive percentage becomes a much higher percentage of valid e-mail that failed. The growing spam problem is recognized ambivalently by IT writers who espouse new Bayesian spam filters but note, “The problem with spam is that it is almost impossible to define” (Vaughan-Nichols, 2003, p. 142), or who advocate legal solutions but say none have worked so far. The technical community seems to be in a state of denial regarding spam. Despite some successes, transmitted spam is increasing. Moral outrage, spam blockers, spamming the spammers, black and white lists, and legal responses have slowed but not stopped it. Spam blockers, by hiding the problem from users, may be making it worse, as a Band-Aid covers but does not cure a systemic sore. Asking for a technical tool to stop spam may be asking the wrong question. If spam is a social problem, it may require a social solution, which in cyberspace means technical support for social requirements (Whitworth & Whitworth, 2004).
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Conference papers on the topic "Bayesian spam filtering"

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Vu Duc Lung and Truong Nguyen Vu. "Bayesian spam filtering for Vietnamese emails." In 2012 International Conference on Computer & Information Science (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccisci.2012.6297237.

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Chuanliang Chen, Yingjie Tian, and Chunhua Zhang. "Spam filtering with several novel bayesian classifiers." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761531.

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Zhang, Hong-yan, and Wei Wang. "Application of Bayesian Method to Spam SMS Filtering." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5365176.

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Yishan Gong and Qiang Chen. "Research of spam filtering based on Bayesian algorithm." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620432.

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Yin, Hu, and Zhang Chaoyang. "An Improved Bayesian Algorithm for Filtering Spam E-Mail." In 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC). IEEE, 2011. http://dx.doi.org/10.1109/iptc.2011.29.

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Wu, Jiansheng, and Tao Deng. "Research in Anti-Spam Method Based on Bayesian Filtering." In 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA). IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.180.

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Deshpande, Vikas P., Robert F. Erbacher, and Chris Harris. "An Evaluation of Naive Bayesian Anti-Spam Filtering Techniques." In 2007 IEEE SMC Information Assurance and Security Workshop. IEEE, 2007. http://dx.doi.org/10.1109/iaw.2007.381951.

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Yun Wang, Zhiqiang Wu, and Runxiu Wu. "Spam filtering system based on rough set and Bayesian classifier." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664716.

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Jiansheng, Wu, and Zhao Xingwen. "Improvement of Chinese spam filtering method based on Bayesian classification." In 2010 2nd International Conference on Future Computer and Communication. IEEE, 2010. http://dx.doi.org/10.1109/icfcc.2010.5497327.

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Taninpong, Phimphaka, and Sudsanguan Ngamsuriyaroj. "Incremental Adaptive Spam Mail Filtering Using Naïve Bayesian Classification." In 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing. IEEE, 2009. http://dx.doi.org/10.1109/snpd.2009.45.

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