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An evaluation of Naive Bayes variants in content-based learning for spam filtering

Alexander K. Seewald
2007 Intelligent Data Analysis  
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two current variants.  ...  Our main motivation was to test whether two variants of Naive Bayes learning, SpamAssassin and CRM114, were superior to simple Naive Bayes learning, represented by SpamBayes.  ...  Acknowledgements The Austrian Research Institute for Artificial Intelligence is supported by the Austrian Federal Ministry of Education, Science and Culture and by the Austrian Federal Ministry for Transport  ... 
doi:10.3233/ida-2007-11505 fatcat:hqfr7tnfdrbnhe4ixruk3kp3vm

Content-based concept drift detection for Email spam filtering

Morteza Zi Hayat, Javad Basiri, Leila Seyedhossein, Azadeh Shakery
2010 2010 5th International Symposium on Telecommunications  
In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution.  ...  The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area.  ...  In this research two variants of Naive Bayes classifier have been applied for spam filtering: multinomial and multi-bernoulli.  ... 
doi:10.1109/istel.2010.5734082 fatcat:eas6glnpnval5ji5qj4hftinni

Spam Detection in Social Media Networking Sites using Ensemble Methodology with Cross Validation

2020 International Journal of Engineering and Advanced Technology  
In this paper we proposed an ensemble methodology for identification spam on Twitter social media network.  ...  In this methodology we used Decision tree induction algorithm, Naïve bayes algorithm and KNN algorithm to construct a model.  ...  Naive Bayes Classifier: This is one of the best machine learning algorithms for spam classification [17] , [28] .  ... 
doi:10.35940/ijeat.c5558.029320 fatcat:ynmcnzxeyja4jpr4a7yzv56gku

A review of machine learning approaches to Spam filtering

Thiago S. Guzella, Walmir M. Caminhas
2009 Expert systems with applications  
In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual-and image-based approaches.  ...  two early naive Bayes models.  ...  However, naive Bayes had an unacceptable performance for k ¼ 9.  ... 
doi:10.1016/j.eswa.2009.02.037 fatcat:gf5z34w6arcdzh2w36tgefqppa

E-Mail Spam Detection using Machine Learning and Deep Learning

Shivam Pandey
2020 International Journal for Research in Applied Science and Engineering Technology  
Here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques.  ...  Our focus is primarily on machine learning-based spam filters and variants that are inspired by them. We report on related ideas, techniques, major efforts and cutting-edge art in the field.  ...  Machine Learning Here we compare the accuracy and performance of SVM with Naïve Bayes Classifier for the same set of data.  ... 
doi:10.22214/ijraset.2020.6159 fatcat:kmn33grvjrcwxek5amfiqi2xvu

Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks

Venkata RamiReddy Chirra, Hoolda Daniel Maddiboyina, Yakobu Dasari, Ranganadhareddy Aluru
2020 Review of Computer Engineer Studies  
However, notably today's spam filters in use are built using traditional approaches such as statistical and content-based techniques.  ...  Today spam mail accounts for 45% of all email and hence there is an ever-increasing need to build efficient spam filters to identify and block spam mail.  ...  The effort is to build an efficient filter for spam with high precision and an improved spam filter compared to the filters used twenty years ago.  ... 
doi:10.18280/rces.070403 fatcat:sp6l3qzhfvb4dbwim4l7zcvlna

An evaluation of statistical spam filtering techniques

Le Zhang, Jingbo Zhu, Tianshun Yao
2004 ACM Transactions on Asian Language Information Processing  
This paper evaluates five supervised learning methods in the context of statistical spam filtering.  ...  An interesting finding is the effect of mail headers on spam filtering, which is often ignored in previous studies.  ...  Acknowledgments The authors are grateful to two anonymous reviewers whose thoughtful comments improved the quality of this paper, especially the use of TCR measure in evaluation.  ... 
doi:10.1145/1039621.1039625 fatcat:nhn7zowx5rgcrmmxja5hdpkfxy

A Model for Fuzzy Logic Based Machine Learning Approach for Spam Filtering

Mehdi Samiei yeganeh
2012 IOSR Journal of Computer Engineering  
E-mail provides a perfect way to send these millions of advertisements without any for a sender, and this fortunate fact is nowadays extensively exploited by several organizations.  ...  As a result, the e-mail boxes of millions of people get cluttered with all these so-called Unsolicited Bulk  ...  Naïve Bayes classifier method In 1998 the Naïve Bayes classifier was proposed for spam recognition [1] . Bayesian spam filtering [12] is a statistical technique of filtering.  ... 
doi:10.9790/0661-0450710 fatcat:rf4olav5unbpfhzarxwsd7hldm

A Cluster-based Approach to Filtering Spam under Skewed Class Distributions

Wen-feng Hsiao, Te-ming Chang, Guo-hsin Hu
2007 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)  
The results show that our proposed classifier can effectively deal with the issue of skewed class distributions in the task of spam filtering.  ...  The purpose of this research is to propose an appropriate classification approach to improving the effectiveness of spam filtering on the issue of skewed class distributions.  ...  Acknowledgement This study was supported by the National Science Council of Taiwan with grant No: NSC 93-2218-E-251 -001.  ... 
doi:10.1109/hicss.2007.7 dblp:conf/hicss/HsiaoCH07 fatcat:emgbfxoi5vfwrcxdr4qnlkzk2q

A survey of learning-based techniques of email spam filtering

Enrico Blanzieri, Anton Bryl
2008 Artificial Intelligence Review  
In this paper we give an overview of the state of the art of machine learning applications for spam filtering, and of the ways of evaluation and comparison of different filtering methods.  ...  Among the approaches developed to stop spam, filtering is an important and popular one.  ...  Fabio Massacci for many useful discussions and for suggesting the way to structure the comparison section.  ... 
doi:10.1007/s10462-009-9109-6 fatcat:sqgo2cdiyveynog45yqmhh4dla

Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends [article]

Alexy Bhowmick, Shyamanta M. Hazarika
2016 arXiv   pre-print
We present a comprehensive review of the most effective content-based e-mail spam filtering techniques.  ...  Learning front in fighting spam.  ...  [Seewald, 2007] investigated the simple Naive Bayes learner represented by SpamBayes, and two variants of Naive Bayes learning, SA-Train and CRM-114.  ... 
arXiv:1606.01042v1 fatcat:cblnuc4knfhehjwzjeeekbgf3m

Classifier Ensembles Using Structural Features For Spammer Detection In Online Social Networks

Muhammad Abulaish, Sajid Y. Bhat
2015 Foundations of Computing and Decision Sciences  
Especially, content-based filtering of spam messages and spammer profiles in online social networks is becoming difficult.  ...  In this paper, we present an ensemble learning method for online social network security by evaluating the performance of some basic ensemble classifiers over novel community-based social networking features  ...  We compare the performance of multiple classifiers, including decision tree and naïve Bayes, and their ensemble variants implemented as a part of WEKA [14] , which is an implementation of machine learning  ... 
doi:10.1515/fcds-2015-0006 fatcat:wlisrgrmzvcktch4vc3eky2cym

Effectiveness and Limitations of Statistical Spam Filters [article]

M. Tariq Banday, Tariq R. Jan
2009 arXiv   pre-print
In this paper we discuss the techniques involved in the design of the famous statistical spam filters that include Naive Bayes, Term Frequency-Inverse Document Frequency, K-Nearest Neighbor, Support Vector  ...  Further, we discuss the effectiveness and limitations of statistical filters in filtering out various types of spam from legitimate e-mails.  ...  We also presented an empirical evaluation in terms of various metrics of four machine learning algorithms namely Naïve Bayes, Term Frequency-Inverse Document Frequency, K-Nearest Neighbor, Support Vector  ... 
arXiv:0910.2540v1 fatcat:e32es6byvzaebob455wiiy7tji

An Observation and Experimental Evaluation of Image Spam Detection

2019 International journal of recent technology and engineering  
In this research paper we spring a exhaustive review and categorization of machine learning and classification systems suggested so far in contradiction of image based spam email, and make an empirical  ...  In belonging to other supports duel beside researchers of image spam detections, unsolicited mail have newly developed the image based spam dodge to construct the investigation of e-mails' content of text  ...  Classifier Based Approach Naïve-Bayes/probabilistic Classifier: In 1998, first probabilistic classifier for unsolicited or identification is proposed.  ... 
doi:10.35940/ijrte.c4733.098319 fatcat:yicxauizbzaobcdfs23ge62vhy

The Impact of Deep Learning Techniques on SMS Spam Filtering

Wael Hassan Gomaa
2020 International Journal of Advanced Computer Science and Applications  
This paper explores the impact of applying various deep learning techniques on SMS spam filtering; by comparing the results of seven different deep neural network architectures and six classifiers for  ...  Most of the previous studies that attempted to detect spam were based on manually extracted features using classical machine learning classifiers.  ...  CONCLUSION AND FUTURE WORK This study covers SMS spam filtering task; thirteen proposed machine learning and deep learning classifiers were evaluated.  ... 
doi:10.14569/ijacsa.2020.0110167 fatcat:3eiy544pgzgdpcm5evdtxbqbsi
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