Universiẗat, Diss.-2004--Würzburg, 2003. / Literaturverz. S. XXIII - LI. Verzeichnis zitierten Internetquellen S. LII - LVI.

Undersöker användbarheten av greylisting för att filtrera skräppost ur en myndighets perspektiv som har juridiska krav på sig att vara kontaktbara via epost och då har begränsningar i hur inkomna epost får filtreras. Genom att sätta upp en simulerad miljö så testas ett antal olika program för massutskick av epost och greylisting visar sig vara mycket effektivt när det kommer till att filtrera bort epost som skickas från klienter som inte till fullo stödjer SMTP's funktion för omsändningar enligt RFC. Greylisting har dock en inbyggd nackdel i sättet som skräposten filtreras och det är att samtlig epost från tidigare ej sedda avsändare kommer att fördröjas, i mina försök och med mina inställningar av Postgrey blev det en genomsnittlig fördröjning på ca 17min. / Investigates the usability of greylisting as a means of filtering spam emails in the perspective of a (swedish) government agency that has got legal obligations to be reachable by email and thus are limited in the ways incoming emails may be filtered. By setting up a virtual environment a few softwares for sending bulk mail are tested and greylisting shows to be a very effective when it comes to filter emails that are sent from clients that does not fully support the SMTP's functions for retransmission listed in the RFC. Greylisting has got an built in disadvantage in the way that email are filtered and that is that all emails from senders that has not been seen before will be delayed, in my tests and with my settings of Postgrey I got an average delay of approximately 17min.

Orientadores: Akebo Yamakami, Tiago Agostinho de Almeida / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T13:22:48Z (GMT). No. of bitstreams: 1 Silva_RenatoMoraes_M.pdf: 4136928 bytes, checksum: 218846058592353cb167c8c2d61e1bfd (MD5) Previous issue date: 2013 / Resumo: Com o crescente aumento do volume de informações disponíveis na Web, as ferramentas de busca tornam-se cada vez mais importantes para os usuários da Internet. Consequentemente, com o objetivo de se tornar mais visíveis, os sites concorrem entre si para ganhar melhores posições nos resultados das buscas feitas por esses usuários. Porém, muitos ganham maior visibilidade através de estratégias que enganam as ferramentas de busca. Esses sites, conhecidos como Web spam, causam prejuízos pessoais e econômicos aos usuários. Diante desse cenário, este trabalho apresenta uma análise do desempenho de diversos métodos de aprendizado de máquina aplicados na detecção automática de Web hosts que propagam Web spam. Os experimentos foram realizados usando duas bases de dados reais, públicas e de grande porte, das quais foram extraídos três diferentes conjuntos de vetores de atributos: baseados no conteúdo das páginas Web, baseados nos links das páginas Web e formados pela transformação dos atributos baseados nos links. Também foi analisada a viabilidade da redução de dimensionalidade do espaço dos atributos. Outra contribuição desse trabalho é a proposta de uma abordagem de classificação de Web spam, em que as predições obtidas com cada tipo de vetor de atributos são combinadas e uma decisão final é obtida usando-se voto majoritário simples. Os resultados obtidos indicam que os métodos de bagging de árvores de decisão, redes neurais perceptron de múltiplas camadas, floresta aleatória e boosting adaptativo de árvores de decisão são promissores na tarefa de detecção de Web spam. Além disso, verificou-se que os métodos de aprendizado tem melhor desempenho quando os vetores de atributos baseados no conteúdo e os vetores formados pela transformação dos atributos baseados nos links são combinados. Por fim, a combinação das predições obtidas com cada tipo de vetor de atributos gera bons resultados e por isso, essa é uma abordagem recomendada para o combate de Web spamming / Abstract: Due to the increasing volume of information available on the Web, search engines become increasingly important to Internet users. Consequently, with the purpose of becoming more visible, the Web sites compete to achieve better positions in the results of the searches made by such users. However, many of them achieve a good visibility through strategies that try to circumvent the search engines. This kind of Web sites are known as Web spam and they are responsible for personal injury and economic losses to users. Given this scenario, this work presents a performance analysis of established machine learning techniques employed to automatically detect Web hosts that disseminate Web spam. The experiments were performed with two real, public and large datasets, from which were extracted three different sets of features vectors: contentbased ones, link-based ones and features vectors generated by the transformation of the link-based features. We also analyzed the viability of the dimensionality reduction of the feature space. Another contribution of this work is the proposal of a Web spam classification approach which combines the predictions achieved by each type of features vector and using a simple majority voting. The results indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of spam hosts classification. Furthermore, we have conclude that the learning techniques perform better when we have combined the content-based features vectors and the features vectors generated by the transformation of the link-based features. Finally, the combination of the predictions achieved with each type of features vector has achieved superior results and therefore it is a recommended approach to automatically detect Web spam / Mestrado / Automação / Mestre em Engenharia Elétrica

The existance of spam email has gone from a fairly small amounts of afew hundred in the late 1970’s to several billions per day in 2010. Thiscontinually growing problem is of great concern to both businesses andusers alike.One attempt to combat this problem comes with a spam filtering toolcalled TRAP. The primary design goal of TRAP is to enable tracking ofthe reputation of mail senders in a decentralized and distributed fashion.In order for the tool to be useful, it is important that it does not haveany security issues that will let a spammer bypass the protocol or gain areputation that it should not have.As a piece of this puzzle, this thesis makes an analysis of TRAP’s protocoland design in order to find threats and vulnerabilies capable of bypassingthe protocol safeguards. Based on these threats we also evaluate possiblemitigations both by analysis and simulation. We have found that althoughthe protocol was not designed with regards to certain attacks on the systemitself most of the attacks can be fairly easily stopped.The analysis shows that by adding cryptographic defenses to the protocola lot of the threats would be mitigated. In those cases where cryptographywould not suffice it is generally down to sane design choices in the implementationas well as not always trusting that a node is being truthful andfollowing protocol.

The classifier discussed in this thesis considers the path traversed by an email (instead of its content) and reputation of the relays, features inaccessible to spammers. Groups of spammers and individual behaviors of a spammer in a given domain were analyzed to yield association patterns, which were then used to identify similar spammers. Unsolicited and phishing emails were successfully isolated from legitimate emails, using analysis results. Spammers and phishers are also categorized into serial spammers/phishers, recent spammers/phishers, prospective spammers/phishers, and suspects. Legitimate emails and trusted domains are classified into socially close (family members, friends), socially distinct (strangers etc), and opt-outs (resolved false positives and false negatives). Overall this classifier resulted in far less false positives when compared to current filters like SpamAssassin, achieving a 98.65% precision, which is well comparable to the precisions achieved by SPF, DNSRBL blacklists.

This thesis deals with spam in mobile and social networks. It focuses on spam in SMS messages and web service Twitter. Theoretical part provides brief overview of those two media, informations about what spam is, how to defend against it and where does it comes from. There is also a list of methods for spam detection, many of them have their roots in filtration of email communication. The rest of thesis is about design, implementation of application  for spam detection in SMS and Twitter messages and evaluation of its performance.

This thesis deals with various methods used for spam detection and identification. In the introduction various methods are described. Then Bayes' theorem and methods for spam detection that use this theorem are described in detail. This section also discusses biological and artificial immune systems and methods for spam detection based on artificial immune systems. Next sections contain the description of custom spam detection system design and implementation. Finally the system is tested and the results are evaluated.

This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dissertation critically discusses the advantages of having using the three tier model to combat spamming Botnets. / Dissertation (MSc)--University of Pretoria, 2013. / gm2014 / Computer Science / unrestricted

Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur "Rechnernetze und verteilte Systeme" der Fakultaet fuer Informatik der TU Chemnitz. Workshop-Thema: Mobilitaet Es werden Filtermöglichkeiten für E-Mails vorgestellt, um die E-Mail-Bearbeitung zu automatisieren und Spam-Mails und Mails mit gefährlichem Inhalt abzuwehren.

Workshop "Netz- und Service-Infrastrukturen" Dieser Beitrag zum Workshop "Netz- und Service-Infrastrukturen" 2004 analysiert den Stand der Anti-Spam-Maßnahmen an der TU Chemnitz und zeigt neue Techniken auf.