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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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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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Mitigating spam using network-level featuresRamachandran, Anirudh Vadakkedath 04 August 2011 (has links)
Spam is an increasing menace in email: 90% of email is spam, and over 90% of spam is sent by botnets---networks of compromised computers under the control of miscreants. In this dissertation, we introduce email spam filtering using network-level features of spammers. Network-level features are based on lightweight measurements that can be made in the network, often without processing or storing a message. These features stay relevant for longer periods, are harder for criminals to alter at will (e.g., a bot cannot act independently of other bots in the botnet), and afford the unique opportunity to observe the coordinated behavior of spammers.
We find that widely-used IP address-based reputation systems (e.g., IP blacklists) cannot keep up with the threats of spam from previously unseen IP addresses, and from new and stealthy attacks---to thwart IP-based reputation systems, spammers are reconnoitering IP Blacklists and sending spam from hijacked IP address space. Finally, spammers are "gaming" collaborative filtering by users in Web-based email by casting fraudulent "Not Spam" votes on spam email.
We present three systems that detect each attack that uses spammer behavior rather than their IP address. First, we present IP blacklist counter-intelligence, a system that can passively enumerate spammers performing IP blacklist reconnaissance. Second, we present SpamTracker, a system that distinguishes spammers from legitimate senders by applying clustering on the set of domains to which email is sent. Third, we analyze vote-gaming attacks in large Web-based email systems that pollutes user feedback on spam emails, and present an efficient clustering-based method to mitigate such attacks.
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Policy-controlled email servicesKaushik, Saket. January 2007 (has links)
Thesis (Ph. D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Jan. 18, 2008). Thesis directors: Paul Amman, Duminda Wijesekera. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 198. Includes bibliographical references (p. 189-197). Also available in print.
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A spam-detecting artificial immune system /Oda, Terri January 1900 (has links)
Thesis (M.C.S.)--Carleton University, 2005. / Includes bibliographical references (p. 115-123). Also available in electronic format on the Internet.
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