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Literatura académica sobre el tema "Spam-Filter"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "Spam-Filter"

Nurlina, Nurlina y Irmayana Irmayana. "Studi Banding Spam-Assassin Mail Server Dengan dan Tanpa Filter di Sisi Mail Client". Creative Information Technology Journal 1, n.º 2 (2 de abril de 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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Mccollough, Andrew W. y Edward K. Vogel. "Your Inner Spam Filter". Scientific American Mind 19, n.º 3 (junio de 2008): 74–77. http://dx.doi.org/10.1038/scientificamericanmind0608-74.

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Almeida, Tiago A. y Akebo Yamakami. "Compression-based spam filter". Security and Communication Networks 9, n.º 4 (25 de septiembre de 2012): 327–35. http://dx.doi.org/10.1002/sec.639.

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Ye, Liang, Ying Hong Liang y Peng Liu. "Bayesian Spam Filter Based on Distributed Architecture". Advanced Materials Research 108-111 (mayo de 2010): 1415–20. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1415.

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The flood of spam promotes the development of anti-spam technology. In this paper, we bring forward the Bayesian filter technology based on the distributed architecture, which can realize the sharing of the Bayesian learning outcomes among servers within the system, so as to increase the accuracy of spam recognition. We, in the paper, discuss the sharing model of information with spam features under the distributed architecture and the spam identification process; analyze the Bayes algorithm and carry out the relevant improvements; design the Bayes Filter based on distributed architecture on the above basis and verify the effect of the filter by experiments.

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Wang, Ying Jie, Xiao Yu Chen, Lin Wang y Xiao Qiang Liang. "Study on ASP-Based Anti-Spam Management System". Applied Mechanics and Materials 411-414 (septiembre de 2013): 581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.581.

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A perfect spam filter would avoid ham misclassification. This paper describes the development and application of spam filter. Specifically, the evaluation methodology was designed on-line open source spam filters. Finally, ASP-based anti-spam management system was created to combine the results of multiple filters. We finally find that the filters of ASP-based anti-spam system also make mistakes but can be used in conjunction with users to minimize errors.

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Youn, Seongwook. "SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology". Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/414583.

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Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user’s background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

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Jiang, Xue y Jun Kai Yi. "Improved Bayesian-Based Spam Filtering Approach". Applied Mechanics and Materials 401-403 (septiembre de 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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Abebe, Tewodros. "Bayesian Spam Filter for Wolaytta". International Journal of Advanced Engineering Research and Science 6, n.º 12 (2019): 540–44. http://dx.doi.org/10.22161/ijaers.612.64.

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Cormack, Gordon V. y Thomas R. Lynam. "Online supervised spam filter evaluation". ACM Transactions on Information Systems 25, n.º 3 (julio de 2007): 11. http://dx.doi.org/10.1145/1247715.1247717.

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Almeida, Tiago A. y Akebo Yamakami. "Occam’s razor-based spam filter". Journal of Internet Services and Applications 3, n.º 3 (2 de octubre de 2012): 245–53. http://dx.doi.org/10.1007/s13174-012-0067-x.

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Tesis sobre el tema "Spam-Filter"

Fredborg, Johan. "Spam filter for SMS-traffic". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94161.

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Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time. The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter. Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.

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Albrecht, Keno. "Mastering spam : a multifaceted approach with the Spamato spam filter system /". Zürich : ETH, 2006. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16839.

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Lingaas, Türk Jakob. "Comparing the relative efficacy of phishing emails". Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42392.

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This study aimed to examine if there was a difference in how likely a victim is to click on a phishing email’s links based on the content of the email, the tone and language used and the structure of the code. This likelihood also includes the email’s ability to bypass spam filters. Method: The method used to examine this was a simulated phishing attack. Six different phishing templates were created and sent out via the Gophish framework to target groups of students (from Halmstad University), from a randomized pool of 20.000 users. The phishing emails contained a link to a landing page (hosted via a virtual machine) which tracked user status. The templates were: Covid19 Pre-Attempt, Spotify Friendly CSS, Spotify Friendly Button, Spotify Aggressive CSS, Spotify Aggressive Button, Student Union. Results: Covid19 Pre-Attempt: 72.6% initial spam filter evasion, 45.8% spam filter evasion, 4% emails opened and 100% links clicked. Spotify Friendly CSS: 50% initial spam filter evasion, 38% spam filter evasion, 26.3% emails opened and 0% links clicked. Spotify Friendly Button: 59% initial spam filter evasion, 28.8% spam filter evasion, 5.8% emails opened and 0 %links clicked. Spotify Aggressive CSS: 50% initial spam filter evasion, 38% spam filter evasion, 10.5% emails opened, and 100% links clicked. Spotify Aggressive Button: 16% initial spam filter evasion, 25% spam filter evasion, 0% emails opened and 0% emails clicked. Student Union: 40% initial spam filter evasion, 75% spam filter evasion, 33.3% emails opened and 100% links clicked. Conclusion: Differently structured emails have different capabilities for bypassing spam filters and for deceiving users. Language and tone appears to affect phishing email efficacy; the results suggest that an aggressive and authoritative tone heightens a phishing email’s ability to deceive users, but seems to not affect its ability to bypass spam filters to a similar degree. Authenticity appears to affect email efficacy; the results showed a difference in deception efficacy if an email was structured like that of a genuine sender. Appealing to emotions such as stress and fear appears to increase the phishing email’s efficacy in deceiving a user.
Syftet med denna studie var att undersöka om det fanns en skillnad i hur troligt det är att ett offer klickar på länkarna till ett phishing-e-postmeddelande, baserat på innehållet i e-postmeddelandet, tonen och språket som används och kodens struktur. Denna sannolikhet inkluderar även e-postens förmåga att kringgå skräppostfilter. Metod: Metoden som användes var en simulerad phishing-attack. Sex olika phishing-mallar skapades och skickades ut via Gophish-ramverket till målgruppen bestående av studenter (från Halmstads universitet), från en slumpmässig pool med 20 000 användare. Phishing-e-postmeddelandena innehöll en länk till en målsida (hostad via en virtuell maskin) som spårade användarstatus. Mallarna var: Covid19 Pre-Attempt, Spotify Friendly CSS, Spotify Friendly Button, Spotify Aggressive CSS, Spotify Aggressive Button, Student Union. Resultat: Covid19 förförsök: 72,6% kringgick det primära spamfiltret, 45,8% kringgick det sekundära spamfiltret, 4% e-postmeddelanden öppnade och 100% länkar klickade Spotify Friendly CSS: 50% kringgick det primära spamfiltret, 38% kringgick det sekundära spamfiltret, 26,3% e-postmeddelanden öppnade och 0% länkar klickade. Spotify Friendly Button: 59% kringgick det primära spamfiltret, 28,8% kringgick det sekundära spamfiltret, 5.8% e-postmeddelanden öppnade och 0% länkar klickade. Spotify Aggressive CSS: 50% kringgick det primära spamfiltret, 38% kringgick det sekundära spamfiltret, 10,5% e-post öppnade och 100% länkar klickade. Spotify Aggressive Button: 16% kringgick det primära spamfiltret, 25% kringgick det sekundära spamfiltret, 0% e-postmeddelanden öppnade och 0% e-postmeddelanden klickade. Studentkåren: 40% kringgick det primära spamfiltret, 75% kringgick det sekundära spamfiltret, 33,3% e-postmeddelanden öppnade och 100% länkar klickade. Slutsats: Olika strukturerade e-postmeddelanden har olika funktioner för att kringgå skräppostfilter och för att lura användare. Språk och ton tycks påverka effektiviteten för epost-phishing. Resultaten tyder på att en aggressiv och auktoritär ton ökar phishing-epostmeddelandets förmåga att lura användare, men verkar inte påverka dess förmåga att kringgå skräppostfilter i motsvarande grad. Autenticitet verkar påverka e-postens effektivitet, då resultaten visade en skillnad i effektivitet om ett e-postmeddelande var strukturerat som en äkta avsändare. Att adressera känslor som stress och rädsla verkar öka phishing-e-postens effektivitet när det gäller att lura en användare.

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Richter, Frank. ""Die guten ins Töpfchen, die schlechten ins ..." - Filter für E-Mail". Universitätsbibliothek Chemnitz, 2001. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200100300.

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Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur "Rechnernetze und verteilte Systeme" der Fakultaet fuer Informatik der TU Chemnitz. Workshop-Thema: Mobilitaet Es werden Filtermöglichkeiten für E-Mails vorgestellt, um die E-Mail-Bearbeitung zu automatisieren und Spam-Mails und Mails mit gefährlichem Inhalt abzuwehren.

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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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Singh, Kuldeep. "An Investigation of Spam Filter Optimaltiy : based on Signal Detection Theory". Thesis, Norwegian University of Science and Technology, Department of Telematics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9960.

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Unsolicited bulk email, commonly known as spam, represents a significant problem on the Internet. The seriousness of the situation is reflected by the fact that approximately 97% of the total e-mail traffic currently (2009) is spam. To fight this problem, various anti-spam methods have been proposed and are implemented to filter out spam before it gets delivered to recipients, but none of these methods are entirely satisfactory. This thesis analyzes the properties of spam filters from the viewpoint of Signal Detection Theory (SDT). The Bayesian approach of Signal Detection Theory provides a basis for determining the tuning of spam filters from the particular user's point of view and helps in determining the utility which the spam filter provides to the user.

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Havens, Russel William. "Naive Bayesian Spam Filters for Log File Analysis". BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2814.

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As computer system usage grows in our world, system administrators need better visibility into the workings of computer systems, especially when those systems have problems or go down. Most system components, from hardware, through OS, to application server and application, write log files of some sort, be it system-standardized logs such syslog or application specific logs. These logs very often contain valuable clues to the nature of system problems and outages, but their verbosity can make them difficult to utilize. Statistical data mining methods could help in filtering and classifying log entries, but these tools are often out of the reach of administrators. This research tests the effectiveness of three off-the-shelf Bayesian spam email filters (SpamAssassin, SpamBayes and Bogofilter) for effectiveness as log entry classifiers. A simple scoring system, the Filter Effectiveness Scale (FES), is proposed and used to compare these filters. These filters are tested in three stages: 1) the filters were tested with the SpamAssassin corpus, with various manipulations made to the messages, 2) the filters were tested for their ability to differentiate two types of log entries taken from actual production systems, and 3) the filters were trained on log entries from actual system outages and then tested on effectiveness for finding similar outages via the log files. For stage 1, messages were tested with normalized bodies, normalized headers and with each sentence from each message body as a separate message with a standardized message. The impact of each manipulation is presented. For stages 2 and 3, log entries were tested with digits normalized to zeros, with words chained together to various lengths and one or all levels of word chains used together. The impacts of these manipulations are presented. In each of these stages, it was found that these widely available Bayesian content filters were effective in differentiating log entries. Tables of correct match percentages or score graphs, according to the nature of tests and numbers of entries are presented, are presented, and FES scores are assigned to the filters according to the attributes impacting their effectiveness. This research leads to the suggestion that simple, off-the-shelf Bayesian content filters can be used to assist system administrators and log mining systems in sifting log entries to find entries related to known conditions (for which there are example log entries), and to exclude outages which are not related to specific known entry sets.

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Frobese, Dirk T. "E-Mail-Kategorisierung und Spam-Detektion mit SENTRAX [Mustererkennung mit Assoziativmatrizen]". Hildesheim Berlin Franzbecker, 2009. http://d-nb.info/999598341/04.

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Jägenstedt, Gabriel. "Analysis and Simulation of Threats in an Open, Decentralized, Distributed Spam Filtering System". Thesis, Linköpings universitet, Databas och informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81012.

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The existance of spam email has gone from a fairly small amounts of afew hundred in the late 1970’s to several billions per day in 2010. Thiscontinually growing problem is of great concern to both businesses andusers alike.One attempt to combat this problem comes with a spam filtering toolcalled TRAP. The primary design goal of TRAP is to enable tracking ofthe reputation of mail senders in a decentralized and distributed fashion.In order for the tool to be useful, it is important that it does not haveany security issues that will let a spammer bypass the protocol or gain areputation that it should not have.As a piece of this puzzle, this thesis makes an analysis of TRAP’s protocoland design in order to find threats and vulnerabilies capable of bypassingthe protocol safeguards. Based on these threats we also evaluate possiblemitigations both by analysis and simulation. We have found that althoughthe protocol was not designed with regards to certain attacks on the systemitself most of the attacks can be fairly easily stopped.The analysis shows that by adding cryptographic defenses to the protocola lot of the threats would be mitigated. In those cases where cryptographywould not suffice it is generally down to sane design choices in the implementationas well as not always trusting that a node is being truthful andfollowing protocol.

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Matula, Tomáš. "Techniky umělé inteligence pro filtraci nevyžádané pošty". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236060.

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This thesis focuses on the e-mail classification and describes the basic ways of spam filtering. The Bayesian spam classifiers and artificial immune systems are analyzed and applied in this thesis. Furthermore, existing applications and evaluation metrics are described. The aim of this thesis is to design and implement an algorithm for spam filtering. Ultimately, the results are compared with selected known methods.

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Más fuentes

Libros sobre el tema "Spam-Filter"

Eisentraut, Peter. Mit Open Source-Tools Spam und Viren beka mpfen: [Lo sungen fu r Postfix, Exim & sendmail]. Beijing: O'Reilly, 2005.

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Gelman, Andrew y Deborah Nolan. Statistical thinking in a data science course. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0021.

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In this chapter, we describe the philosophy, goals, syllabus, and activities for a course that we have developed in data science course. In this course we integrate topics from computing, statistics, and working with data. This integrated approach addresses many core aspects in statistics training, including statistical thinking, the role of context in addressing a statistical problem, statistical communication through code, and the balance between programming and mathematical approaches to problems. When designing this course, we asked ourselves what our students ought to be able to do computationally. While we do provide a list of technical material, we also considered the broader goals of the course. Examples include plotting on Google Earth and developing a spam filter for unwanted email.

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Capítulos de libros sobre el tema "Spam-Filter"

Garcia, Flavio D., Jaap-Henk Hoepman y Jeroen Nieuwenhuizen. "Spam Filter Analysis". En Security and Protection in Information Processing Systems, 395–410. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/1-4020-8143-x_26.

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Seibel, Peter. "Practical: A Spam Filter". En Practical Common Lisp, 291–309. Berkeley, CA: Apress, 2005. http://dx.doi.org/10.1007/978-1-4302-0017-8_23.

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Bezerra, George B., Tiago V. Barra, Hamilton M. Ferreira, Helder Knidel, Leandro Nunes de Castro y Fernando J. Von Zuben. "An Immunological Filter for Spam". En Lecture Notes in Computer Science, 446–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823940_34.

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Cheng, Xiaochun, Xiaoqi Ma, Long Wang y Shaochun Zhong. "A Mobile Agent Based Spam Filter System". En Computational Intelligence and Security, 422–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_62.

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Nelson, Blaine, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar y Kai Xia. "Misleading Learners: Co-opting Your Spam Filter". En Machine Learning in Cyber Trust, 17–51. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88735-7_2.

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Kuang, Bing Qia, Pi Yuan Lin, Pei Jie Huang, Jian Feng Zhang y Guo Qiu Liang. "Spam Filter Based on Multiple Classifiers Combinational Model". En Lecture Notes in Electrical Engineering, 689–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40618-8_89.

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Iranmanesh, Seyed Amir, Hemant Sengar y Haining Wang. "A Voice Spam Filter to Clean Subscribers’ Mailbox". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 349–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36883-7_21.

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Wang, Zhan, Yoshiaki Hori y Kouichi Sakurai. "Application and Evaluation of Bayesian Filter for Chinese Spam". En Information Security and Cryptology, 253–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11937807_20.

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Lai, Gu-Hsin, Chao-Wei Chou, Chia-Mei Chen y Ya-Hua Ou. "Anti-spam Filter Based on Data Mining and Statistical Test". En Computer and Information Science 2009, 179–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01209-9_17.

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Özgür, Levent, Tunga Güngör y Fikret Gürgen. "Spam Mail Detection Using Artificial Neural Network and Bayesian Filter". En Lecture Notes in Computer Science, 505–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_74.

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Actas de conferencias sobre el tema "Spam-Filter"

Hassan, T., P. Cole y Chun Che Fung. "An Intelligent SPAM filter - GetEmail5". En 2006 IEEE Conference on Cybernetics and Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/iccis.2006.252253.

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Zhang, Ni, Yu Jiang, Binxing Fang, Xueqi Cheng y Li Guo. "Traffic classification-based spam filter". En 2006 IEEE International Conference on Communications. IEEE, 2006. http://dx.doi.org/10.1109/icc.2006.255085.

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Lynam, Thomas R., Gordon V. Cormack y David R. Cheriton. "On-line spam filter fusion". En the 29th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1148170.1148195.

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Brewer, D., S. Thirumalai, K. Gomadam y Kang Li. "Towards an Ontology Driven Spam Filter". En 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.151.

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Zhao, Yaqing, Yan Xu y Xiaodan Zhao. "Chinese Spam Filter under Adversarial Impact". En 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icmmcce-15.2015.562.

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Issac, Biju, Wendy Japutra Jap y Jofry Hadi Sutanto. "Improved Bayesian Anti-Spam Filter Implementation and Analysis on Independent Spam Corpuses". En 2009 International Conference on Computer Engineering and Technology (ICCET). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.170.

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Cai, Jie, Yuezhong Tang y Rile Hu. "Spam Filter for Short Messages Using Winnow". En 2008 International Conference on Advanced Language Processing and Web Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/alpit.2008.14.

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Guo, Yanhui, Yaolong Zhang, Jianyi Liu y Cong Wang. "Research on the Comprehensive Anti-Spam Filter". En 2006 IEEE International Conference on Industrial Informatics. IEEE, 2006. http://dx.doi.org/10.1109/indin.2006.275765.

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Chengcheng Li y Jianyi Liu. "Combining behavior and Bayesian Chinese spam filter". En 2009 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2009). IEEE, 2009. http://dx.doi.org/10.1109/icnidc.2009.5360937.

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Liang Ye, Weiming Zhong, Zhiyong Xiong y Peng Liu. "Bayesian filter based on Anti-Spam Grid". En 2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies (EDT). IEEE, 2010. http://dx.doi.org/10.1109/edt.2010.5496577.

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