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Journal articles on the topic 'Bayesian spam filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Jiang, Xue, and Jun Kai Yi. "Improved Bayesian-Based Spam Filtering Approach." Applied Mechanics and Materials 401-403 (September 2013): 1885–91. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1885.

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Bayesian filtering approach is widely used in the field of anti-spam now. However, the two assumptions of this algorithm are significantly different with the actual situation so as to reduce the accuracy of the algorithm. This paper proposes a detailed improvement on researching of Bayesian Filtering Algorithm principle and implement method. It changes the priori probability of spam from constant figure to the actual probability, improves selection and selection rules of the token, and also adds URL and pictures to the detection content. Finally it designs a spam filter based on improved Bayesian filter approach. The experimental result of this improved Bayesian Filter approach indicates that it has a beneficial effect in the spam filter application.

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Al-Alwani, Abdulkareem, and Majdi Beseiso. "Arabic Spam filtering using Bayesian Model." International Journal of Computer Applications 79, no. 7 (October 18, 2013): 11–14. http://dx.doi.org/10.5120/13752-1582.

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3

Zhang, Zhiying. "A Bayesian Topic Model for Spam Filtering." Journal of Information and Computational Science 10, no. 12 (August 10, 2013): 3719–27. http://dx.doi.org/10.12733/jics20102279.

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Srivastava, Anushka. "Junk Filtering through Naive Bayesian Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 1999–2004. http://dx.doi.org/10.22214/ijraset.2021.36801.

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As the world is seamlessly developing at a very high pace, we have been seeing enormous growth in various sectors of Technology. Networking has played a crucial part in the exchange of technological culture around the globe, and the Internet being the sole medium of Network enhancement has taken over every aspect of our society. Today, most of the professional communications are done through emailing. As far as email has proven to be an efficient, professional and easy way of communication, it also comes with the disadvantage of unwanted bulk bombarding of spam content. This has been a critical concern for email users. Consequently, it has become very difficult for spam filters to efficiently filter the unwanted emails, since nowadays emails are written in such a manner that any existing algorithm cannot give 100% accuracy in predicting spam. This paper deals with Naive Bayesian Classifier that is a Machine Learning algorithm for antispam filtering, which gives satisfactory results by automatically constructing anti-spam filters with extended conduct. The review over the researched performance of Naive Bayes algorithm is done by the investigations of Spam ham csv datasets. The performance of the algorithm is evaluated based on the accuracy, recall and precision it shows on the mentioned datasets. This technique gives 96-97% accuracy and 89% precision on the investigated dataset. The result also highlights that the content of the email and the number of instances of the dataset has an apparent effect on the performance of the algorithm.

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Krishna, Mr B. "E-Mail Spam Classification using Naive Bayesian Classifier." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5209–14. http://dx.doi.org/10.22214/ijraset.2021.36153.

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— E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it’s an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naïve Bayesian Classifier for spam classification. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the Lingspam dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature. The result is to increase the accuracy of the system.

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Liu, Xin, Pingjun Zou, Weishan Zhang, Jiehan Zhou, Changying Dai, Feng Wang, and Xiaomiao Zhang. "CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing." Wireless Communications and Mobile Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1457870.

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Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.

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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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8

赵, 彩迪. "Research on Naive Bayesian Spam SMS Filtering Based on MapReduce." Computer Science and Application 06, no. 07 (2016): 443–50. http://dx.doi.org/10.12677/csa.2016.67054.

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Haseeb, Saima, Mahak Motwani, and Amit Saxena. "Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC." International Journal of Computer Applications 74, no. 17 (July 26, 2013): 9–14. http://dx.doi.org/10.5120/12975-9567.

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10

Priyanka, T. "Bayesian Decision Framework for an Efficient Spam Filtering in Social Network." International Journal of Computer & Organization Trends 7, no. 1 (April 25, 2014): 20–24. http://dx.doi.org/10.14445/22492593/ijcot-v7p304.

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11

Jia, ZhiYi, Haiyun Yu, and Xincun Yang. "Research on spam filtering algorithm based on mutual information and weighted naive Bayesian classification." International Journal of Ad Hoc and Ubiquitous Computing 37, no. 4 (2021): 240. http://dx.doi.org/10.1504/ijahuc.2021.10040647.

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Yang, Xincun, Haiyun Yu, and ZhiYi Jia. "Research on spam filtering algorithm based on mutual information and weighted naive Bayesian classification." International Journal of Ad Hoc and Ubiquitous Computing 37, no. 4 (2021): 240. http://dx.doi.org/10.1504/ijahuc.2021.117313.

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13

Shahi, Tej Bahadur, and Abhimanu Yadav. "Mobile SMS Spam Filtering for Nepali Text Using Naïve Bayesian and Support Vector Machine." International Journal of Intelligence Science 04, no. 01 (2014): 24–28. http://dx.doi.org/10.4236/ijis.2014.41004.

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14

Guo, Yanyan, Lei Zhou, Kemeng He, Yuwan Gu, and Yuqiang Sun. "Bayesian Spam Filtering Mechanism Based on Decision Tree of Attribute Set Dependence in the MapReduce Framework." Open Cybernetics & Systemics Journal 8, no. 1 (December 31, 2014): 435–41. http://dx.doi.org/10.2174/1874110x01408010435.

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15

Vernanda, Yustinus, Seng Hansun, and Marcel Bonar Kristanda. "Indonesian language email spam detection using N-gram and Naïve Bayes algorithm." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 2012–19. http://dx.doi.org/10.11591/eei.v9i5.2444.

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Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.

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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "A novel committee selection mechanism for combining classifiers to detect unsolicited emails." VINE Journal of Information and Knowledge Management Systems 46, no. 4 (November 14, 2016): 524–48. http://dx.doi.org/10.1108/vjikms-07-2015-0042.

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Purpose The email is an important medium for sharing information rapidly. However, spam, being a nuisance in such communication, motivates the building of a robust filtering system with high classification accuracy and good sensitivity towards false positives. In that context, this paper aims to present a combined classifier technique using a committee selection mechanism where the main objective is to identify a set of classifiers so that their individual decisions can be combined by a committee selection procedure for accurate detection of spam. Design/methodology/approach For training and testing of the relevant machine learning classifiers, text mining approaches are used in this research. Three data sets (Enron, SpamAssassin and LingSpam) have been used to test the classifiers. Initially, pre-processing is performed to extract the features associated with the email files. In the next step, the extracted features are taken through a dimensionality reduction method where non-informative features are removed. Subsequently, an informative feature subset is selected using genetic feature search. Thereafter, the proposed classifiers are tested on those informative features and the results compared with those of other classifiers. Findings For building the proposed combined classifier, three different studies have been performed. The first study identifies the effect of boosting algorithms on two probabilistic classifiers: Bayesian and Naïve Bayes. In that study, AdaBoost has been found to be the best algorithm for performance boosting. The second study was on the effect of different Kernel functions on support vector machine (SVM) classifier, where SVM with normalized polynomial (NP) kernel was observed to be the best. The last study was on combining classifiers with committee selection where the committee members were the best classifiers identified by the first study i.e. Bayesian and Naïve bays with AdaBoost, and the committee president was selected from the second study i.e. SVM with NP kernel. Results show that combining of the identified classifiers to form a committee machine gives excellent performance accuracy with a low false positive rate. Research limitations/implications This research is focused on the classification of email spams written in English language. Only body (text) parts of the emails have been used. Image spam has not been included in this work. We have restricted our work to only emails messages. None of the other types of messages like short message service or multi-media messaging service were a part of this study. Practical implications This research proposes a method of dealing with the issues and challenges faced by internet service providers and organizations that use email. The proposed model provides not only better classification accuracy but also a low false positive rate. Originality/value The proposed combined classifier is a novel classifier designed for accurate classification of email spam.

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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "A study of boosted evolutionary classifiers for detecting spam." Global Knowledge, Memory and Communication 69, no. 4/5 (November 1, 2019): 269–87. http://dx.doi.org/10.1108/gkmc-05-2019-0060.

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Purpose Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique. Design/methodology/approach Text mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine). Findings This study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain. Research limitations/implications The research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations. Practical implications In this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research. Originality/value A comparison between GP and GAs has been shown with/without ensemble in spam classification application domain.

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18

Boudia, Mohamed Amine, Reda Mohamed Hamou, and Abdelmalek Amine. "A New Meta-Heuristic based on Human Renal Function for Detection and Filtering of SPAM." International Journal of Information Security and Privacy 9, no. 4 (October 2015): 26–58. http://dx.doi.org/10.4018/ijisp.2015100102.

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The e-mail is therefore one of the most used methods for its efficiency and profitability. In the last few years, the undesirables emails (SPAM) are widely spread as they play an important part in the inbox. Consequently, several recent studies have provided evidence of the importance of detection and filtering of SPAM as a major interest for the Internet community. In the present paper, the authors propose and experiment a new and original meta-heuristic based on the renal system for detection and filtering spam. The natural model of the renal system is taken as an inspiration for its purification of blood, the filtering of toxins as well as the regularization of the blood pressure. The messages are represented by both a bag words and N-Gram method which is independent of languages because an email can be received in any language. After that, the authors propose to use two models to apply a Bayesien classification on textual data: Bernoulli or Multinomial model.

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19

Kardaras, Constantinos, and Johannes Ruf. "Filtration shrinkage, the structure of deflators, and failure of market completeness." Finance and Stochastics 24, no. 4 (September 2, 2020): 871–901. http://dx.doi.org/10.1007/s00780-020-00435-2.

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Abstract We analyse the structure of local martingale deflators projected on smaller filtrations. In a general continuous-path setting, we show that the local martingale parts in the multiplicative Doob–Meyer decomposition of projected local martingale deflators are themselves local martingale deflators in the smaller information market. Via use of a Bayesian filtering approach, we demonstrate the exact mechanism of how updates on the possible class of models under less information result in the strict supermartingale property of projections of such deflators. Finally, we demonstrate that these projections are unable to span all possible local martingale deflators in the smaller information market, by investigating a situation where market completeness is not retained under filtration shrinkage.

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20

Yang, Yitao, Guozi Sun, and Chengyan Qiu. "Bayesian Spam Detection Framework on Mobile Device." Recent Patents on Computer Science 12 (August 19, 2019). http://dx.doi.org/10.2174/2213275912666190819121251.

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In recent years, the spam message problem becomes more serious. Similar to spam mail, the spam message in phone brings a big trouble to users. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtering method. A Bayesian spam detection framework is designed in the paper and is deployed on Android device to test. Besides it can filtering coming messages and classify them into normal or spam in real time, it introduces feedback learning mechanism to make its result more accurate. The experiments are conducted under the real environment. The results show that the framework can meet the requirement of spam filtering.

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21

Selo W.T., Gilang Jalu, and Budi Susanto. "IMPLEMENTASI NAÏVE BAYESIAN CLASSIFIER UNTUK KASUS FILTERING SMS SPAM." Jurnal Informatika 9, no. 2 (July 9, 2014). http://dx.doi.org/10.21460/inf.2013.92.317.

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In 2011, the circulation of SMS spam in Indonesia was rampant. The SMS can contain promotion of a product which is often unsolicited by the recipient or fraud. This is an overlooked issue in Indonesia. But spam has been a very common topic in other countries. To resolve these problems, we need a system that can recognize SMS spam so the SMS can be diverted or marked prior to the user. In this research, we built a system that implementing the Naive Bayesian classifier for classifying SMS spam, so the user can recognize the SMS spam. The result of this research, the system built is able to classify a SMS into categories spam and not spam. Naïve Bayesian classifier can be implemented effectively in the case of SMS spam filtering. The proper use of text preprocessing can improve the performance of this classification system.

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22

SHESHIKALA, M. "IMPROVING SPAM EMAIL FILTERING EFFICIENCY USING BAYESIAN BACKWARD APPROACH PROJECT." International Journal of Computer Science and Informatics, July 2014, 1–6. http://dx.doi.org/10.47893/ijcsi.2014.1164.

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Unethical e-mail senders bear little or no cost for mass distribution of messages, yet normal e-mail users are forced to spend time and effort in reading undesirable messages from their mailboxes. Due to the rapid increase of electronic mail (or e-mail), several people and companies found it an easy way to distribute a massive amount of undesired messages to a tremendous number of users at a very low cost. These unwanted bulk messages or junk e-mails are called spam messages .Several machine learning approaches have been applied to this problem. In this paper, we explore a new approach based on Bayesian classification that can automatically classify e-mail messages as spam or legitimate. We study its performance for various datasets.

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