Brunel University Research Archive: MapReduce based RDF assisted distributed SVM for high throughput spam filtering

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/7572

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DC Field	Value	Language
dc.contributor.advisor	Li, M	-
dc.contributor.author	Caruana, Godwin	-
dc.date.accessioned	2013-07-10T13:41:02Z	-
dc.date.available	2013-07-10T13:41:02Z	-
dc.date.issued	2013	-
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/7572	-
dc.description	This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University	en_US
dc.description.abstract	Electronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filtering	en_US
dc.language.iso	en	en_US
dc.publisher	Brunel University School of Engineering and Design PhD Theses	-
dc.relation.uri	http://bura.brunel.ac.uk/bitstream/2438/7572/1/FulltextThesis.pdf	-
dc.subject	Distributed SVM	en_US
dc.subject	High througput	en_US
dc.title	MapReduce based RDF assisted distributed SVM for high throughput spam filtering	en_US
dc.type	Thesis	en_US
Appears in Collections:	Electronic and Electrical Engineering Dept of Electronic and Electrical Engineering Theses

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