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Filters
Spam Filtering
2014
Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) - WIMS '14
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Use only 2% of the overall message labels Consider natural grouping of data : select representative instances for training ...
Unsupervised learning, based on local structure Create groups of highly correlated data-points No re-clustering of data Our contribution : Active Learning combined with Incremental Clustering ...
, X n } and a cluster C j,k of a clustering Cl j compute : Enron-Spam, NSCR "Demokritos" Baseline : 2%B Target Model : Supervised Training (ST) Thresholds tested: [0.3,0.5], [0.5,1.0] Evaluation ...
doi:10.1145/2611040.2611059
dblp:conf/wims/GeorgalaKP14
fatcat:qcet2r7jazgdtddp7qvrwa5acy
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
[chapter]
2009
Lecture Notes in Computer Science
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It makes the cost too high for fully supervised learning to frequently collect sufficient labeled data for training. ...
In this paper, we propose a semi-supervised approach, regularized discriminant EM algorithm (RDEM), to detect image spam emails, which leverages small amount of labeled data and large amount of unlabeled ...
Most of them leverage supervised machine learning algorithms to build a classifier for filtering spam images [14, 15] by using image-based features. ...
doi:10.1007/978-3-642-03348-3_17
fatcat:x2xizupfa5gbhllzwnhev3b3je
Trends in Combating Image Spam E-mails
[article]
2012
arXiv
pre-print
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Initially, spam e-mails contained only textual messages which were easily detected by the text-based spam filters. ...
In this paper, we examine the motivations and the challenges in image spam filtering research, and we review the recent trends in combating image spam e-mails. ...
The techniques listed in Table 1 uses supervised learning for training the classifiers. Few studies [21] have used semi-supervised learning techniques for image spam detection. ...
arXiv:1212.1763v1
fatcat:t3urvsj2b5dkjclap6poegdyuu
Semi-Supervised Spam Detection in Twitter Stream
2018
IEEE Transactions on Computational Social Systems
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2017 pre-print arXiv:1702.01032v1 fatcat:66rgzvgelbcdliy2jde47kmd6q
In this paper, we propose a Semi-Supervised Spam Detection (S3D) framework for spam detection at tweet-level. ...
Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. ...
In this paper, we propose a semi-supervised framework for spam tweet detection. ...
doi:10.1109/tcss.2017.2773581
fatcat:nffhcngc7rgwlf3whjfnzxialq
Robust personalizable spam filtering via local and global discrimination modeling
2012
Knowledge and Information Systems
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E-mail service providers have two options for automatic spam filtering at the service-side: a single global filter for all users or a personalized filter for each user. ...
The results demonstrate the robustness and effectiveness of our filter and its suitability for global and personalized spam filtering at the service-side. ...
We would also like to thank the anonymous reviewers for their helpful feedback. ...
doi:10.1007/s10115-012-0477-x
fatcat:uwnwycedwjc2lm4oeue5cigvca
A Comprehensive Survey for Intelligent Spam Email Detection
2019
IEEE Access
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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. ...
INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering. VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. ...
Semi-supervised spam filtering systems have also demonstrated promise, even though not many attempts have been taken to construct such systems yet. ...
doi:10.1109/access.2019.2954791
fatcat:ikt6cayggbb2dkrm52fxzz2dqm
An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams
2015
Knowledge and Information Systems
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In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. ...
This e-offprint is for personal use only and shall not be self-archived in electronic repositories. ...
Acknowledgments The authors would like to thank the anonymous reviewers for their valuable comments and suggestions which improved the paper. ...
doi:10.1007/s10115-015-0837-4
fatcat:xo66au7lpvd6bivc6uw3rhuwva
A Study on Spam Detection Methods for Safe SMS Communication
2018
International Journal of Engineering & Technology
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The paper has explored the types of SMS spams, its threats and various filtration methods to detect the spam SMS. ...
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. ...
Author [11] has used the cluster adaptive method for semi-supervised analysis for spam message detection. ...
doi:10.14419/ijet.v7i3.12.16502
fatcat:ni53wyzmffcadca2ghrxginery
Semi-supervised Classification for Natural Language Processing
[article]
2014
arXiv
pre-print
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This study explores the possibilities and achievements as well as complexity and limitations of semi-supervised classification for several natural langue processing tasks like parsing, biomedical information ...
For instance, supervised classification exploits only labeled data that are expensive, often difficult to get, inadequate in quantity, and require human experts for annotation. ...
The results in 2006 ECML/PKDD spam discovery challenge [10] indicated that spam filters based on semi-supervised classification outperformed supervised filters. ...
arXiv:1409.7612v1
fatcat:r2qppm2xcbcm3o5hlzgjrkzube
A Machine Learning Method for Spam Detection in Twitter using Naive Bayes and ERF Algorithms
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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To combat with the issue of spams, there has been a lot of methods available, Yet, there is not a perfect effective solution for detect the Twitter spams with the exactness. ...
The vast increase in the popularity in the social media also makes the hackers to spam, thus causes the conceivable losses. ...
It is a kind of stream clustering which filter the spam by categorising tweets as spam and non spam clusters. In Stream clustering methods which cluster may have number of micro clusters. ...
doi:10.35940/ijitee.f4729.049620
fatcat:eghpqql7mzbqxmtym5yqeivjie
SoK: Applying Machine Learning in Security - A Survey
[article]
2016
arXiv
pre-print
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Our examinations lead to 1) a taxonomy on ML paradigms and security domains for future exploration and exploitation, and 2) an agenda detailing open and upcoming challenges. ...
Consequently, research on applying and designing ML algorithms and systems for security has grown fast, ranging from intrusion detection systems(IDS) and malware classification to security policy management ...
Mostly using supervised learning paradigm, NB, SVM with different kernels, and LR are popular ML classifiers for filtering spam and phishing. ...
arXiv:1611.03186v1
fatcat:hfvc5hhu7ze77lrnjufslcg6gm
PSSF: A Novel Statistical Approach for Personalized Service-side Spam Filtering
2007
IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)
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Since the distribution of spam and non-spam e-mails is often different for different users a single filter trained on a general corpus is not optimal for all users. ...
We address this question by presenting PSSF, a novel statistical approach for personalized service-side spam filtering. ...
Cheng and Li present a semi-supervised classifier ensemble approach for the personalized spam filtering problem [4] . ...
doi:10.1109/wi.2007.47
dblp:conf/webi/JunejoK07
fatcat:wrbo2zidazbefnvumr52f2yl5y
An Improved Feature Selection Method for Short Text Classification
2019
Journal of Physics, Conference Series
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Thus, a modified Genetic Algorithm (GA) for improve feature selection and Artificial Immune System (AIS) algorithm was proposed for effective text classification in mobile short messages. ...
The exponential growth of text documents shared among users globally has increased the threat of misclassification associated with mobile devices such as Spam, Phishing, License to kill, Malware and privacy ...
Authors [4] improved text classification using semi-supervised clustering approach. The method uses labeled text for clustering and unlabeled text was used to adapt to centroids. ...
doi:10.1088/1742-6596/1235/1/012021
fatcat:abskwxcawrg2hdmrujmhypnjei
Automatic Personalized Spam Filtering through Significant Word Modeling
2007
19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007)
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We present an automatic approach for personalized spam filtering that does not require users' feedback. ...
A personalized spam filter is built by taking into account the characteristics of e-mails in individual users' inboxes. ...
Cheng and Li present a semi-supervised classifier ensemble approach for the personalized spam filtering problem [19] . ...
doi:10.1109/ictai.2007.66
dblp:conf/ictai/JunejoK07
fatcat:ucyps6ixrnfk5jexny66sar75i
A STUDY OF SPAM DETECTION ALGORITHM ON SOCIAL MEDIA NETWORKS
2014
Journal of Computer Science
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The growing popularity of social networking sites has made them prime targets for spammers. ...
Therefore, this study attempts to review various spam detection frameworks which deals about the detection and elimination of spams in various sources. ...
Here, spam comment is an irrelevant response received for a blog post in the form of a comment. This comments are analyzed using supervised and semi supervised methods. ...
doi:10.3844/jcssp.2014.2135.2140
fatcat:56u4f2hs75gdbl2w5yjyoagfym
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