Journal articles: '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 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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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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Duan, Zhenhai, Peng Chen, Fernando Sanchez, Yingfei Dong, Mary Stephenson, and James Michael Barker. "Detecting Spam Zombies by Monitoring Outgoing Messages." IEEE Transactions on Dependable and Secure Computing 9, no. 2 (March 2012): 198–210. http://dx.doi.org/10.1109/tdsc.2011.49.

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Bhavika, S., B. Prema Sindhuri, and G. Bhavana. "Spam detection using semantic web in mail services." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 44. http://dx.doi.org/10.14419/ijet.v7i2.7.10255.

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Electronic mails have become a part of our daily lives to exchange different type of information and messages. They provide a great medium to communicate with large number of people in a single stretch. This made so many marketing groups to think that email is a great platform for publicizing their goods or products. Not only are these marketers there so many other types of users who wants to make use of these emails for their own needs. As the time prolongs, this had become a problem for the other users because of the continuous undesired electronic messages sent by different marketing and some other unauthorized users. These messages are termed as spam messages. These spam mails have become a serious issue and there is a need to clear away all these junk mails. To do so different spam detection methodologies are developed and employed for providing an effective mailing service to the users. In this paper, we present various spam detection methods that are existing and also finding the accurate, effective and reliable spam detection method.

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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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Świtalski, Piotr, and Mateusz Kopówka. "Machine Learning Methods in E-mail Spam Classification." Studia Informatica, no. 23 (December 22, 2020): 57–76. http://dx.doi.org/10.34739/si.2019.23.04.

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Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.

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Kiran, P. Sai, K. Prudhvi Chowdary, and T. T. Venkata Rayudu K. Vinay Kumar. "Netspam: An Efficient Approach to Prevent Spam Messages using Support Vector Machine." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1704–7. http://dx.doi.org/10.31142/ijtsrd11419.

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Alguliev, Rasim M., Ramiz M. Aliguliyev, and Saadat A. Nazirova. "Classification of Textual E-Mail Spam Using Data Mining Techniques." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/416308.

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A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined byk-nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.

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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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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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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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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 (July 10, 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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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, 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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Bhatia, Shailee, and . "A Study on Spam Detection Methods for Safe SMS Communication." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 790. http://dx.doi.org/10.14419/ijet.v7i3.12.16502.

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The electronic communication enables the instant and all type availability of user. The different form of information transition can be drawn in the form of SMS and emails. But these emails and SMS systems are also used by the individuals and firm as medium of their advertisement. Spam messages not only involves the unwanted messages but it also includes some viruses and threat to the security system. In this paper, a study to the SMS filtration methods is provided. The paper has explored the types of SMS spams, its threats and various filtration methods to detect the spam SMS.

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Nagaramani, K., K. Vandanarao, and B. Mamatha. "Machine Learning Algorithms for Spam Detection in Social Networks." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 41–44. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2090.

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Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.

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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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Chan, Patrick P. K., Cheng Yang, Daniel S. Yeung, and Wing W. Y. Ng. "Spam filtering for short messages in adversarial environment." Neurocomputing 155 (May 2015): 167–76. http://dx.doi.org/10.1016/j.neucom.2014.12.034.

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Fang, Yajie, and Ping Zhang. "Recognition of Spam Messages Based on Text Mining." Journal of Physics: Conference Series 1624 (October 2020): 052024. http://dx.doi.org/10.1088/1742-6596/1624/5/052024.

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Liu, Jian-Yun, Yu-Hang Zhao, Zhao-Xiang Zhang, Yun-Hong Wang, Xue-Mei Yuan, Lei Hu, and Zhen-Jiang Dong. "Spam Short Messages Detection via Mining Social Networks." Journal of Computer Science and Technology 27, no. 3 (January 2012): 506–14. http://dx.doi.org/10.1007/s11390-012-1239-7.

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Jain, Ankit Kumar, Diksha Goel, Sanjli Agarwal, Yukta Singh, and Gaurav Bajaj. "Predicting Spam Messages Using Back Propagation Neural Network." Wireless Personal Communications 110, no. 1 (September 14, 2019): 403–22. http://dx.doi.org/10.1007/s11277-019-06734-y.

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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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Felkai, Péter, and Ingrid Lengyel. "Kéretlen e-mailek az orvos postafiókjában: ezek veszélyei az egészségnevelésre, a betegtájékoztatásra és a tudományos munkára." Orvosi Hetilap 160, no. 43 (October 2019): 1706–10. http://dx.doi.org/10.1556/650.2019.31531.

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Abstract: Introduction: The term “spam” is applied to unwanted commercial e-mails sent to all whose e-mail addresses have been acquired by the spammers. The number of undesirable e-mails is growing in the health-care related areas as well. The targets of health-care related spams are laymen, physicians and academic researchers alike. Method: On the basis of 12,986 unwanted letters received in one year, the authors concluded that percentage of health-related spam is the second most common spam (27%) in relation to all spam. Most of the spam (63%) aggressively promoted purchasing of various consumer goods, but health-related spam are far ahead of the rest. The collected data were grouped by year and topic and they are analyzed by simple descriptive statistics. Spam form of cyber attacks on health care issues were divided into two: spam what is jeopardized individuals’ health (e.g. medical compounds without any curing effect, misleading statement on medical device, fraudulent panacea offers, and cheating cure methods, etc.) and onslaught on medical scientific activity (pseudo-scientific congress invitation, predator journal invitation etc.). Results: The topics of spams addressed to laymen are offered for perfect healing by strange treatments, cures (31%), panaceas (19%), lifestyle advice (19%), massage (16%), brand new health-care devices (4%) and drugs for sexual dysfunction (11%). The topics of spams addressed to physicians and researchers are deluged by pseudoscientific materials: invitation for articles to be sent to no-name/fake open-access journals (68%), invitation to participate at an obscure congress (27%) or newsletters on miscellanous medical topics (5%). Conclusion: The spams offer very often relief or solution to medical problems that the present-day medical practice cannot solve perfectly (oncological, musculo-sceletal, endocrin or metabolic problems). Understandably, the patients would hold on to fake hopes – and the authentic patient education and health promotion will be neglected. These unwanted messages practically cannot be unsubscribed, and – while the spam filters are far from perfection – the victim must go through the filtered spam-dustbin in order not to miss some real messages. Unfortunately no legal regulation (neither local, nor GDPR) can block or stop the spams. The spams are misleading the laymen and jeopardise the effects of professional and responsible health promotion and health education. Orv Hetil. 2019; 160(43): 1706–1710.

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Ghaedi, Parichehr, and Ali Harounabadi. "Identifying spam e-mail messages using an intelligence algorithm." Decision Science Letters 3, no. 3 (2014): 439–44. http://dx.doi.org/10.5267/j.dsl.2014.1.002.

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Latreche, Abdelkrim, and Kadda Benyahia. "A New Bio-Inspired Method for Spam Image-Based Emails Filtering." International Journal of Organizational and Collective Intelligence 11, no. 2 (April 2021): 29–50. http://dx.doi.org/10.4018/ijoci.2021040102.

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Electronic mail has become one of the most popular and frequently used channels for personal and professional online communication. Despite its benefits, e-mail faces a major security problem, which is the daily reception of a large number of unsolicited electronic messages, known as “spam emails.” Today, most electronic mail systems have simple spam filtering mechanisms based on text spam filtering technologies. To circumvent these filters, spammers are introducing new techniques of embedding spam messages in the image attached to the mail. In this article, the authors propose a new method for spam image filtering. The proposed system can distinguish between legitimate images from spam images based on the texture characteristics of the image attached to an email. From each image, around 20 characteristics can be extracted from the gray level co-occurrence matrix (GLCM). Then, to classify the images as spam or ham, the authors use a new metaheuristic nature-inspired model for building classifiers based on the social worker bees and enhanced nearest-centroid classification method.

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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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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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Alqahtani, Sahar, and Daniyal Alghazzawi. "A survey of Emerging Techniques in Detecting SMS Spam." Transactions on Machine Learning and Artificial Intelligence 7, no. 5 (November 8, 2019): 23–35. http://dx.doi.org/10.14738/tmlai.75.7116.

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In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

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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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Metlapalli, Ayyappa Chakravarthi, Thillaikarasi Muthusamy, and Bhanu Prakash Battula. "Classification of Social Media Text Spam Using VAE-CNN and LSTM Model." Ingénierie des systèmes d information 25, no. 6 (December 31, 2020): 747–53. http://dx.doi.org/10.18280/isi.250605.

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Presently a day's human relations are kept up by online life systems. Customary connections now days are outdated. To keep up in affiliation, sharing thoughts, trade information between we utilize web-based social networking organizing locales. Web based life organizing locales like Twitter, Facebook, LinkedIn and so forth are accessible in the correspondence condition. Through Twitter media clients share their sentiments, interests, information to others by messages. Simultaneously a portion of the client's mislead the certifiable clients. These certified clients are additionally called requested clients and the clients what misguidance's identity is called spammers. These spammers present undesirable data on the non-spam clients. The non-spammers may retweet them to other people and they follow the spammers. Generally most of the spam messages are in the form of text, images and different multimedia formats. Considering all different formats in one process may not give the best classification results. In this paper address the process and classification of text spam messages. Classification of text messages is a complex task in order to achieve this deep learning based hybrid VAE-CNN and LSTM model is proposed and evaluated the model using the performance metrics of precision, recall and F measure metrics.

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Baig, Azhar. "Email Spam Detection using SVM." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 669–72. http://dx.doi.org/10.22214/ijraset.2021.36383.

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E-mail contributes to internet messaging as a necessary component. Spam mails are unwanted messages that appear in large numbers and are exploited by spammers to divulge personal information of the user. These e-mails are frequently company/control announcements or malware that the user receives suddenly. Email spamming is one of the Internet's unsolved challenges, causing inconvenience to users and loss to businesses. Filtering is one of the foremost widely used and important methods for preventing spam emails. Email filters are commonly wont to organize incoming emails, protect computers from viruses, and eliminate spam. We present this method to classifying spam emails using support vector machines during this study, the SVM outperformed other classifiers.

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Priti, Dr, and Uma Uma. "Performance Analysis, Comparative Survey of Various Classification Techniques in Spam Mail Filtering." Oriental journal of computer science and technology 10, no. 3 (July 1, 2017): 698–702. http://dx.doi.org/10.13005/ojcst/10.03.21.

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One of the most common methods of communication involves the use of e-mail for personal messages or for business purposes. One of the major concerns of using the emails is the problem of e-mail spam. The worst part of the spam emails is that, these are invading the users without their consent and bombarding of these spam mails fills up the whole email space of the user along with that, the issue of the wasting the network capacity and time consumption in checking and deleting the spam mails makes it even more concerning issue. With the increasing demand of removing the e-mail spams the area has become magnetic to the researchers. This paper intends to present the performance comparison analysis of various pre-existing classification technique. This paper discusses about spam mails in section (I), In section (II) various feature selection methods are discussed , In section (III) classification techniques concept in spam filtering has been elaborated, In section (IV) existing algorithms for classification are discussed and are compared. In section (V) concludes the paper giving brief summary of the work.

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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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Шанін, А. О. "Rotation Forest model modification within the email spam classification." Системи обробки інформації, no. 1(164) (March 17, 2021): 114–20. http://dx.doi.org/10.30748/soi.2021.164.12.

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Increased use of email in daily transactions for many businesses or general communication due to its cost-effectiveness has made emails vulnerable to attacks, including spam. Spam emails are unsolicited messages that are very similar to each other and sent to multiple recipients randomly. This study analyzes the Rotation Forest model and modifies it for spam classification problem. Also, the aim of this study is to create a better classifier. To improve classifier stability, the experiments were carried out on Enron spam, Ling spam, and SpamAssasin datasets and evaluated for accuracy, f-measure, precision, and recall.

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Sohn, Dae-Neung, Jung-Tae Lee, Seung-Wook Lee, Joong-Hwi Shin, and Hae-Chang Rim. "Korean Mobile Spam Filtering System Considering Characteristics of Text Messages." Journal of the Korea Academia-Industrial cooperation Society 11, no. 7 (July 31, 2010): 2595–602. http://dx.doi.org/10.5762/kais.2010.11.7.2595.

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Pathan, Sumaiya, and R. H. "Detection of Spam Messages in Social Networks based on SVM." International Journal of Computer Applications 145, no. 10 (July 15, 2016): 34–38. http://dx.doi.org/10.5120/ijca2016910793.

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Jeong, Sihyun, and Kyu-haeng Lee. "Spam Classification Based on Signed Network Analysis." Applied Sciences 10, no. 24 (December 15, 2020): 8952. http://dx.doi.org/10.3390/app10248952.

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Online social networking services have become the most important information-sharing medium of modern society due to several merits, such as creating opportunities to broaden social relations, easy and instant communication, and fast data propagation. These advantages, however, are being abused by malicious users to disseminate unsolicited spam messages, causing great harm to both users and service providers. To address this problem, numerous spam detection methods utilizing various spam characteristics have been proposed, but most of them suffer from several limitations. Using individual behaviors and the content of messages for spam classification has been revealed to have bounded performance, since attackers can easily fake them. Instead, exploitation of social-network-related features has been highlighted as an alternative solution, but recent spam attacks can adroitly avoid these methods by controlling their ranking through various forms of attack. In this paper, we delineate a signed-network-analysis-based spam classification method. Our key hypothesis is that the edge signs are highly likely to be determined by considering users’ social relationships, so there will be a substantial difference between the edge sign patterns of spammers and that of non-spammers. To identify our hypothesis, we employ two social psychological theories for signed networks—structural balance theory and social status theory—and the concept of surprise is adopted to quantitatively analyze the given network according to these theories. These surprise measurements are then used as the main features for spam classification. In addition, we develop a graph-converting method for applying our scheme to unsigned networks. Extensive experimental results with Twitter and Epinions datasets show that the proposed scheme obtains significant classification performance improvement compared to conventional schemes.

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Et. al., T. Poonkodi,. "E-Mail Spam Filtering Through Feature Selection Using Enriched Firefly Optimization Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 1248–55. http://dx.doi.org/10.17762/turcomat.v12i5.1791.

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E-mail is the most common method of communication because due to its ability to obtain, the rapid modification of messages and low cost of distribution. Spam causes traffic issues and bottlenecks that limit the amount of memory and bandwidth, power and computing speed. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. A methodology based on Sine- Cosine Algorithm (SCA) introduced which address the problem of space and time complexities are increased in E-Mail spam detection. In this method, WordNet optimized semantic ontology applies different methods based on semantics and similarity measures to reduce the large number of extracted textual features. This paper proposed the Enriched Firefly Optimization Algorithm (EFOA) method effectively selecting suitable features from an upper dimensional space using the fitness function. Once the best feature space is identified through EFOA, the spam classification is done using ANN. Intially, E-mail spam dataset is preprocessed, then the extracted textual features are Semantic-based reduction and Features weights updated using optimized semantic WordNet. The results obtained showed that the ANN classifier after selection of features using EFOA was able to classify e-mails as spam and non-spam. This EFOA demonstrates that the proposed method has led to a remarkable improvement compared to the SCA methods.

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Roy, Sanjiban Sekhar, and V. Madhu Viswanatham. "Classifying Spam Emails Using Artificial Intelligent Techniques." International Journal of Engineering Research in Africa 22 (February 2016): 152–61. http://dx.doi.org/10.4028/www.scientific.net/jera.22.152.

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Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

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Reddy, Gaddam Akhil, and Dr B. Indira Reddy. "Classification of Spam Text using SVM." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 17, 2021): 616–24. http://dx.doi.org/10.51201/jusst/21/08437.

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The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

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Plice, Robert, Oleg Pavlov, and Nigel Melville. "Spam and Beyond: An Information-Economic Analysis of Unwanted Commercial Messages." Journal of Organizational Computing and Electronic Commerce 18, no. 4 (October 2008): 278–306. http://dx.doi.org/10.1080/10919390802421267.

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Ghourabi, Abdallah, Mahmood A. Mahmood, and Qusay M. Alzubi. "A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages." Future Internet 12, no. 9 (September 18, 2020): 156. http://dx.doi.org/10.3390/fi12090156.

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Despite the rapid evolution of Internet protocol-based messaging services, SMS still remains an indisputable communication service in our lives until today. For example, several businesses consider that text messages are more effective than e-mails. This is because 82% of SMSs are read within 5 min., but consumers only open one in four e-mails they receive. The importance of SMS for mobile phone users has attracted the attention of spammers. In fact, the volume of SMS spam has increased considerably in recent years with the emergence of new security threats, such as SMiShing. In this paper, we propose a hybrid deep learning model for detecting SMS spam messages. This detection model is based on the combination of two deep learning methods CNN and LSTM. It is intended to deal with mixed text messages that are written in Arabic or English. For the comparative evaluation, we also tested other well-known machine learning algorithms. The experimental results that we present in this paper show that our CNN-LSTM model outperforms the other algorithms. It achieved a very good accuracy of 98.37%.

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Journal articles on the topic 'Spam messages'

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