Relevant bibliographies by topics / SMS spam filtering

Contents

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

Academic literature on the topic 'SMS spam filtering'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "SMS spam filtering"

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Mehta, Riya, and Ankita Gandhi. "A Survey: SMS Spam Filtering." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2672–77. http://dx.doi.org/10.31142/ijtsrd12850.

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Ma, Jialin, Yongjun Zhang, Zhijian Wang, and Kun Yu. "A Message Topic Model for Multi-Grain SMS Spam Filtering." International Journal of Technology and Human Interaction 12, no. 2 (2016): 83–95. http://dx.doi.org/10.4018/ijthi.2016040107.

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At present, content-based methods are regard as the more effective in the task of Short Message Service (SMS) spam filtering. However, they usually use traditional text classification technologies, which are more suitable to deal with normal long texts; therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. In addition, the existing SMS spam filtering methods usually consider the SMS spam task as a binary-class problem, which could not provide for different categories for multi-grain SMS spam filtering. In this paper, the authors propose a message topic model (MTM) for multi-grain SMS spam filtering. The MTM derives from the famous probability topic model, and is improved in this paper to make it more suitable for SMS spam filtering. Finally, the authors compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.
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Delany, Sarah Jane, Mark Buckley, and Derek Greene. "SMS spam filtering: Methods and data." Expert Systems with Applications 39, no. 10 (2012): 9899–908. http://dx.doi.org/10.1016/j.eswa.2012.02.053.

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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 (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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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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DOGAN, Turgut. "On Term Weighting for Spam SMS Filtering." Sakarya University Journal of Computer and Information Sciences 3, no. 3 (2020): 239–49. http://dx.doi.org/10.35377/saucis.03.03.735463.

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Zhang Ye. "The SMS spam filtering based on Adaboost." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 7 (2013): 843–50. http://dx.doi.org/10.4156/aiss.vol5.issue7.99.

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Nagwani, Naresh Kumar, and Aakanksha Sharaff. "SMS spam filtering and thread identification using bi-level text classification and clustering techniques." Journal of Information Science 43, no. 1 (2016): 75–87. http://dx.doi.org/10.1177/0165551515616310.

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SMS spam detection is an important task where spam SMS messages are identified and filtered. As greater numbers of SMS messages are communicated every day, it is very difficult for a user to remember and correlate the newer SMS messages received in context to previously received SMS. SMS threads provide a solution to this problem. In this work the problem of SMS spam detection and thread identification is discussed and a state of the art clustering-based algorithm is presented. The work is planned in two stages. In the first stage the binary classification technique is applied to categorize SMS messages into two categories namely, spam and non-spam SMS; then, in the second stage, SMS clusters are created for non-spam SMS messages using non-negative matrix factorization and K-means clustering techniques. A threading-based similarity feature, that is, time between consecutive communications, is described for the identification of SMS threads, and the impact of the time threshold in thread identification is also analysed experimentally. Performance parameters like accuracy, precision, recall and F-measure are also evaluated. The SMS threads identified in this proposed work can be used in applications like SMS thread summarization, SMS folder classification and other SMS management-related tasks.
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Abdulhamid, Shafi'I Muhammad, Muhammad Shafie Abd Latiff, Haruna Chiroma, et al. "A Review on Mobile SMS Spam Filtering Techniques." IEEE Access 5 (2017): 15650–66. http://dx.doi.org/10.1109/access.2017.2666785.

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Taufiq Nuruzzaman, M., Changmoo Lee, Mohd Fikri Azli bin Abdullah, and Deokjai Choi. "Simple SMS spam filtering on independent mobile phone." Security and Communication Networks 5, no. 10 (2012): 1209–20. http://dx.doi.org/10.1002/sec.577.

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Dissertations / Theses on the topic "SMS spam filtering"

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

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Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries. This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time. The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter. Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.
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Bäckman, David. "EVALUATION OF MACHINE LEARNING ALGORITHMS FOR SMS SPAM FILTERING." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163188.

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The purpose of this thesis is to evaluate different machine learning algorithms and methods for text representation in order to determine what is best suited to use to distinguish between spam SMS and legitimate SMS. A data set that contains 5573 real SMS has been used to train the algorithms K-Nearest Neighbor, Support Vector Machine, Naive Bayes and Logistic Regression. The different methods that have been used to represent text are Bag of Words, Bigram and Word2Vec. In particular, it has been investigated if semantic text representations can improve the performance of classification. A total of 12 combinations have been evaluated with help of the metrics accuracy and F1-score.The results shows that Logistic Regression together with Bag of Words reach the highest accuracy and F1-score. Bigram as text representation seems to work worse then the others methods. Word2Vec can increase the performnce for K-Nearst Neigbor but not for the other algorithms.
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Silva, Tiago Pasqualini da. "Normalização textual e indexação semântica aplicadas da filtragem de SMS spam." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8811.

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Submitted by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:19Z No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5)<br>Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:26Z (GMT) No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5)<br>Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T17:49:32Z (GMT) No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5)<br>Made available in DSpace on 2017-06-01T17:49:38Z (GMT). No. of bitstreams: 1 SILVA_Tiago_2016.pdf: 13631569 bytes, checksum: 7774c3913aa556cc48c0669f686cd3b5 (MD5) Previous issue date: 2016-07-01<br>Não recebi financiamento<br>The rapid popularization of smartphones has contributed to the growth of SMS usage as an alternative way of communication. The increasing number of users, along with the trust they inherently have in their devices, makes SMS messages a propitious environment for spammers. In fact, reports clearly indicate that volume of mobile phone spam is dramatically increasing year by year. SMS spam represents a challenging problem for traditional filtering methods nowadays, since such messages are usually fairly short and normally rife with slangs, idioms, symbols and acronyms that make even tokenization a difficult task. In this scenario, this thesis proposes and then evaluates a method to normalize and expand original short and messy SMS text messages in order to acquire better attributes and enhance the classification performance. The proposed text processing approach is based on lexicography and semantic dictionaries along with the state-of-the-art techniques for semantic analysis and context detection. This technique is used to normalize terms and create new attributes in order to change and expand original text samples aiming to alleviate factors that can degrade the algorithms performance, such as redundancies and inconsistencies. The approach was validated with a public, real and non-encoded dataset along with several established machine learning methods. The experiments were diligently designed to ensure statistically sound results which indicate that the proposed text processing techniques can in fact enhance SMS spam filtering.<br>A popularização dos smartphones contribuiu para o crescimento do uso de mensagens SMS como forma alternativa de comunicação. O crescente número de usuários, aliado à confiança que eles possuem nos seus dispositivos tornam as mensagem SMS um ambiente propício aos spammers. Relatórios recentes indicam que o volume de spam enviados via SMS está aumentando vertiginosamente nos últimos anos. SMS spam representa um problema desafiador para os métodos tradicionais de detecção de spam, uma vez que essas mensagens são curtas e geralmente repletas de gírias, símbolos, abreviações e emoticons, que torna até mesmo a tokenização uma tarefa difícil. Diante desse cenário, esta dissertação propõe e avalia um método para normalizar e expandir amostras curtas e ruidosas de mensagens SMS de forma a obter atributos mais representativos e, com isso, melhorar o desempenho geral na tarefa de classificação. O método proposto é baseado em dicionários lexicográficos e semânticos e utiliza técnicas modernas de análise semântica e detecção de contexto. Ele é empregado para normalizar os termos que compõem as mensagens e criar novos atributos para alterar e expandir as amostras originais de texto com o objetivo de mitigar fatores que podem degradar o desempenho dos métodos de classificação, tais como redundâncias e inconsistências. A proposta foi avaliada usando uma base de dados real, pública e não codificada, além de vários métodos consagrados de aprendizado de máquina. Os experimentos foram conduzidos para garantir resultados estatisticamente corretos e indicaram que o método proposto pode de fato melhorar a detecção de spam em SMS.
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Adrian, Angelia Melani, and 洪美蘭. "A Challenge Response System for Filtering Automated SMS Spam." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/42665923533935956511.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>98<br>Nowadays SMS Spam start becomes a big problem, especially in country such as China, Korea, and Vietnam. Usually the SMS Spam is sent by computer program or bot. Many researchers try to address this problem using Turing test with the conjunction of whitelist and blacklist. They focus on using CAPTCHA as the Turing test. This thesis want to try to address the problem in SMS SPAM by using another type of Turing test called Challenge Response System. The C/R System works as follows; when a message is sent by the sender, it will send to the SMSC first, and the SMSC will send the challenge questions to sender. Sender will reply the answer and SMSC will verify it. If the answer is correct then SMSC will forward the message to destination otherwise if the answer is wrong then SMSC will delete the message. The result from the experimental evaluation is quite good. The successful percentage rate for human user to pass is 94 % as the lowest rate and 100% as the highest rate, while for the machine is 0%. This result indicating that the tests are difficult enough to block automated SMS spammers. We also compare this work with previous work by some researcher in SMS Spam area and this work has some advantages compare to the previous work.
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Book chapters on the topic "SMS spam filtering"

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Vishwakarma, Arvind Kumar, Mohd Dilshad Ansari, and Gaurav Rai. "SMS Spam Filtering Using Machine Learning Technique." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_66.

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Prasanna Bharathi, P., G. Pavani, K. Krishna Varshitha, and Vaddi Radhesyam. "Spam SMS Filtering Using Support Vector Machines." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_53.

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Serrano, José M. Bande, José Hernández Palancar, and René Cumplido. "The Evaluation of Ordered Features for SMS Spam Filtering." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_47.

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Joe, Inwhee, and Hyetaek Shim. "An SMS Spam Filtering System Using Support Vector Machine." In Future Generation Information Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_56.

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Al Moubayed, Noura, Toby Breckon, Peter Matthews, and A. Stephen McGough. "SMS Spam Filtering Using Probabilistic Topic Modelling and Stacked Denoising Autoencoder." In Artificial Neural Networks and Machine Learning – ICANN 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_50.

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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. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5780-9_2.

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Bouarara, Hadj Ahmed. "Enhanced Bio-Inspired Algorithms for Detecting and Filtering Spam." In Global Implications of Emerging Technology Trends. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4944-4.ch011.

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The internet era promotes electronic commerce and facilitates access to many services. In today's digital society, the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This chapter unveils fresh bio-inspired techniques (artificial social cockroaches [ASC], artificial haemostasis system [AHS], and artificial heart lungs system [AHLS]) and their application for SPAM detection. For the experimentation, the authors used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy, and error). They optimize the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine-learning algorithms (decision tree C4.5 and K-means).
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Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "New Bio Inspired Techniques in the Filtering of Spam." In Robotic Systems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch037.

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The internet era promotes electronic commerce and facilitates access to many services. In today's digital society the explosion in communication has revolutionized the field of electronic communication. Unfortunately, this technology has become incontestably the original source of malicious activities, especially the plague called undesirables email (SPAM) that has grown tremendously in the last few years. This paper deals on the unveiling of fresh bio-inspired techniques (artificial social cockroaches (ASC), artificial haemostasis system (AHS) and artificial heart lungs system (AHLS)) and their application for SPAM detection. For the authors' experimentation, they have used the benchmark SMS Spam corpus V.0.1 and the validation measures (recall, precision, f-measure, entropy, accuracy and error). They have optimising the sensitive parameters of each algorithm (text representation technique, distance measure, weightings, and threshold). The results are positive compared to the result of artificial social bees and machine learning algorithms (decision tree C4.5 and K-means).
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Conference papers on the topic "SMS spam filtering"

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Khemapatapan, Chaiyaporn. "Thai-English spam SMS filtering." In 2010 16th Asia-Pacific Conference on Communications (APCC). IEEE, 2010. http://dx.doi.org/10.1109/apcc.2010.5679770.

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Gómez Hidalgo, José María, Guillermo Cajigas Bringas, Enrique Puertas Sánz, and Francisco Carrero García. "Content based SMS spam filtering." In the 2006 ACM symposium. ACM Press, 2006. http://dx.doi.org/10.1145/1166160.1166191.

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Nuruzzaman, M. Taufiq, Changmoo Lee, and Deokjai Choi. "Independent and Personal SMS Spam Filtering." In 2011 IEEE 11th International Conference on Computer and Information Technology (CIT). IEEE, 2011. http://dx.doi.org/10.1109/cit.2011.23.

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Androulidakis, Iosif, Vasileios Vlachos, and Alexandros Papanikolaou. "Spam goes mobile: Filtering unsolicited SMS traffic." In 2012 20th Telecommunications Forum Telfor (TELFOR 2012). IEEE, 2012. http://dx.doi.org/10.1109/telfor.2012.6419492.

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Ma, Jialin, Yongjun Zhang, Jinling Liu, Kun Yu, and XuAn Wang. "Intelligent SMS Spam Filtering Using Topic Model." In 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS). IEEE, 2016. http://dx.doi.org/10.1109/incos.2016.47.

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Uysal, Alper Kursat, Serkan Gunal, Semih Ergin, and Efnan Sora Gunal. "A novel framework for SMS spam filtering." In 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2012. http://dx.doi.org/10.1109/inista.2012.6246947.

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Cormack, Gordon V., José María Gómez Hidalgo, and Enrique Puertas Sánz. "Feature engineering for mobile (SMS) spam filtering." In the 30th annual international ACM SIGIR conference. ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277951.

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Taheri, Rahim, and Reza Javidan. "Spam filtering in SMS using recurrent neural networks." In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017. http://dx.doi.org/10.1109/aisp.2017.8515158.

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Pham, Thai-Hoang, and Phuong Le-Hong. "Content-based approach for Vietnamese spam SMS filtering." In 2016 International Conference on Asian Language Processing (IALP). IEEE, 2016. http://dx.doi.org/10.1109/ialp.2016.7875930.

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Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. "Contributions to the study of SMS spam filtering." In the 11th ACM symposium. ACM Press, 2011. http://dx.doi.org/10.1145/2034691.2034742.

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