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A study on combating the problem of unsolicited electronic messages in Hong KongCheung, Pak-to, Patrick. January 2007 (has links)
Thesis (M.P.A.)--University of Hong Kong, 2007. / Title from title frame. Also available in printed format.
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Methoden der Spambekämpfung und -vermeidung /Eggendorfer, Tobias. January 2007 (has links)
Zugl.: Hagen, FernUniversiẗat, Diss., 2007.
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The socio-economic impact of unsolicited bulk email (spam) on New Zealand organisations and employees : comparative case studies. A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Computing at Unitec New Zealand /Foster, Brian. January 2007 (has links)
Thesis (M.Comp.)--Unitec New Zealand, 2007. / Includes bibliographical references (leaves 223-230).
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Investigating unsupervised feature learning for email spam classificationDiale, Melvin January 2017 (has links)
A dissertation submitted in partial ful llment of the requirements for the degree
Master of Science.
School of Computer Science and Applied Mathematics,
Faculty of Science,
University of the Witwatersrand, Johannesburg.
November 2017 / In the cyberspace, spam emails are used as a way to divulge sensitive information of
victims through social engineering. There are various classi cation systems that have
been employed previously to identify spam emails. The primary objective of email spam
classi cation systems is to classify incoming email as either legitimate (non-spam) or
spam emails. The spam classi cation task can thus be regarded as a two-class classi
cation problem. This kind of a problem involves the use of various classi ers such
as Decision Trees (DTs) and Support Vector Machines (SVMs). DTs and SVMs have
been shown to perform well on email spam classi cation tasks. Several studies have
failed to mention how these classi ers were optimized in terms of their hyperparameters.
As a result, poor performance was encountered with complex datasets. This is
because SVM classi er is dependent on the selection of the kernel function and the optimization
of kernel hyperparameters. Additionally, many studies on spam email ltering
task use words and characters to compute Term-Frequency (TF) based feature space.
However, TF based feature space leads to sparse representation due to the continuous
vocabulary growth. This problem is linked with the curse of dimensionality. Overcoming
dimensionality issues involves the use of feature reduction techniques. Traditional
feature reduction techniques, for instance, Information Gain (IG) may cause feature
representations to lose important features for identifying spam emails. This proposed
study demonstrates the use of Distributed Memory (DM), Distributed Bag of Words
(DBOW), Cosine Similarity (CS) and Autoencoder for feature representation to retain
a better class separability. Generated features enable classi ers to identify spam emails
in a lower dimension feature space. The use of the Autoencoder for feature reduction led
to improved classi cation performance. Furthermore, a comparison of kernel functions
and CS measure is taken into consideration to evaluate their impacts on classi ers when
employed for feature transformation. The study further shows that removal of more
frequent words, which have been regarded as noisy words and stemming process, may
negatively a ect the performance of the classi ers when word order is taken into consideration.
In addition, this study investigates the performance of DTs and SVM classi ers
on the publicly available datasets. This study makes a further investigation on the selection
of optimal kernel function and optimization of kernel hyperparameters for each
feature representation. It is further investigated whether the use of Stacked Autoencoder
as a pre-processing step for multilayer perceptron (MLP) will lead to improved
classi cation results. / MT 2018
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Temporal data mining in a dynamic feature space /Wenerstrom, Brent, January 2006 (has links) (PDF)
Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 43-45).
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Towards eradication of SPAM : a study on intelligent adaptive SPAM filters /Hassan, Tarek. January 2006 (has links)
Thesis (M. Computer Sci.)--Murdoch University, 2006. / Thesis submitted to the Division of Arts. Includes bibliographical references (leaves 95-102).
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A study on combating the problem of unsolicited electronic messages inHong KongCheung, Pak-to, Patrick., 張伯陶. January 2007 (has links)
published_or_final_version / abstract / Public Administration / Master / Master of Public Administration
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Models to combat email spam botnets and unwanted phone callsHusna, Husain. Dantu, Ram, January 2008 (has links)
Thesis (M.S.)--University of North Texas, May, 2008. / Title from title page display. Includes bibliographical references.
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E-Mail-Kategorisierung und Spam-Detektion mit SENTRAX [Mustererkennung mit Assoziativmatrizen]Frobese, Dirk T. January 2009 (has links)
Zugl.: Hildesheim, Univ., Diss., 2009
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Skräppost eller skinka? : En jämförande studie av övervakade maskininlärningsalgoritmer för spam och ham e-mailklassifikation / Spam or ham? : A comparative study of monitored machine learning algorithms for spam and ham e-mail classification.Bergens, Simon, Frykengård, Pontus January 2019 (has links)
Spam messages in the form of e-mail is a growing problem in today's businesses. It is a problem that costs time and resources to counteract. Research into this has been done to produce techniques and tools aimed at addressing the growing number on incoming spam e-mails. The research on different algorithms and their ability to classify e-mail messages needs an update since both tools and spam e-mails have become more advanced. In this study, three different machine learning algorithms have been evaluated based on their ability to correctly classify e-mails as legitimate or spam. These algorithms are naive Bayes, support vector machine and decision tree. The algorithms are tested in an experiment with the Enron spam dataset and are then compared against each other in their performance. The result of the experiment was that support vector machine is the algorithm that correctly classified most of the data points. Even though support vector machine has the largest percentage of correctly classified data points, other algorithms can be useful from a business perspective depending on the task and context.
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