Relevant bibliographies by topics / Spam messages

Contents

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

Academic literature on the topic 'Spam messages'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Spam messages"

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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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Prasad, K. Munivara, A. Rama Mohan Reddy, and K. Venugopal Rao. "Efficient Detection of SPAM messages and SPAM zombies in the Internet using Naïve-Bayesian and Sequential Probability Ratio Test (SPRT)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 15, 2013): 539–48. http://dx.doi.org/10.24297/ijct.v7i2.3455.

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The Internet is a global system of interconnected computer networks that provides the communication to serve billions of users worldwide. Compromised machines in the internet allows the attackers to launch various security attacks such as DDoS, spamming, and identity theft. Compromised machines are the one of the major security threat on the internet. In this paper we address this issue by using Naïve-Bayesian and SPRT to automatically identify compromised machines in a network. Spamming allows the attackers to recruit the large number of compromised machines to generate the SPAM messages by hiding the identity, these compromised machines commonly known as spam zombies. We used Naïve-Bayesian and manual methods to detect the SPAM messages and used SPRT technique to identify the spam zombies from the SPAM messages. We proved that the Naïve-Bayesian approach minimizes the error rate, false positives and false negatives compared to the manual approach in the process of detecting SPAM message. Our evaluation studies based on one day email trace collected in our organization network that shows Naïve-Bayesian and SPRT are the effective and efficient systems in automatically detecting SPAM messages and compromised machines in a network.
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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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Jalal Mussa, Diyari, and Noor Ghazi M. Jameel. "Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm." Kurdistan Journal of Applied Research 4, no. 2 (November 21, 2019): 110–20. http://dx.doi.org/10.24017/science.2019.2.11.

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In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were extracted from Tiago’s dataset, which is the most frequently used dataset by researchers. For selecting the optimal relevant features, two different types of wrapper feature selection algorithms were used in order to reduce and select best relevant features. The accuracy and performance obtained by the selected features via sequential backward selection method was better comparing to sequential forward selection method. The extracted nine optimal features can be a good representation of a spam SMS message. Additionally, the classification accuracy obtained by the proposed method using nine optimal features with XGBoost algorithm is 98.64 using 10-fold cross validation.
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Setiyono, Agus, and Hilman F. Pardede. "KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE." Jurnal Pilar Nusa Mandiri 15, no. 2 (September 8, 2019): 275–80. http://dx.doi.org/10.33480/pilar.v15i2.693.

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It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.
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Wu, Hongli, and Yong Hui Jiang. "SMS Spam Filtering Based on “Cloud Security”." Applied Mechanics and Materials 263-266 (December 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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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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Ratniasih, Ni Luh, Made Sudarma, and Nyoman Gunantara. "PENERAPAN TEXT MINING DALAM SPAM FILTERING UNTUK APLIKASI CHAT." Majalah Ilmiah Teknologi Elektro 16, no. 3 (December 29, 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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Dissertations / Theses on the topic "Spam messages"

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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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Sroufe, Paul Dantu Ram. "E-shape analysis." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/ark:/67531/metadc12201.

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Sroufe, Paul. "E‐Shape Analysis." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12201/.

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The motivation of this work is to understand E-shape analysis and how it can be applied to various classification tasks. It has a powerful feature to not only look at what information is contained, but rather how that information looks. This new technique gives E-shape analysis the ability to be language independent and to some extent size independent. In this thesis, I present a new mechanism to characterize an email without using content or context called E-shape analysis for email. I explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering and social-context based finger printing. The second part of this thesis takes what I apply E-shape analysis to activity recognition of humans. Using the Android platform and a T-Mobile G1 phone I collect data from the triaxial accelerometer and use it to classify the motion behavior of a subject.
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Books on the topic "Spam messages"

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

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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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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 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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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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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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Book chapters on the topic "Spam messages"

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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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Ezpeleta, Enaitz, Urko Zurutuza, and José María Gómez Hidalgo. "Short Messages Spam Filtering Using Sentiment Analysis." In Text, Speech, and Dialogue, 142–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45510-5_17.

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Zainal, Kamahazira, and Mohd Zalisham Jali. "A Review of Feature Extraction Optimization in SMS Spam Messages Classification." In Communications in Computer and Information Science, 158–70. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2777-2_14.

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Choudhary, Neelam, and Ankit Kumar Jain. "Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique." In Communications in Computer and Information Science, 18–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5780-9_2.

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Harsule, Sneha R., and Mininath K. Nighot. "N-Gram Classifier System to Filter Spam Messages from OSN User Wall." In Advances in Intelligent Systems and Computing, 21–28. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0419-3_3.

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Kiliroor, Cinu C., and C. Valliyammai. "Social Context Based Naive Bayes Filtering of Spam Messages from Online Social Networks." In Soft Computing in Data Analytics, 699–706. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_66.

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Zainal, Kamahazira, Mohd Zalisham Jali, and Abu Bakar Hasan. "Comparative Analysis of Danger Theory Variants in Measuring Risk Level for Text Spam Messages." In Advances in Intelligent Systems and Computing, 133–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78753-4_11.

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Kiliroor, Cinu C., and C. Valliyammai. "Binary and Continuous Feature Engineering Analysis on Twitter Data Stream for Classification of Spam Messages." In Lecture Notes in Electrical Engineering, 581–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0829-5_55.

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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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Conference papers on the topic "Spam messages"

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Cormack, Gordon V., José María Gómez Hidalgo, and Enrique Puertas Sánz. "Spam filtering for short messages." In the sixteenth ACM conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1321440.1321486.

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Agarwal, Sakshi, Sanmeet Kaur, and Sunita Garhwal. "SMS spam detection for Indian messages." In 2015 1st International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2015. http://dx.doi.org/10.1109/ngct.2015.7375198.

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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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Cai, Jie, Yuezhong Tang, and Rile Hu. "Spam Filter for Short Messages Using Winnow." In 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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Duan, Z., P. Chen, F. Sanchez, Y. Dong, M. Stephenson, and J. Barker. "Detecting Spam Zombies by Monitoring Outgoing Messages." In 2009 Proceedings IEEE INFOCOM. IEEE, 2009. http://dx.doi.org/10.1109/infcom.2009.5062096.

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Ezpeleta, Enaitz, Urko Zurutuza, and José María Gómez Hidalgo. "Short Messages Spam Filtering Using Personality Recognition." In CERI '16: 4th Spanish Conference in Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2934732.2934742.

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Raj, R. Jeberson Retna, Senduru Srinivasulu, and Aldrin Ashutosh. "A Multi-classifier Framework for Detecting Spam and Fake Spam Messages in Twitter." In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2020. http://dx.doi.org/10.1109/csnt48778.2020.9115796.

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Vipin, N. S., and M. Abdul Nizar. "Efficient on-line SPAM filtering for encrypted messages." In 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091540.

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Uysal, Alper Kursat, Serkan Gunal, Semih Ergin, and Efnan Sora Gunal. "Detection of SMS spam messages on mobile phones." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204485.

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McElligott, Adrian E. "A security pass for messages." In the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2030376.2030398.

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