Relevant bibliographies by topics / Spam message

Contents

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

Academic literature on the topic 'Spam message'

Author: Grafiati
Published: 28 May 2022
Last updated: 29 May 2022

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Journal articles on the topic "Spam message"

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Jain, Ankit Kumar, Sumit Kumar Yadav, and Neelam Choudhary. "A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques." International Journal of E-Services and Mobile Applications 12, no. 1 (January 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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She, Xue Bing. "Incremental-Learning Spam Messages Distinguishing and Sorting System Based on Arm Platform." Applied Mechanics and Materials 602-605 (August 2014): 3843–45. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3843.

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Now that the endless spam messages have sevely affected people on their works and daliy lives. The omission and even the help of the serrvice providrs (SP) in this regard make it extremely necessary to distinguish and sort the spam messages through filtering the messages on user’s mobile phone. The paper expatiated on a “self-study spam messages distinguishing and soring system’ developed onARM9 platform. By adding a spam box to SMS software of cellphome besidrs all os its normal functions, the system incessantly adjusts the weight of the featues though incremental learning method so as to achieve the highly accurate discrimination on whether a message received is the spam one or not, and futher decides to sort the message to the message box or the spam box, Test result on ARM9 platform shows, our technology can be applied completely to the mobile phones with just general performance.
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GIANNELLA, CHRIS R., RANSOM WINDER, and BRANDON WILSON. "(Un/Semi-)supervised SMS text message SPAM detection." Natural Language Engineering 21, no. 4 (October 15, 2014): 553–67. http://dx.doi.org/10.1017/s1351324914000102.

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AbstractWe address the problem of unsupervised and semi-supervised SMS (Short Message Service) text message SPAM detection. We develop a content-based Bayesian classification approach which is a modest extension of the technique discussed by Resnik and Hardisty in 2010. The approach assumes that the bodies of the SMS messages arise from a probabilistic generative model and estimates the model parameters by Gibbs sampling using an unlabeled, or partially labeled, SMS training message corpus. The approach classifies new SMS messages as SPAM or HAM (non-SPAM) by zero-thresholding their logit estimates. We tested the approach on a publicly available SMS corpora collected from the UK. Used in semi-supervised fashion, the approach clearly outperformed a competing algorithm, Semi-Boost. Used in unsupervised fashion, the approach outperformed a fully supervised classifier, an SVM (Support Vector Machine), when the number of training messages used by the SVM was small and performed comparably otherwise. We believe the approach works well and is a useful tool for SMS SPAM detection.
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Hemalatha, M., Sriharsha Katta, R. Sai Santosh, and Priyanka Priyanka. "E-MAIL SPAM DETECTION." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 36–44. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.006.

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E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.
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Jany Shabu, S. L., V. Netaji Subhash Chandra Bose, Venkatesh Bandaru, Sardar Maran, and J. Refonaa. "Spam and Fake Spam Message Detection Framework Using Machine Learning Algorithm." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3444–48. http://dx.doi.org/10.1166/jctn.2020.9202.

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Online reviews about the acquisition of items or administrations gave have become the primary wellspring of clients’ conclusions. So as to pick up benefit or acclaim, as a rule spam reviews are composed to advance or downgrade a couple of target items or administrations. This training is known as review spamming. In the previous barely any years, an assortment of strategies have been proposed so as to illuminate the issue of spam reviews. It is a mainstream correspondence and furthermore known as information trade media. Information could be of a book, numbers, figures or insights that are gotten to by a PC. These days, numerous individuals relies upon substance accessible in web-based social networking in their choices. Sharing of data with people groups has additionally pulled in social spammers to endeavor and spread spam messages to advance individual web logs, notices, advancements, phishing, trick, fakes, etc. The possibility that anyone will leave a review give a brilliant possibility for spammers to post spit audit with respect to item and administrations for different interests and possibilities. In this way, we propose a fake message detection system utilizing ML to recognize the spam and fake messages on the internet based life stage.
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GuangJun, Luo, Shah Nazir, Habib Ullah Khan, and Amin Ul Haq. "Spam Detection Approach for Secure Mobile Message Communication Using Machine Learning Algorithms." Security and Communication Networks 2020 (July 9, 2020): 1–6. http://dx.doi.org/10.1155/2020/8873639.

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The spam detection is a big issue in mobile message communication due to which mobile message communication is insecure. In order to tackle this problem, an accurate and precise method is needed to detect the spam in mobile message communication. We proposed the applications of the machine learning-based spam detection method for accurate detection. In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device communication. The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research. The results of the experiments demonstrated that the classification performance of LR is high as compared with K-NN and DT, and the LR achieved a high accuracy of 99%. Additionally, the proposed method performance is good as compared with the existing state-of-the-art methods.
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Ma, Jialin, Yongjun Zhang, Lin Zhang, Kun Yu, and Jinlin Liu. "Bi-Term Topic Model for SMS Classification." International Journal of Business Data Communications and Networking 13, no. 2 (July 2017): 28–40. http://dx.doi.org/10.4018/ijbdcn.2017070103.

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With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.
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Adewole, Kayode Sakariyah, Nor Badrul Anuar, Amirrudin Kamsin, and Arun Kumar Sangaiah. "SMSAD: a framework for spam message and spam account detection." Multimedia Tools and Applications 78, no. 4 (July 21, 2017): 3925–60. http://dx.doi.org/10.1007/s11042-017-5018-x.

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&NA;. "E-mail spam sends a message." Nursing 35, no. 9 (September 2005): 34. http://dx.doi.org/10.1097/00152193-200509000-00032.

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Kim, Seongyoon, Taesoo Cha, Jeawon Park, Jaehyun Choi, and Namyong Lee. "A Technique of Statistical Message Filtering for Blocking Spam Message." Journal of the Korea society of IT services 13, no. 3 (September 30, 2014): 299–308. http://dx.doi.org/10.9716/kits.2014.13.3.299.

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Dissertations / Theses on the topic "Spam message"

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Qvist, Olof, and Julia Berggren. "Viral Marketing : How does the individual view a viral marketing message and what makes him or her pass it along?" Thesis, University of Gävle, Department of Business Administration and Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-672.

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Viral marketing is a form of marketing that is based on individuals sharing of a message within their social network. When viral marketing works, it’s cheap and efficient and there are several examples of successful viral marketing campaigns that has given products or companies great success.

Viral marketing is relatively unexplored as a phenomenon, and there are several different suggested paths to choose to form a successful campaign. One suggestion is that viral marketers base their campaigns on different feelings to make the individual share the campaign, or feeling, with its social network. This is one of the things that we are looking at in this report. With a quantitative study based on the replies of over 800 students, we try to determine which feeling is more efficient in viral marketing campaigns. We also try to determine how viral campaigns are received and handled as well as students view on viral marketing as a phenomenon.

This report shows that receivers of viral marketing campaigns have a pattern in the way they act as a result of it. Receivers who view one viral marketing campaign as spam, block the messages sender or delete the message are likely to view all campaigns as spam, no matter which type of viral marketing campaign they receive. This pattern does not exist for those who choose to forward viral messages, nor does the strength of the feeling matter. However, we are able to distinguish that campaigns based on sadness, anger, fear and disgust are forwarded more than campaigns based on other feelings. These types of campaigns are often forwarded by women. Campaigns based on sick humor or surprise are more commonly forwarded by men. However, women are more likely to forward viral messages.


Virusmarknadsföring är en typ av marknadsföring som går ut på att individer inom sociala nätverk skickar ett budskap vidare till varandra. När marknadsföringsformen fungerar är den mycket billig och effektiv. Det finns ett flertal exempel på framgångsrika virusmarknadsföringskampanjer som lyckats och gett produkter eller företag stor framgång.

Virusmarknadsföringen som fenomen är relativt outforskad och det finns olika förslag på vägar att gå för att forma en framgångsrik kampanj. Bland annat kan virusmarknadsförare spela på individers känslor för att få dem att skicka ett budskap vidare inom sitt sociala nätverk. I denna uppsats tittar vi närmare på just detta, och tar reda på vilka känslor som bäst lämpar sig för en virusmarknadsföringskampanj. Genom en kvantitativ undersökning där cirka 800 studenter från Blekinge Tekniska Högskola och Högskolan i Gävle deltagit tar vi också reda på hur synen på virusmarknadsföring ser ut, samt vilken reaktion det får när det mottas.

Detta examensarbete visar att de som tar emot virusmarknadsföring har ett mönster i sitt agerande. De som ser en virusmarknadsföringskampanj som spam eller till och med blockerar avsändaren, gör detsamma för oberoende av vilken typ av kampanj de mottar. Samma sak gäller de som väljer att radera meddelandet. Mönstret i agerandet gäller dock inte för de som skickar budskap vidare. Hur stark känsla som väcks av kampanjen spelar heller ingen roll. Dock tycker vi oss se att virusmarknadsföringskampanjer baserade på sorg, ilska, rädsla och äckel fungerar bättre än andra kampanjer. Dessa skickas oftast vidare av kvinnor. Män skickar inte virusmarknadsföringskampanjer vidare lika ofta som kvinnor. När de gör det så väljer de dock att skicka vidare sjuk humor eller budskap baserade på förvåning.

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Рабізо, А. В. "Інформаційна технологія фільтрації спам-повідомлень." Thesis, Сумський державний університет, 2017. http://essuir.sumdu.edu.ua/handle/123456789/64946.

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

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Martins, Da Cruz José Márcio. "Contribution au classement statistique mutualisé de messages électroniques (spam)." Phd thesis, École Nationale Supérieure des Mines de Paris, 2011. http://pastel.archives-ouvertes.fr/pastel-00637173.

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Depuis la fin des années 90, les différentes méthodes issues de l'apprentissage artificiel ont été étudiées et appliquées au problème de classement de messages électroniques (filtrage de spam), avec des résultats très bons, mais pas parfaits. Il a toujours été considéré que ces méthodes étaient adaptées aux solutions de filtrage orientées vers un seul destinataire et non pas au classement des messages d'une communauté entière. Dans cette thèse notre démarche a été, d'abord, de chercher à mieux comprendre les caractéristiques des données manipulées, à l'aide de corpus réels de messages, avant de proposer des nouveaux algorithmes. Puis, nous avons utilisé un classificateur à régression logistique avec de l'apprentissage actif en ligne - pour démontrer empiriquement qu'avec un algorithme simple et une configuration d'apprentissage mieux adaptée au contexte réel de classement, on peut obtenir des résultats aussi bons que ceux que l'on obtient avec des algorithmes plus complexes. Nous avons aussi démontré, avec des ensembles de messages d'un petit groupe d'utilisateurs, que la perte d'efficacité peut ne pas être significative dans un contexte de classement mutualisé.
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Cruz, José Marcio Martins da. "Contribution au classement statistique mutualisé de messages électroniques (spam)." Paris, ENMP, 2011. https://pastel.archives-ouvertes.fr/pastel-00637173.

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Depuis la fin des années 90, les différentes méthodes issues de l'apprentissage artificiel ont été étudiées et appliquées au problème de classement de messages électroniques (filtrage de spam), avec des résultats très bons, mais pas parfaits. Il a toujours été considéré que ces méthodes étaient adaptées aux solutions de filtrage orientées vers un seul destinataire et non pas au classement des messages d'une communauté entière. Dans cette thèse notre démarche a été, d'abord, de chercher à mieux comprendre les caractéristiques des données manipulées, à l'aide de corpus réels de messages, avant de proposer des nouveaux algorithmes. Puis, nous avons utilisé un classificateur à régression logistique avec de l'apprentissage actif en ligne - pour démontrer empiriquement qu'avec un algorithme simple et une configuration d'apprentissage mieux adaptée au contexte réel de classement, on peut obtenir des résultats aussi bons que ceux que l'on obtient avec des algorithmes plus complexes. Nous avons aussi démontré, avec des ensembles de messages d'un petit groupe d'utilisateurs, que la perte d'efficacité peut ne pas être significative dans un contexte de classement mutualisé
Since the 90's, different machine learning methods were investigated and applied to the email classification problem (spam filtering), with very good but not perfect results. It was always considered that these methods are well adapted to filter messages to a single user and not filter to messages of a large set of users, like a community. Our approach was, at first, look for a better understanding of handled data, with the help of a corpus of real messages, before studying new algorithms. With the help of a logistic regression classifier with online active learning, we could show, empirically, that with a simple classification algorithm coupled with a learning strategy well adapted to the real context it's possible to get results which are as good as those we can get with more complex algorithms. We also show, empirically, with the help of messages from a small group of users, that the efficiency loss is not very high when the classifier is shared by a group of users
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Cheung, Pak-to Patrick, and 張伯陶. "A study on combating the problem of unsolicited electronic messages inHong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38608248.

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Rajdev, Meet. "Fake and Spam Messages: Detecting Misinformation During Natural Disasters on Social Media." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4462.

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During natural disasters or crises, users on social media tend to easily believe contents of postings related to the events, and retweet the postings, hoping that the postings will be reached by many other users. Unfortunately, there are malicious users who understand the tendency and post misinformation such as spam and fake messages with expecting wider propagation. To resolve the problem, in this paper we conduct a case study of the 2013 Moore Tornado and Hurricane Sandy. Concretely, we (i) understand behaviors of these malicious users; (ii) analyze properties of spam, fake and legitimate messages; (iii) propose at and hierarchical classification approaches; and (iv) detect both fake and spam messages with even distinguishing between them. Our experimental results show that our proposed approaches identify spam and fake messages with 96.43% accuracy and 0.961 F-measure.
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Laurent-Ricard, Eric. "Rétablir la confiance dans les messages électroniques : Le traitement des causes du "spam"." Thesis, Paris 2, 2011. http://www.theses.fr/2011PA020085/document.

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L'utilisation grandissante de la messagerie électronique dans les échanges dématérialisés, aussi bien pour les entreprises que pour les personnes physiques, et l'augmentation du nombre de courriers indésirables, nommés « spams » (pourriels) génèrent une perte de temps importante de traitement manuel, et un manque de confiance à la fois dans les informations transmises et dans les émetteurs de ces messages. Quels sont les solutions pour rétablir ou établir la confiance dans ces échanges ? Comment traiter et faire diminuer le nombre grandissant de « spams » ? Les solutions existantes sont parfois lourdes à mettre en oeuvre ou relativement peu efficaces et s’occupent essentiellement de traiter les effets du « spam », en oubliant d’analyser et de traiter les causes. L'identification, si ce n'est l'authentification de l'émetteur et des destinataires, est un des points clés permettant de valider l'origine d'un message et d’en garantir le contenu, aussi bien qu’un niveau important de traçabilité, mais ce n’est pas le seul, et les mécanismes de base mêmes de la messagerie électronique, plus précisément au niveau des protocoles de communication sont également en jeu. Le contenu de cette thèse portera plus spécifiquement sur les possibilités liées aux modifications de certains protocoles de l'Internet, en particulier le protocole SMTP, la mise en oeuvre de spécifications peu utilisées, et les outils et méthodes envisageables pour garantir l’identification des parties de façon simple et transparente pour les utilisateurs. L’objectif est de définir, d'une part une méthodologie d'utilisation de la messagerie pouvant assurer fiabilité et confiance, et d'autre part de rédiger les bases logiques de programmes clients et serveurs pour la mise en application de cette méthodologie
The growing use of email in dematerialized exchanges, for both businesses and individuals, and the increase of undesirable mails, called "spam" (junk emails) generate a significant loss of time of manual processing And a lack of confidence both in the information transmitted and the issuers of such messages. What are the solutions to restore or build confidence in these exchanges? How to treat and reduce the growing number of «spam»?Existing solutions are often cumbersome to implement or relatively ineffective and are primarily concerned with treating the effects of "«spam»", forgetting to analyze and address the causes.The identification, if not the authentication, of the sender and recipients, is a key point to validate the origin of a message and ensure the content, as well as a significant level of traceability, but it is not the only one, and the basic mechanisms, themselves, of the email system, more precisely in terms of communication protocols are also at stake.The content of this thesis will focus primarily on opportunities related to changes in some Internet protocols, in particular SMTP, implementation specifications rarely used, and the tools and possible methods to ensure the identification of parties in a simple and transparent way for users.The objective is to define, firstly a methodology for using the mail with reliability and confidence, and secondly to draw the logical foundations of client and server programs for the implementation of this methodology
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Kigerl, Alex Conrad. "An Empirical Assessment of the CAN SPAM Act." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/704.

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In January 2004, the United States Congress passed and put into effect the Controlling the Assault of Non-Solicited Pornography and Marketing Act (CAN SPAM). The Act was set forth to regulate bulk commercial email (spam) and set the limits for what was acceptable. Various sources have since investigated and speculated on the efficacy of the CAN SPAM Act, few of which report a desirable outcome for users of electronic mail. Despite the apparent consensus of anti-spam firms and the community of email users that the Act was less than effective, there is little to no research on the efficacy of the Act that utilizes any significant statistical rigor or accepted scientific practices. The present study seeks to determine what, if any, impact the CAN SPAM act had on spam messages, to identify areas of improvement to help fight spam that is both fraudulent and dangerous. The data consisted of 2,071,965 spam emails sent between February 1, 1998 and December 31, 2008. The data were aggregated by month and an interrupted time series design was chosen to assess the impact the CAN SPAM Act had on spam. Analyses revealed that the CAN SPAM Act had no observable impact on the amount of spam sent and received; no impact on two of three CAN SPAM laws complied with among spam emails, the remaining law of which there was a significant decrease in compliance after the Act; and no impact on the number of spam emails sent from within the United States. Implications of these findings and suggestions for policy are discussed.
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Kaushik, Saket. "Policy-controlled email services." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2937.

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Thesis (Ph. D.)--George Mason University, 2007.
Title from PDF t.p. (viewed Jan. 18, 2008). Thesis directors: Paul Amman, Duminda Wijesekera. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 198. Includes bibliographical references (p. 189-197). Also available in print.
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Books on the topic "Spam message"

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Spam kings: The real story behind the high-rolling hucksters pushing porn, pills and @*#?% enlargements. Sebastopol, Calif: O'Reilly, 2005.

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Torat ha-meser: Ben diṿur yashir le-"spam" = Online communication : from direct mail to spam. Ramat Gan: Ṿiṭal hotsaʼah le-or, 2012.

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O spam e as pragas digitais: Uma visão jurídico-tecnológica. São Paulo: Editora LTr, 2009.

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

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United, States Congress Senate Committee on Commerce Science and Transportation. Can-Spam Act of 2003: Report of the Committee on Commerce, Science, and Transportation on S. 877. Washington: U.S. G.P.O., 2003.

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Unsolicited Commercial Electronic Mail Act of 2000: Report (to accompany H.R. 3113) (including cost estimate of the Congressional Budget Office). [Washington, D.C: U.S. G.P.O., 2000.

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Can-Spam Act of 2003: Report of the Committee on Commerce, Science, and Transportation on S. 877. Washington: U.S. G.P.O., 2003.

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United States. Congress. House. Committee on Commerce. Unsolicited Commercial Electronic Mail Act of 2001: Report together with additional views (to accompany H.R. 718). [Washington, D.C: U.S. G.P.O., 2001.

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Commerce, United States Congress House Committee on. Unsolicited Commercial Electronic Mail Act of 2000: Report (to accompany H.R. 3113) (including cost estimate of the Congressional Budget Office). [Washington, D.C: U.S. G.P.O., 2000.

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United, States Congress Senate Committee on Commerce Science and Transportation. Can-Spam Act of 2003: Report of the Committee on Commerce, Science, and Transportation on S. 877. Washington: U.S. G.P.O., 2003.

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Book chapters on the topic "Spam message"

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Duan, Zhenhai, Yingfei Dong, and Kartik Gopalan. "DMTP: Controlling Spam Through Message Delivery Differentiation." In NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems, 355–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753810_30.

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Sartaj, Sahil, and Ayatullah Faruk Mollah. "An Intelligent System for Spam Message Detection." In Algorithms for Intelligent Systems, 387–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2248-9_37.

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Yang, Yitao, Runqiu Hu, Chengyan Qiu, Guozi Sun, and Huakang Li. "A Spam Message Detection Model Based on Bayesian Classification." In Advances in Internetworking, Data & Web Technologies, 424–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59463-7_42.

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Chen, Pei-Chi, Hahn-Ming Lee, Hsiao-Rong Tyan, Jain-Shing Wu, and Te-En Wei. "Detecting Spam on Twitter via Message-Passing Based on Retweet-Relation." In Technologies and Applications of Artificial Intelligence, 56–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13987-6_6.

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Saeed, Waddah. "Comparison of Automated Machine Learning Tools for SMS Spam Message Filtering." In Communications in Computer and Information Science, 307–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8059-5_18.

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Jimoh, Rasheed G., Kayode S. Adewole, Tunbosun E. Aderemi, and Abdullateef O. Balogun. "Investigative Study of Unigram and Bigram Features for Short Message Spam Detection." In International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020), 70–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80216-5_6.

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Chen, Cong-Jie, Yi-Dong Cui, and Ting Xie. "Study of Spam Short Message Filtering Based on Features Selection of Key Words." In Communications in Computer and Information Science, 646–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_79.

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Wu, Fangzhao, and Yongfeng Huang. "Chapter 9 Social Spammer and Spam Message Detection in an Online Social Network: A Codetection Approach." In Social Network Analysis, 225–42. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9781315369594-10.

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Zheng, Zhiyong. "Shannon Theory." In Financial Mathematics and Fintech, 91–151. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0920-7_3.

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AbstractAccording to Shannon, a message x is a random event. Let p(x) be the probability of occurrence of event x. If $$p(x)=0$$ p ( x ) = 0 , this event does not occur; If $$p(x)=1$$ p ( x ) = 1 , this event must occur. When $$p(x) = 0$$ p ( x ) = 0 or $$p(x) = 1$$ p ( x ) = 1 , information x can be called trivial information or spam information. Therefore, the real mathematical significance of information x lies in its uncertainty, that is $$0<p(x)<1$$ 0 < p ( x ) < 1 . Quantitative research on the uncertainty of nontrivial information constitutes all the starting point of Shannon’s theory, this starting point is now called information quantity or information entropy, or entropy for short. Shannon and his colleagues at Bell laboratory considered “bit” as the basic quantitative unit of information. What is “bit”? We can simply understand it as the number of bits in the binary system. However, according to Shannon, the binary system with n digits can express up to $$2^{n}$$ 2 n numbers. From the point of view of probability and statistics, the probability of occurrence of these $$2^{n}$$ 2 n numbers is $$\frac{1}{2^{n}}$$ 1 2 n . Therefore, a bit is the amount of information contained in event x with probability $$\frac{1}{2}$$ 1 2 . Taking this as the starting point, Shannon defined the self information I(x) contained in an information x as
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Goswami, Gaurav, Richa Singh, and Mayank Vatsa. "Automated Spam Detection in Short Text Messages." In Machine Intelligence and Signal Processing, 85–98. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2625-3_8.

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Conference papers on the topic "Spam message"

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Fan, Wen-chan. "Spam Message Recognition Based on Content." In 2011 International Conference on Computational and Information Sciences (ICCIS). IEEE, 2011. http://dx.doi.org/10.1109/iccis.2011.257.

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Duan, Longzhen, Nan Li, and Longjun Huang. "A New Spam Short Message Classification." In 2009 First International Workshop on Education Technology and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.299.

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Lee, Hyun-Young, and Seung-Shik Kang. "Word Embedding Method of SMS Messages for Spam Message Filtering." In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2019. http://dx.doi.org/10.1109/bigcomp.2019.8679476.

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Mathew, Kuruvilla, and Biju Issac. "Intelligent spam classification for mobile text message." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6181918.

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Lueg, Christopher. "Spam and Anti-Spam Measures: A Look at Potential Impacts." In 2003 Informing Science + IT Education Conference. Informing Science Institute, 2003. http://dx.doi.org/10.28945/2729.

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The proliferation of unrestricted Internet access has brought the community spam which has become a serious problem costing companies billions of dollars per annum. Typical anti-spam measures, such as filtering and blocking techniques, exist but focus on solving the spam problem on the message transportation level. Using such techniques may have impacts beyond the realm of spam-filters and block lists. In this paper we argue that implementing typical anti-spam measures means that computers are assigned the power to assess legitimacy of email. This means, for example, that legitimate email might be rejected because the sender used the 'wrong' mail server or the wrong terminology. In this paper, we describe some of the core problems and discuss alternatives.
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Dewi, Fatia Kusuma, Mgs M. Rizqi Fadhlurrahman, Mohamad Dwiyan Rahmanianto, and Rahmad Mahendra. "Multiclass SMS message categorization: Beyond spam binary classification." In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2017. http://dx.doi.org/10.1109/icacsis.2017.8355035.

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Chen, Hao. "Spam Message Filtering Recognition System Based on TensorFlow." In 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE, 2018. http://dx.doi.org/10.1109/icmcce.2018.00124.

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He, Peizhou, Yong Sun, Wei Zheng, and Xiangming Wen. "Filtering Short Message Spam of Group Sending Using CAPTCHA." In First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008). IEEE, 2008. http://dx.doi.org/10.1109/wkdd.2008.131.

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Kumar, D. Ganesh, M. Kameswara Rao, and K. Premnath. "A Recurrent Neural Network Model for Spam Message Detection." In 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. http://dx.doi.org/10.1109/icces48766.2020.9137940.

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Lahmadi, Abdelkader, Laurent Delosieres, and Olivier Festor. "Hinky: Defending against Text-Based Message Spam on Smartphones." In ICC 2011 - 2011 IEEE International Conference on Communications. IEEE, 2011. http://dx.doi.org/10.1109/icc.2011.5962722.

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