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Artículos de revistas sobre el tema "Spam-Filter"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 1 de febrero de 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Jain, Ankit Kumar, Sumit Kumar Yadav y Neelam Choudhary. "A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques". International Journal of E-Services and Mobile Applications 12, n.º 1 (enero de 2020): 21–38. http://dx.doi.org/10.4018/ijesma.2020010102.

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Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.
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12

Wu, Hongli y Yong Hui Jiang. "SMS Spam Filtering Based on “Cloud Security”". Applied Mechanics and Materials 263-266 (diciembre de 2012): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2015.

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“Cloud Computing” technology has very big advantage in the computing power, scalability, reliability and cost etc. “Cloud Security "and " Cloud Storage " is one of the two main research fields. This paper puts forward “filter cloud” strategies of filter spam messages based on "Cloud Security" in order to achieve the purpose of filtering spam messages by addressing its root causes. It is a new attempt that applying “Cloud Computing” in spam messages filter and more mobile business would move to "cloud computing" platform in the future.
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13

Varghese, Liny, M. H. Supriya y K. Poulose Jacob. "Improved Spam Filter for Obfuscated Emails". International Journal of Data Mining And Emerging Technologies 7, n.º 1 (2017): 33. http://dx.doi.org/10.5958/2249-3220.2017.00005.2.

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14

Ellis, B. "Spam or ham? [email protection filter]". Engineering & Technology 3, n.º 11 (21 de junio de 2008): 32–33. http://dx.doi.org/10.1049/et:20081101.

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15

Roy, Pradeep Kumar, Jyoti Prakash Singh y Snehasish Banerjee. "Deep learning to filter SMS Spam". Future Generation Computer Systems 102 (enero de 2020): 524–33. http://dx.doi.org/10.1016/j.future.2019.09.001.

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16

Chirra, Venkata RamiReddy, Hoolda Daniel Maddiboyina, Yakobu Dasari y Ranganadhareddy Aluru. "Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks". Review of Computer Engineering Studies 7, n.º 4 (31 de diciembre de 2020): 91–95. http://dx.doi.org/10.18280/rces.070403.

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Spam in email box is received because of advertising, collecting personal information, or to indulge malware through websites or scripts. Most often, spammers send junk mail with an intention of committing email fraud. Today spam mail accounts for 45% of all email and hence there is an ever-increasing need to build efficient spam filters to identify and block spam mail. However, notably today’s spam filters in use are built using traditional approaches such as statistical and content-based techniques. These techniques don’t improve their performance while handling huge data and they need a lot of domain expertise, human intervention and they neglect the relation between the words in context and consider the occurrence of the word. To address these limitations, we developed a spam filter using deep neural networks. In this work, various deep neural networks such as RNN, LSTM, GRU, Bidirectional RNN, Bidirectional LSTM, and Bidirectional GRU are used to a built spam filter. The experimentation was carried out on two datasets, one is a 20 newsgroup dataset, which contains multi-classes with 20,000 documents and the other is ENRON, a dataset contains 5,000 emails. The custom-designed models have performed well on both benchmark datasets and attained greater accuracy.
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17

Liu, Xin, Pingjun Zou, Weishan Zhang, Jiehan Zhou, Changying Dai, Feng Wang y 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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18

Yu, Hong Tao, Xing Li y Rui Fei Cui. "A Rapid Image Spam Recognition Method Based on Content Feature Fusion Decision". Applied Mechanics and Materials 321-324 (junio de 2013): 2623–29. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2623.

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As a variation of spam, image spam can evade the text based filter simply and effectively. Based on the characteristic that this kind of mail is usually generated in manner of template and the image content under the same template are highly similar, this paper proposes a rapid method for identifying image spam by fusing content features. The layout features are first adopted to localize the spam speedily, and then the wavelet transform based content feature fusion decision algorithm is used to determine the spam accurately. The experimental results demonstrate that the proposed method is fast, accurate and practical.
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19

Lv, Teng, Ping Yan, Hongwu Yuan y Weimin He. "Spam Filter Based on Naive Bayesian Classifier". Journal of Physics: Conference Series 1575 (junio de 2020): 012054. http://dx.doi.org/10.1088/1742-6596/1575/1/012054.

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20

Zhong, Zhenyu y Kang Li. "Speed Up Statistical Spam Filter by Approximation". IEEE Transactions on Computers 60, n.º 1 (enero de 2011): 120–34. http://dx.doi.org/10.1109/tc.2010.92.

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21

Li, Kang y Zhenyu Zhong. "Fast statistical spam filter by approximate classifications". ACM SIGMETRICS Performance Evaluation Review 34, n.º 1 (26 de junio de 2006): 347–58. http://dx.doi.org/10.1145/1140103.1140317.

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22

RajKishore Sahni. "Analysis of Naıve Bayes Algorithm for Email Spam Filtering". January 2021 7, n.º 01 (1 de enero de 2021): 5–9. http://dx.doi.org/10.46501/ijmtst0701002.

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The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep learning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails
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23

Yang, Hong, Qihe Liu, Shijie Zhou y Yang Luo. "A Spam Filtering Method Based on Multi-Modal Fusion". Applied Sciences 9, n.º 6 (19 de marzo de 2019): 1152. http://dx.doi.org/10.3390/app9061152.

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In recent years, the single-modal spam filtering systems have had a high detection rate for image spamming or text spamming. To avoid detection based on the single-modal spam filtering systems, spammers inject junk information into the multi-modality part of an email and combine them to reduce the recognition rate of the single-modal spam filtering systems, thereby implementing the purpose of evading detection. In view of this situation, a new model called multi-modal architecture based on model fusion (MMA-MF) is proposed, which use a multi-modal fusion method to ensure it could effectively filter spam whether it is hidden in the text or in the image. The model fuses a Convolutional Neural Network (CNN) model and a Long Short-Term Memory (LSTM) model to filter spam. Using the LSTM model and the CNN model to process the text and image parts of an email separately to obtain two classification probability values, then the two classification probability values are incorporated into a fusion model to identify whether the email is spam or not. For the hyperparameters of the MMA-MF model, we use a grid search optimization method to get the most suitable hyperparameters for it, and employ a k-fold cross-validation method to evaluate the performance of this model. Our experimental results show that this model is superior to the traditional spam filtering systems and can achieve accuracies in the range of 92.64–98.48%.
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24

Zhang, Dai Yuan y Lei Yang. "Implementation of Mail Classification Using Neural Networks of the Second Type Spline Weight Functions". Applied Mechanics and Materials 513-517 (febrero de 2014): 687–90. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.687.

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How to effectively filter out spam is a topic worthy of further study for the growing proliferation of spam. The main purpose of this paper is to apply a new neural network algorithm to the classification of spam. In this paper, we introduce a second type of spline weight function neural network algorithm, as well as e-mail feature extraction and vectorization, and then introduced the mail sorting process. Experiments show that it can get a relatively high accuracy and recall rate on the spam classification. Therefore, with this new algorithm, we can achieve better classification results.
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Wang, Jing, Xing Wei Wang y Min Huang. "A Spam Filtering Scheme Based on Scalable Decision Tree". Applied Mechanics and Materials 602-605 (agosto de 2014): 3076–79. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3076.

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Nowadays, Internet has been flooded with spams, which not only disrupts routine network application, but also affects people’s normal life and work. Thus, an accurate and effective spam filtering scheme is indispensable. In this paper, the scalable decision tree is utilized to analyze feature information of email-header to obtain anti-spam rules, which can be further used to filter spams. As is shown in the test result that the proposed scheme has satisfying performance and it is feasible in spam filtering.
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Ansari, Gunjan, Tanvir Ahmad y Mohammad Najmud Doja. "Spam Review Classification Using Ensemble of Global and Local Feature Selectors". Cybernetics and Information Technologies 18, n.º 4 (1 de noviembre de 2018): 29–42. http://dx.doi.org/10.2478/cait-2018-0046.

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Abstract In our work, we propose an ensemble of local and global filter-based feature selection method to reduce the high dimensionality of feature space and increase accuracy of spam review classification. These selected features are then used for training various classifiers for spam detection. Experimental results with four classifiers on two available datasets of hotel reviews show that the proposed feature selector improves the performance of spam classification in terms of well-known performance metrics such as AUC score.
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27

Nguyen, Tuan-Anh, Quang-Anh Tran y Xuan-Thang Nguyen. "Spam Filter based on Dynamic Sender Policy Framework". Proceedings of the Asia-Pacific Advanced Network 30 (31 de diciembre de 2010): 1. http://dx.doi.org/10.7125/apan.30.1.

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Shvartsman, Alex. "Staff meeting, as seen by the spam filter". Nature 526, n.º 7575 (octubre de 2015): 734. http://dx.doi.org/10.1038/526734a.

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Amol, Khandekar, Garje Pramod y Rakesh Thakare. "Anti-spam Filter Based on Machine Learning Algorithm". IOSR Journal of Computer Engineering 16, n.º 1 (2014): 93–96. http://dx.doi.org/10.9790/0661-16189396.

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González-Talaván, Guillermo. "A simple, configurable SMTP anti-spam filter: Greylists". Computers & Security 25, n.º 3 (mayo de 2006): 229–36. http://dx.doi.org/10.1016/j.cose.2006.02.005.

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Wang, Bin, Gareth J. F. Jones y Wenfeng Pan. "Using online linear classifiers to filter spam emails". Pattern Analysis and Applications 9, n.º 4 (3 de octubre de 2006): 339–51. http://dx.doi.org/10.1007/s10044-006-0045-7.

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Adisantoso, Julio y Wildan Rahman. "Pengukuran Kinerja Spam Filter Menggunakan Graham's Naïve Bayes Classifier". Jurnal Ilmu Komputer dan Agri-Informatika 2, n.º 1 (1 de mayo de 2013): 1. http://dx.doi.org/10.29244/jika.2.1.1-8.

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<p>Email spam telah menjadi masalah utama bagi pengguna dan penyedia jasa Internet. Pendekatan heuristic telah dilakukan untuk menyaring spam seperti black-listing atau rule-based filtering, namun hasilnya kurang memuaskan sehingga pendekatan berbasis konten (content-based filtering) menggunakan pengklasifikasi naïve Bayes lebih banyak digunakan saat ini. Penelitian ini bertujuan membandingkan pengklasifikasi naïve Bayes multinomial yang menggunakan atribut boolean dengan versi Graham, dan juga membandingkan kinerja dari dua metode untuk data latih, yaitu train-everything (TEFT) dan train-on-error (TOE). Hasil evaluasi menunjukkan bahwa naïve Bayes multinomial memiliki kinerja lebih baik dibanding versi Graham. Di samping itu, metode data latih menggunakan TEFT dapat meningkatkan akurasi model klasifikasi dibanding metode TOE.</p><p>Kata kunci: filter spam, naïve Bayes, metode training</p>
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Salomon, Simon y Seng Hansun. "Spam Filter Situs Jejaring Sosial Mahasiswa Menggunakan Regular Expression". Jurnal ULTIMA InfoSys 8, n.º 2 (2 de abril de 2018): 69–73. http://dx.doi.org/10.31937/si.v8i2.615.

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Spam is an unexpected and unsolicited email sent randomly indiscriminately, directly or indirectly by the sender who has no connection whatsoever with the recipient. The purpose of spam itself is to send information to the recipient, where the content of the sent message generally contains ads that offer nonessential products or illegal products, scams, promotional purposes, or spreading malware designed to hijack computers receiver. Based on the background of the problem, it is necessary anti-spam on a chat or dissemination of information in social networking using regular expression. From this study, the behavioral intention to use at level of 80% means that the user agrees that this website increases user interest in obtaining information and communication, and generates an immersion level of 80% which means the user is very focused when using the website. This website generates value by 98% precision and 98% recall that produce harmonic mean value of 97% so that it can be concluded that it has the precision and recall value harmonious. Index Terms—social networking, regular expression, spam, website
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Teja, P. Sai. "Prediction of Spam Email using Machine Learning Classification Algorithm". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (15 de junio de 2021): 1107–12. http://dx.doi.org/10.22214/ijraset.2021.35226.

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Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.
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Vernanda, Yustinus, Seng Hansun y Marcel Bonar Kristanda. "Indonesian language email spam detection using N-gram and Naïve Bayes algorithm". Bulletin of Electrical Engineering and Informatics 9, n.º 5 (1 de octubre de 2020): 2012–19. http://dx.doi.org/10.11591/eei.v9i5.2444.

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Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.
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Ratniasih, Ni Luh, Made Sudarma y Nyoman Gunantara. "PENERAPAN TEXT MINING DALAM SPAM FILTERING UNTUK APLIKASI CHAT". Majalah Ilmiah Teknologi Elektro 16, n.º 3 (29 de diciembre de 2017): 13. http://dx.doi.org/10.24843/mite.2017.v16i03p03.

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The Internet has become something important in the communication development. One communication facilities on the Internet is the Internet relay chat or known as chat. Chat applications in real time is often misused for the purpose of spreading the virus, promotions, and other interests known as spam. Spamming is the sending of unwanted messages by someone who has a chat account. This causes the chat account feel uncomfortable with the condition. Based on these problems this research create a chat application that can filter messages or spam filtering by applying text mining. Spam filtering process can be done in two phases: text pre-processing and analyzing. These two phases are carried out to calculate the weight (W) of connectedness with the word spam messages. Based on the results of tests performed on chat applications by applying text mining to perform filtering on spam messages generate the level of accuracy of 91.41%.
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Bouarara, Hadj Ahmed, Reda Mohamed Hamou y Abdelmalek Amine. "A Novel Bio-Inspired Approach for Multilingual Spam Filtering". International Journal of Intelligent Information Technologies 11, n.º 3 (julio de 2015): 45–87. http://dx.doi.org/10.4018/ijiit.2015070104.

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In today's digital world the email service has revolutionized the sphere of electronic communication. It has become a veritable social phenomenon in our daily life. Unfortunately, this technology has become incontestably the original source of malicious activities especially the plague called undesirable emails (SPAM) that has grown tremendously in the last few years. The battle against spam emails is extremely fierce. This paper deals with an intelligent spam filtering system called artificial heart-lungs system (AHLS) mimicked from the biological phenomenon of general circulation and oxygenation of blood. It is composed of different steps: Selection to stop automatically emails with undesirable identifier. Multilingual pre-processing to treat the problem of multilingual spam emails and vectoring them. Heart filter and lungs filter to classify unwelcome email in the spam folder and welcome email in the ham folder to present them to the recipient. The method uses an automatic updating of learning basis and black list, and a ranking step to order the spam mails according to their spam relevancy. For the authors' experimentation, they have constructed a new dataset M.SPAM composed of emails pre-classified as spam or ham with different language (English, Spanish, French, and melange) and using the validation measures (recall, precision, f-measure, entropy, accuracy and error, false positive rate and false negative rate, ROC and learning curve). The authors have optimized the sensitive parameters (text representation technique, lungs filters, and the size of initial leaning basis). The results are positive compared to the result of other bio-inspired techniques (artificial social bees, artificial social cockroaches), supervised algorithm (decision tree C4.5) and automatic algorithm (K-means). Finally, a visual result mining tool was developed in order to see the results in graphical form (3d cub and cobweb) with more realism using the functionality of zooming and rotation. The authors' aims are to eliminate a large proportion of unwelcome email, treated the multilingual emails, ensuring an automatic updating of their system and poses a minimal risk of eliminating ham email.
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38

Aswin, R., E. Ganesh y M. Babu. "E-Mail Security Algorithm to Filter Out Spam E-mails using Machine Learning". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 223–28. http://dx.doi.org/10.31142/ijtsrd10815.

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39

Lee, Song-Wook. "Spam Filter by Using X2Statistics and Support Vector Machines". KIPS Transactions:PartB 17B, n.º 3 (30 de junio de 2010): 249–54. http://dx.doi.org/10.3745/kipstb.2010.17b.3.249.

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40

Saeedian, Mehrnoush Famil y Hamid Beigy. "Learning to filter spam emails: An ensemble learning approach". International Journal of Hybrid Intelligent Systems 9, n.º 1 (13 de marzo de 2012): 27–43. http://dx.doi.org/10.3233/his-2011-0145.

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41

Yue, Xun, Ajith Abraham, Zhong-Xian Chi, Yan-You Hao y Hongwei Mo. "Artificial immune system inspired behavior-based anti-spam filter". Soft Computing 11, n.º 8 (2 de septiembre de 2006): 729–40. http://dx.doi.org/10.1007/s00500-006-0116-0.

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42

Wang, Junzhang, Diwen Xue y Karen Shi. "An Ensemble Framework for Spam Detection on Social Media Platforms". International Journal of Machine Learning and Computing 11, n.º 1 (enero de 2021): 77–84. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1017.

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As various review sites grow in popularity and begin to hold more sway in consumer preferences, spam detection has become a burgeoning field of research. While there have been various attempts to resolve the issue of spam on the open web, specifically as it relates to reviews, there does not yet exist an adaptive and robust framework out there today. We attempt to address this issue in a domain-specific manner, choosing to apply it to Yelp.com first. We believe that while certain processes do exist to filter out spam reviews for Yelp, we have a comprehensive framework that can be extended to other applications of spam detection as well. Furthermore, our framework exhibited a robust performance even when trained on small datasets, providing an approach for practitioners to conduct spam detection when the available data is inadequate. To the best of our knowledge, our framework uses the most number of extracted features and models in order to finely tune our results. In this paper, we will show how various sets of online review features add value to the final performance of our proposed framework, as well as how different machine learning models perform regarding detecting spam reviews.
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43

Yao, Lei Yue. "Filtering Ineffective Product Reviews in Electronic Commerce Website". Applied Mechanics and Materials 644-650 (septiembre de 2014): 2759–62. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2759.

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Excessive product reviews bring much inconvenient for users to obtain information. It will waste a lot of time, and these spam reviews will occupy much data traffic. Paper focuses on the design a fast filtering algorithm to filter out spam based on content and text length, only leave useful reviews to readers. It can help users to quickly view the more valuable and useful product reviews information, help users understand product information, and facilitate electronic transactions.
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44

Pi, Shih Ming, Hsiu Li Liao, Su Houn Liu y Ding Kang Liu. "Using Artificial Neural Network to Filter Spam for Chinese Mail". Applied Mechanics and Materials 55-57 (mayo de 2011): 762–66. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.762.

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As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.
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45

Mohd Tamil, Emran, Wan Nur Azhani Wan Samsudin, Mohd Yamani Idna Idris, Madihah Mohd Saudi, Zaidi Razak y Noorzaily Mohamed Noor. "Bayesian and Fuzzy Logic Implementation for SPAM/UCE Inline Filter". International Journal of Technology, Knowledge, and Society 4, n.º 5 (2008): 1–8. http://dx.doi.org/10.18848/1832-3669/cgp/v04i05/55926.

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46

kaur, Amandeep y Malti Sarangal. "A Hybrid approach for enhancing the capability of Spam Filter". International Journal of Computer Applications Technology and Research 2, n.º 6 (10 de diciembre de 2013): 759–62. http://dx.doi.org/10.7753/ijcatr0206.1024.

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47

Pidmohylʹnyy, O. O., O. M. Tkachenko, O. I. Holubenko y O. V. Drobyk. "Naive Bayes Classifier as one way to filter spam mail". Connectivity 142, n.º 6 (2019): 58–60. http://dx.doi.org/10.31673/2412-9070.2019.065860.

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48

Aski, Ali Shafigh y Navid Khalilzadeh Sourati. "Proposed efficient algorithm to filter spam using machine learning techniques". Pacific Science Review A: Natural Science and Engineering 18, n.º 2 (julio de 2016): 145–49. http://dx.doi.org/10.1016/j.psra.2016.09.017.

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49

Caruana, Godwin, Maozhen Li y Yang Liu. "An ontology enhanced parallel SVM for scalable spam filter training". Neurocomputing 108 (mayo de 2013): 45–57. http://dx.doi.org/10.1016/j.neucom.2012.12.001.

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50

Wheatcroft, David J. "Co-evolution: A Behavioral ‘Spam Filter’ to Prevent Nest Parasitism". Current Biology 19, n.º 4 (febrero de 2009): R170—R171. http://dx.doi.org/10.1016/j.cub.2008.12.034.

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