Journal articles: '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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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 NaÃ¯ve-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 NaÃ¯ve-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 NaÃ¯ve-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 NaÃ¯ve-Bayesian and SPRT are the effective and efficient systems in automatically detecting SPAM messages and compromised machines in a network.

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Ojugo, Arnold Adimabua, and David Ademola Oyemade. "Boyer Moore string-match framework for a hybrid short message service spam filtering technique." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 519. http://dx.doi.org/10.11591/ijai.v10.i3.pp519-527.

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Advances in technology and the proliferation of mobile device have continued to advance the ubiquitous nature of computing alongside their many prowess and improved features it brings as a disruptive technology to aid information sharing amongst many online users. This popularity, usage and adoption ease, mobility, and portability of the mobile smartphone devices have allowed for its acceptability and popularity. Mobile smartphones continue to adopt the use of short messages services accompanied with a scenario for spamming to thrive. Spams are unsolicited message or inappropriate contents. An effective spam filter studies are limited as short-text message service (SMS) are 140bytes, 160-characters, and rippled with abbreviation and slangs that further inhibits the effective training of models. The study proposes a string match algorithm used as deep learning ensemble on a hybrid spam filtering technique to normalize noisy features, expand text and use semantic dictionaries of disambiguation to train underlying learning heuristics and effectively classify SMS into legitimate and spam classes. Study uses a profile hidden Markov network to select and train the network structure and employs the deep neural network as a classifier network structure. Model achieves an accuracy of 97% with an error rate of 1.2%.

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Duan, Zhenhai, Yingfei Dong, and Kartik Gopalan. "DMTP: Controlling spam through message delivery differentiation." Computer Networks 51, no. 10 (July 2007): 2616–30. http://dx.doi.org/10.1016/j.comnet.2006.11.015.

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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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Ezpeleta, Enaitz, Iñaki Garitano, Urko Zurutuza, and José María Gómez Hidalgo. "Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 175–89. http://dx.doi.org/10.1142/s0218488517400177.

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Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

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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 (April 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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Volkov, Andrey Anatol’evich, and Sergey Vladimirovich Antonov. "Algorithm of restoring unambiguity in the system of distance emergency alerts from persons with disabilities." Vestnik MGSU, no. 11 (November 2015): 186–92. http://dx.doi.org/10.22227/1997-0935.2015.11.186-192.

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Usually a message on fire or other emergency is sent to operations control by a witness. The situation causes stress. That’s why it may be difficult to understand the meaning of the witness’s text message because of pressing adjacent letters or T9 mistakes. So an operator may take such a message for spam and may not react adequately. Though if the system of “Smart House” is equipped with the module of processing Messages-112, the problem will be solved. The article analyzes the way of processing the messages to Messages-112 from persons with disabilities in the system of “Smart House”. The authors offer a variant of recovering unambiguity of notion sense from messages with errors of T9 and possible accidental pressing of adjacent letters. The system looks for key words, reduces noise, chooses the target rescue services and redirects the message to them.

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Qin, Jian, and Shi Qun Yin. "Research of Chinese Multi-Pattern Fuzzy Matching Method for SMS Spam Monitoring." Advanced Materials Research 532-533 (June 2012): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.748.

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In this paper, it is analyzed the content and achieving mechanism of WM algorithm on account of frequently asked questions during the monitoring and filtering of Short Message Service(SMS) spam, and brought forward a kind of Chinese message with multi- patterns fuzzy matching method to improve WM algorithm to apply to Chinese spam message filtering. The method firstly does pretreatment of key words and fuzzification of monitor for message on account of expressive diversity of Chinese message, and then realizes initial matching by improving WM algorithm. Because the preliminary result is not the final one, it needs further fuzzily match for the result. It does particular introduction of the method and the model of monitor and filtration for Chinese SMS spam in the paper. The method’s correctness and performing efficiency also has been done experimental analysis, test and comparison experiment with former system that had not been done pretreatment. Experimental results show the validity of the improving algorithms. The method is testing further during the practice.

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P, Mahesh, Basappa B. Kodada, and Shivakumar K. M. "Spam Control Mechanism using Identity based Message Admission." International Journal of Computer Applications 74, no. 3 (July 26, 2013): 24–31. http://dx.doi.org/10.5120/12865-9696.

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Xia, Hu, Yan Fu, Junlin Zhou, and Qi Xia. "Intelligent spam filtering for massive short message stream." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 32, no. 2 (March 2013): 586–96. http://dx.doi.org/10.1108/03321641311296963.

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Rani, T. Jhansi, T. Jaya Vumesh, P. Saiteja, V. Ajay Kumar Reddy, and M. Meghana. "SMS Spam Detection Framework Using Machine Learning Algorithms and Neural Networks." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 10–19. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.002.

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In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS’s (Short Message Service’s) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API’s. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS’s. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.

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KANARIS, IOANNIS, KONSTANTINOS KANARIS, IOANNIS HOUVARDAS, and EFSTATHIOS STAMATATOS. "WORDS VERSUS CHARACTER N-GRAMS FOR ANTI-SPAM FILTERING." International Journal on Artificial Intelligence Tools 16, no. 06 (December 2007): 1047–67. http://dx.doi.org/10.1142/s0218213007003692.

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The increasing number of unsolicited e-mail messages (spam) reveals the need for the development of reliable anti-spam filters. The vast majority of content-based techniques rely on word-based representation of messages. Such approaches require reliable tokenizers for detecting the token boundaries. As a consequence, a common practice of spammers is to attempt to confuse tokenizers using unexpected punctuation marks or special characters within the message. In this paper we explore an alternative low-level representation based on character n-grams which avoids the use of tokenizers and other language-dependent tools. Based on experiments on two well-known benchmark corpora and a variety of evaluation measures, we show that character n-grams are more reliable features than word-tokens despite the fact that they increase the dimensionality of the problem. Moreover, we propose a method for extracting variable-length n-grams which produces optimal classifiers among the examined models under cost-sensitive evaluation.

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Hamou, Reda Mohamed, and Abdelmalek Amine. "The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam." International Journal of Information Retrieval Research 3, no. 1 (January 2013): 43–59. http://dx.doi.org/10.4018/ijirr.2013010103.

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Spam is now seized of the Internet in phenomenal proportions since it high represents a percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article to develop a hybrid algorithm based primarily on the probabilistic model in this case Naïve Bayes for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes namely spam and ham. To determine the sensitive parameters that improve the classifications the authors are interested in studying the content of the messages by using a representation of messages by the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation opt to get a good classification. The authors have chosen several metrics evaluation to validate their results.

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Velammal B. L. and Aarthy N. "Improvised Spam Detection in Twitter Data Using Lightweight Detectors and Classifiers." International Journal of Web-Based Learning and Teaching Technologies 16, no. 4 (July 2021): 12–32. http://dx.doi.org/10.4018/ijwltt.20210701.oa2.

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Receiving spam messages is one of the most serious issues in social media, especially in Twitter, which is a widely used platform to reflect the opinions and emotions of an individual publicly as well as focused to a specific group of members with similar thoughts or discussion topic. In such focused discussion groups, getting spam message through social media sites is the most annoying issue. In this paper, a system is developed to detect spam tweets by using four lightweight detectors, namely blacklist domain detector, near duplicate detector, reliable ham detector, and multiclass detector. The detected tweets are then classified using ensemble classifiers such as naïve Bayes, logistic regression, and random forest. Voting method is applied to decide the labels for the tweets obtained after classification process. The proposed system has achieved an accuracy of 79% to detect spam tweets with the help of naïve Bayes classifier method and the value seems to be optimizing further with the availability of more sample data.

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Kim, Bum-Bae, and Hyoung-Kee Choi. "Spam Message Filtering with Bayesian Approach for Internet Communities." KIPS Transactions:PartC 13C, no. 6 (October 30, 2006): 733–40. http://dx.doi.org/10.3745/kipstc.2006.13c.6.733.

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Dmello, Acquin, Gaurang Mhatre, Rohan Lopes, and Haince Pen. "Spammer Detection by Extracting Message Parameters from Spam Emails." International Journal of Computer Applications 78, no. 10 (September 18, 2013): 21–25. http://dx.doi.org/10.5120/13526-1232.

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ZHANG, Yongjun, and Jinling LIU. "Spam short message classifier model based on word terms." Journal of Computer Applications 33, no. 5 (October 14, 2013): 1334–37. http://dx.doi.org/10.3724/sp.j.1087.2013.01334.

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Ezpeleta, Enaitz, Iñaki Velez de Mendizabal, José María Gómez Hidalgo, and Urko Zurutuza. "Novel email spam detection method using sentiment analysis and personality recognition." Logic Journal of the IGPL 28, no. 1 (January 14, 2020): 83–94. http://dx.doi.org/10.1093/jigpal/jzz073.

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Abstract Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. During the past years several techniques to detect unsolicited emails have been developed. This work provides means to validate the hypothesis that the identification of the email messages’ intention can be approached by sentiment analysis and personality recognition techniques. These techniques will provide new features that improve current spam classification techniques. We combine personality recognition and sentiment analysis techniques to analyse email content. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best email spam classifiers and filters to each dataset in order to compare results.

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Syahril, Muhammad Akbar Fhad. "Published Privacy Rights via Short Messages." Amsir Law Journal 3, no. 1 (October 9, 2021): 11–19. http://dx.doi.org/10.36746/alj.v3i1.45.

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Short messages in the form of advertisements are increasingly being accepted by the public through their cell phones. The public never specifically gave the phone number to the party sending the advertising message. This is considered to be even more annoying because the short message advertisement violates the principles of consumer protection. This study aims to determine and analyze the extent of privacy violations against the spread of spam information via short messages. This study uses the empirical normative method, namely research conducted with the approach of legal norms or substances, legal principles, legal postulates, and legal comparisons, using a conceptual approach. The results show that short messages in the form of offers that are not directly related to the services used by cellular subscribers must be a concern for customer convenience.

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Gao, Bibu, and Wenqiang Zhang. "A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering." Applied Sciences 11, no. 14 (July 9, 2021): 6350. http://dx.doi.org/10.3390/app11146350.

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As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.

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Alshahrani, Ali. "Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 16 (August 23, 2021): 52. http://dx.doi.org/10.3991/ijim.v15i16.24197.

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<p class="0abstract">SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.</p>

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Hamou, Reda Mohamed, Abdelmalek Amine, and Amine Boudia. "A New Meta-Heuristic Based on Social Bees for Detection and Filtering of Spam." International Journal of Applied Metaheuristic Computing 4, no. 3 (July 2013): 15–33. http://dx.doi.org/10.4018/ijamc.2013070102.

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Spam is now seized the Internet in phenomenal proportions since a high percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article experiencing a meta-heuristic based on social bees. The authors took inspiration from biological model of social bees and especially, their organization in the workplace, and collective intelligence. The authors chose this meta-heuristic because it presents effects allow the authors to detect the characteristics of unwanted data. Messages are indexed and represented by the n-gram words and characters independent of languages ??(because a message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining.

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A.A, Prof Shitole, Sonam H. Anpat, Pooja N. Bodhale, and Chaitrali B. Gaikwad. "Social Chat Application Include Security and Detect the Spam Message." IJARCCE 5, no. 12 (December 30, 2016): 131–33. http://dx.doi.org/10.17148/ijarcce.2016.51226.

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Hameed, Sarab M. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 596. http://dx.doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.

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Henke, Marcia, Eulanda Santos, Eduardo Souto, and Altair O. Santin. "Spam Detection Based on Feature Evolution to Deal with Concept Drift." JUCS - Journal of Universal Computer Science 27, no. 4 (April 28, 2021): 364–86. http://dx.doi.org/10.3897/jucs.66284.

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Electronic messages are still considered the most significant tools in business and personal applications due to their low cost and easy access. However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users. Several approaches have been proposed to detect spam, such as filters implemented in e-mail servers and user-based spam message classification mechanisms. A major problem with these approaches is spam detection in the presence of concept drift, especially as a result of changes in features over time. To overcome this problem, this work proposes a new spam detection system based on analyzing the evolution of features. The proposed method is divided into three steps: 1) spam classification model training; 2) concept drift detection; and 3) knowledge transfer learning. The first step generates classification models, as commonly conducted in machine learning. The second step introduces a new strategy to avoid concept drift: SFS (Similarity-based Features Se- lection) that analyzes the evolution of the features taking into account similarity obtained between the feature vectors extracted from training data and test data. Finally, the third step focuses on the following questions: what, how, and when to transfer acquired knowledge? The proposed method is evaluated using two public datasets. The results of the experiments show that it is possible to infer a threshold to detect changes (drift) in order to ensure that the spam classification model is updated through knowledge transfer. Moreover, our anomaly detection system is able to perform spam classification and concept drift detection as two parallel and independent tasks.

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Marathe, Aditi P. "Improving the Accuracy of Spam Message Filtering using Hybrid CNN Classification." International Journal of Emerging Trends in Engineering Research 8, no. 5 (May 25, 2020): 2194–98. http://dx.doi.org/10.30534/ijeter/2020/116852020.

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Kim, Tae-Hee, and Moon-Seol Kang. "Spam Message Filtering for Internet Communities using Collection and Frequency Analysis." KIPS Transactions:PartC 18C, no. 2 (April 30, 2011): 61–70. http://dx.doi.org/10.3745/kipstc.2011.18c.2.061.

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Oluwatoyin, Odukoya, Akinyemi Bodunde, Gooding Titus, and Aderounmu Ganiyu. "An Improved Machine Learning-Based Short Message Service Spam Detection System." International Journal of Computer Network and Information Security 11, no. 12 (December 8, 2019): 40–48. http://dx.doi.org/10.5815/ijcnis.2019.12.05.

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Ou, SooBin, and Jongwoo Lee. "Implementation of a Spam Message Filtering System using Sentence Similarity Measurements." KIISE Transactions on Computing Practices 23, no. 1 (January 15, 2017): 57–64. http://dx.doi.org/10.5626/ktcp.2017.23.1.57.

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Kang, Seung-Shik. "A Normalization Method of Distorted Korean SMS Sentences for Spam Message Filtering." KIPS Transactions on Software and Data Engineering 3, no. 7 (July 31, 2014): 271–76. http://dx.doi.org/10.3745/ktsde.2014.3.7.271.

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Lu, Li, Xiangui Xue, and Tao Li. "Application of Bayes Classification method in mobile phone spam short message filtering system." MATEC Web of Conferences 232 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201823201002.

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The paper discussed the use of Bayes classification method in filtration system of short message spam (SMS). The method can classify the content of SMS, thus realizing effective filtering. Finally the paper carried out the result analysis and the appraisal of the Bayes classification model, which testified the model has some actually feasibility and extensibility.

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Salomon, Simon, and Seng Hansun. "Spam Filter Situs Jejaring Sosial Mahasiswa Menggunakan Regular Expression." Jurnal ULTIMA InfoSys 8, no. 2 (April 2, 2018): 69–73. http://dx.doi.org/10.31937/si.v8i2.615.

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Spam is an unexpected and unsolicited email sent randomly indiscriminately, directly or indirectly by the sender who has no connection whatsoever with the recipient. The purpose of spam itself is to send information to the recipient, where the content of the sent message generally contains ads that offer nonessential products or illegal products, scams, promotional purposes, or spreading malware designed to hijack computers receiver. Based on the background of the problem, it is necessary anti-spam on a chat or dissemination of information in social networking using regular expression. From this study, the behavioral intention to use at level of 80% means that the user agrees that this website increases user interest in obtaining information and communication, and generates an immersion level of 80% which means the user is very focused when using the website. This website generates value by 98% precision and 98% recall that produce harmonic mean value of 97% so that it can be concluded that it has the precision and recall value harmonious. Index Terms—social networking, regular expression, spam, website

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Nazih, Waleed, Yasser Hifny, Wail Elkilani, Tamer Abdelkader, and Hossam Faheem. "Efficient Detection of Attacks in SIP Based VoIP Networks Using Linear l1-SVM Classifier." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 518–29. http://dx.doi.org/10.15837/ijccc.2019.4.3563.

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The Session Initiation Protocol (SIP) is one of the most common protocols that are used for signaling function in Voice over IP (VoIP) networks. The SIP protocol is very popular because of its flexibility, simplicity, and easy implementation, so it is a target of many attacks. In this paper, we propose a new system to detect the Denial of Service (DoS) attacks (i.e. malformed message and invite flooding) and Spam over Internet Telephony (SPIT) attack in the SIP based VoIP networks using a linear Support Vector Machine with l1 regularization (i.e. l1-SVM) classifier. In our approach, we project the SIP messages into a very high dimensional space using string based n-gram features. Hence, a linear classifier is trained on the top of these features. Our experimental results show that the proposed system detects malformed message, invite flooding, and SPIT attacks with a high accuracy. In addition, the proposed system outperformed other systems significantly in the detection speed.

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Pranata, Eko Ardian, Subari Subari, and Go Frendi Gunawan. "Penerapan Metode Naïve Bayes Untuk Klasifikasi Sms Spam Menggunakan Java Rogramming." J-INTECH 7, no. 02 (December 20, 2019): 104–8. http://dx.doi.org/10.32664/j-intech.v7i02.435.

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Short Message Service (SMS) adalah salah satu layanan komunikasi untuk mengirim dan menerima pesansingkat berupa teks pada telepon seluler (ponsel). SMS masih digunakan setiap harinya karena kemudahanpenggunaan, sederhana, cepat, dan murah. Meningkatnya penggunaan SMS dimanfaatkan oleh banyak pihak untuk mendapatkan keuntungan, salah satunya adalah mengirimkan spam melalui SMS. Metode yangdigunakan melakukan pendekatan probabilistik dalam melakukan inferensi yakni berbasis teorema bayessecara umum. Data latih yang digunakan pada proses pengkategorian didapat dari jurnal dan sudah memiliki kategori sebelumnya yaitu SMS spam dan bukan spam. Aplikasi pada SMS berbahasa Indonesia, yang mempunyai morfologi tertentu dalam pemrosesan pengkategorian. Aplikasi melakukan beberapa tahapan dalam melakukan pemrosesan diantaranya adalah preprocessing berupa case folding, dan parsing,transformation berupa penghapusan stopword removal dan stemming, penghitungan frekuensi danprobabilitas dan perhitungan naïve bayes. Pengkategorian yang dihasilkan oleh aplikasi dibandingkan dengan pengkategorian manual mempunyai rata rata precision sebesar 24%, recall 88% dan Confusion Matrix (Akurasi) sebesar 62%

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Min, Moohong, Jemin J. Lee, and Kyungho Lee. "Detecting Illegal Online Gambling (IOG) Services in the Mobile Environment." Security and Communication Networks 2022 (February 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/3286623.

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Despite the extensive ramifications of illegal online gambling (IOG) services, actions taken by government authorities have had little effect in halting these operations. In order to reduce the prevalence of IOG, the ability to detect malicious uniform resource locators (URLs) is crucial. Text mining and binary classification have been widely adopted to detect and prevent spam short message services (SMSs), but government authorities and various task forces that monitor and regulate gambling also rely on the analysis of malicious URLs. This study proposes a novel system to analyse the characteristics of spam URLs, offering a method that can assist government agencies combatting mobile IOG sites.

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JIN, Zhan. "Spam message self-adaptive filtering system based on Naive Bayes and support vector machine." Journal of Computer Applications 28, no. 3 (July 10, 2008): 714–18. http://dx.doi.org/10.3724/sp.j.1087.2008.00714.

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Amir Sjarif, Nilam Nur, Nurulhuda Firdaus Mohd Azmi, Suriayati Chuprat, Haslina Md Sarkan, Yazriwati Yahya, and Suriani Mohd Sam. "SMS Spam Message Detection using Term Frequency-Inverse Document Frequency and Random Forest Algorithm." Procedia Computer Science 161 (2019): 509–15. http://dx.doi.org/10.1016/j.procs.2019.11.150.

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ri.J, Erhi, Adebayo A.O, Akinsanya A.O, Sodiya A.S, Eze M.O, and Ebiesuwa Seun. "A Genetic-Bayesian Short Message Service Spam Filter with Text Normalization and Semantic Indexing." International Journal of Computer & Organization Trends 7, no. 6 (November 25, 2017): 14–19. http://dx.doi.org/10.14445/22492593/ijcot-v7i6p303.

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Yahlali, Mebarka. "A Survey on Bio-Inspired Method for Detection of Spamming." International Journal of Strategic Information Technology and Applications 8, no. 3 (July 2017): 1–19. http://dx.doi.org/10.4018/ijsita.2017070101.

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The objective of this work is to show the importance of bi-inspiration SPAM filtering. To achieve this goal, the author compared two methods: Social bees vs inspiration from the Human Renal. The inspiration is taken from a biological model. Messages are indexed and represented by the n-gram words and characters independent of languages (because message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining. The author starts this article with a short introduction where the readers will see the importance of IT security—especially today. The author then explains and experiments on a two original meta-heuristics and explains the natural model and then the artificial model.

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Majmundar, Anuja, NamQuyen Le, Meghan Bridgid Moran, Jennifer B. Unger, and Katja Reuter. "Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis." JMIR Public Health and Surveillance 6, no. 4 (December 7, 2020): e20649. http://dx.doi.org/10.2196/20649.

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Background Prior research suggests that social media–based public health campaigns are often targeted by countercampaigns. Objective Using reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media–based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion. Methods User comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions. Results Of the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75%) and disagreed with the health messages (39.77%), while the majority made healthy contributions to the discussions (84.38%). Only 0.56% of messages mentioned government agencies, and only 0.48% of the comments were antiregulation. Comments employing a positive tone (84.13%) or making healthy contributions (69.11%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25%) or making toxic contributions (36.26%) generally disagreed with the messages (P=0.01). Conclusions The majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media–based tobacco prevention campaigns to consider.

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Journal articles on the topic 'Spam message'

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