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MapReduce based RDF assisted distributed SVM for high throughput spam filtering
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic 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 Filterin
A engenharia social e os perigos do phishing
A Engenharia Social e a técnica do phishing são temas que têm evoluído cada mais ao longo dos anos, principalmente através do email, uma das ferramentas mais utilizadas no mundo. Os emails de phishing normalmente estão relacionadas com Engenharia Social e podem-se propagar através de links e/ou anexos contidos neste tipo de email. O utilizador quando faz download de um anexo, pode estar automaticamente a descarregar software malicioso e dar ao atacante (hacker), o controlo total do computador, sem que se aperceba. Através dos links, o utilizador pode divulgar as suas credenciais ou outro tipo de informação pessoal/confidencial, uma vez que pode não perceber que está a ser redirecionado para um remetente malicioso.
Diversos estudos já realizados indicam que existem cada vez mais ataques deste tipo e cada vez com mais impacto na população. Por seu lado, a população não está ciente dos perigos que poderá encontrar ao carregar neste tipo de emails ou noutra forma de propagação de phishing.
A presente dissertação aborda o tema do phishing através do email e pretende definir métodos de prevenção para este tipo de crime informático. Numa primeira fase foram realizadas entrevistas a profissionais da área de Segurança Informática, com intuito de perceber mais sobre este tema. Posteriormente, realizou-se um questionário online, de forma a averiguar o conhecimento dos inquiridos em relação a este tema e identificar medidas que são usadas por eles antes e após um ataque informático. No final serão feitas as conclusões de forma a atingir os objetivos desta investigação.Social Engineering and phishing technique are subjects that have been evolving as the years pass, mainly through email, which is one of the most used communication tools in the world. Phishing emails are usually related to Social Engineering and can be propagated through links and/or attachments contained in this type of email. When downloading an attachment, the user can automatically activate the malicious software and allow the attacker (hacker), the complete control of the computer, without being aware of it. Through the links, you may disclose your credentials or other personal/confidential information, as you may not notice that you are being redirected to a malicious sender.
Several studies already carried out indicate that there are more and more attacks of this kind and with increasing impact on the population. On the other hand, the population is not aware of the dangers they may encounter when uploading this type of emails or other form of phishing propagation.
The present dissertation addresses the theme of phishing through email and aims to define prevention methods for this type of computer crime. Initially, interviews were conducted professionals in the area of Computer Security, in order to understand more about this topic. Subsequently, an online questionnaire was conducted to ascertain the respondents' knowledge of this topic and to identify measures that are used by them before and after a computer attack. In the end the conclusions will be made in order to reach the objectives of this investigation