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1
Hao, Shuang. "Early detection of spam-related activity." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53091.
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Spam, the distribution of unsolicited bulk email, is a big security threat on the Internet. Recent studies show approximately 70-90% of the worldwide email traffic—about 70 billion messages a day—is spam. Spam consumes resources on the network and at mail servers, and it is also used to launch other attacks on users, such as distributing malware or phishing. Spammers have increased their virulence and resilience by sending spam from large collections of compromised machines (“botnets”). Spammers also make heavy use of URLs and domains to direct victims to point-of-sale Web sites, and miscreants register large number of domains to evade blacklisting efforts. To mitigate the threat of spam, users and network administrators need proactive techniques to distinguish spammers from legitimate senders and to take down online spam-advertised sites.
In this dissertation, we focus on characterizing spam-related activities and developing systems to detect them early. Our work builds on the observation that spammers need to acquire attack agility to be profitable, which presents differences in how spammers and legitimate users interact with Internet services and exposes detectable during early period of attack. We examine several important components across the spam life cycle, including spam dissemination that aims to reach users' inboxes, the hosting process during which spammers set DNS servers and Web servers, and the naming process to acquire domain names via registration services.
We first develop a new spam-detection system based on network-level features of spamming bots. These lightweight features allow the system to scale better and to be more robust. Next, we analyze DNS resource records and lookups from top-level domain servers during the initial stage after domain registrations, which provides a global view across the Internet to characterize spam hosting infrastructure. We further examine the domain registration process and present the unique registration behavior of spammers. Finally, we build an early-warning system to identify spammer domains at time-of-registration rather than later at time-of-use.
We have demonstrated that our detection systems are effective by using real-world datasets. Our work has also had practical impact. Some of the network-level features that we identified have since been incorporated into spam filtering products at Yahoo! and McAfee, and our work on detecting spammer domains at time-of-registration has directly influenced new projects at Verisign to investigate domain registrations.
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Sheikhalishahi, Mina. "Spam campaign detection, analysis, and formalization." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26935.
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Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2016-2017Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.
Spam emails yearly impose extremely heavy costs in terms of time, storage space, and money to both private users and companies. To effectively fight the problem of spam emails, it is not enough to stop spam messages to be delivered to end user inbox or be collected in spam box. It is mandatory either to try to find and persecute the spammers, generally hiding behind complex networks of infected devices, which send spam emails against their user will, i.e. botnets; or analyze the spammer behavior to find appropriate strategies against it. However, such a task is difficult due to the camouflage techniques, which makes necessary a manual analysis of correlated spam emails to find the spammers. To facilitate such an analysis, which should be performed on large amounts of unclassified raw emails, we propose a categorical clustering methodology, named CCTree, to divide large amount of spam emails into spam campaigns by structural similarity. We show the effectiveness and efficiency of our proposed clustering algorithm through several experiments. Afterwards, a self-learning approach is proposed to label spam campaigns based on the goal of spammer, e.g. phishing. The labeled spam campaigns are used to train a classifier, which can be applied in classifying new spam emails. Furthermore, the labeled campaigns, with the set of four more ranking features, are ordered according to investigators priorities. A semiring-based structure is proposed to abstract CCTree representation. Through several theorems we show under some conditions the proposed approach fully abstracts the tree representation. The abstract schema of CCTree, named CCTree term, is applied to formalize CCTree parallelism. Through a number of mathematical analysis and experimental results, we show the efficiency and effectiveness of our proposed framework as an automatic tool for spam campaign detection, labeling, ranking, and formalization.
3
Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.
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Wu, Hao. "Detecting spam relays by SMTP traffic characteristics using an autonomous detection system." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/10926.
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Spam emails are flooding the Internet. Research to prevent spam is an ongoing concern. SMTP traffic was collected from different sources in real networks and analyzed to determine the difference regarding SMTP traffic characteristics of legitimate email clients, legitimate email servers and spam relays. It is found that SMTP traffic from legitimate sites and non-legitimate sites are different and could be distinguished from each other. Some methods, which are based on analyzing SMTP traffic characteristics, were purposed to identify spam relays in the network in this thesis. An autonomous combination system, in which machine learning technologies were employed, was developed to identify spam relays in this thesis. This system identifies spam relays in real time before spam emails get to an end user by using SMTP traffic characteristics never involving email real content. A series of tests were conducted to evaluate the performance of this system. And results show that the system can identify spam relays with a high spam relay detection rate and an acceptable ratio of false positive errors.
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Jaroš, Ján. "Detekce nevyžádaných zpráv v mobilní komunikaci a na sociálních sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236082.
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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.
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Lam, Ho-Yu. "A learning approach to spam detection based on social networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LAM.
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Nachenahalli, Bhuthegowda Bharath Kumar. "Methods for Analyzing the Evolution of Email Spam." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24213.
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Email spam has steadily grown and has become a major problem for users, email service providers, and many other organizations. Many adversarial methods have been proposed to combat spam and various studies have been made on the evolution of email spam, by finding evolution patterns and trends based on historical spam data and by incorporating spam filters. In this thesis, we try to understand the evolution of email spam and how we can build better classifiers that will remain effective against adaptive adversaries like spammers. We compare various methods for analyzing the evolution of spam emails by incorporating spam filters along with a spam dataset. We explore the trends based on the weights of the features learned by the classifiers and the accuracies of the classifiers trained and tested in different settings. We also evaluate the effectiveness of the classifier trained in adversarial settings on synthetic data.
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Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.
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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
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Neuwirth, David. "Realizace spamového filtru na bázi umělého imunitního systému." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236637.
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Unsolicited e-mails generally present a major problem within the e-mail communication nowadays. There exist several methods that can detect spam and distinguish it from the requested messages. The theoretical part of the masters thesis introduces the ways of detecting unsolicited messages by using artificial immune systems. It presents and subsequently analyses several methods of the artificial immune systems that can assist in the fight against spam. The practical part of the masters thesis deals with the implementation of a spam filter on the basis of the artificial immune systems. The project ends with comparison of effectiveness of the newly designed spam filter and the one which uses common methods for spam detection.
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Hayati, Pedram. "Addressing the new generation of spam (Spam 2.0) through Web usage models." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/850.
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New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem.
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Singh, Kuldeep. "An Investigation of Spam Filter Optimaltiy : based on Signal Detection Theory." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9960.
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Unsolicited bulk email, commonly known as spam, represents a significant problem on the Internet. The seriousness of the situation is reflected by the fact that approximately 97% of the total e-mail traffic currently (2009) is spam. To fight this problem, various anti-spam methods have been proposed and are implemented to filter out spam before it gets delivered to recipients, but none of these methods are entirely satisfactory. This thesis analyzes the properties of spam filters from the viewpoint of Signal Detection Theory (SDT). The Bayesian approach of Signal Detection Theory provides a basis for determining the tuning of spam filters from the particular user's point of view and helps in determining the utility which the spam filter provides to the user.
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Tan, Enhua. "Spam Analysis and Detection for User Generated Content in Online Social Networks." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365520334.
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Sun, Yingcheng. "Topic Modeling and Spam Detection for Short Text Segments in Web Forums." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1575281495398615.
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Wang, De. "Analysis and detection of low quality information in social networks." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53991.
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Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. As social networks like Facebook, Twitter and Google+ have become important communication platforms in people's daily lives, malicious users make them as major targets to pollute with low quality information, which we also call as Denial of Information (DoI) attacks. How to analyze and detect low quality information in social networks for preventing DoI attacks is the major research problem I will address in this dissertation.
Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My work contains three major parts: 1). analytics and detection framework of low quality information, 2). evolutionary study of low quality information, and 3). detection approaches of low quality information. In part I, I proposed social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. In part II, I performed a large-scale evolutionary study on web page spam and email spam over a long period of time. In part III, I designed three detection approaches used in detecting low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumors in social networks. Our study shows promising results in analyzing and detecting low quality information in social networks.
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Washha, Mahdi. "Information quality in online social media and big data collection : an example of Twitter spam detection." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30080/document.
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La popularité des médias sociaux en ligne (Online Social Media - OSM) est fortement liée à la qualité du contenu généré par l'utilisateur (User Generated Content - UGC) et la protection de la vie privée des utilisateurs. En se basant sur la définition de la qualité de l'information, comme son aptitude à être exploitée, la facilité d'utilisation des OSM soulève de nombreux problèmes en termes de la qualité de l'information ce qui impacte les performances des applications exploitant ces OSM. Ces problèmes sont causés par des individus mal intentionnés (nommés spammeurs) qui utilisent les OSM pour disséminer des fausses informations et/ou des informations indésirables telles que les contenus commerciaux illégaux. La propagation et la diffusion de telle information, dit spam, entraînent d'énormes problèmes affectant la qualité de services proposés par les OSM. La majorité des OSM (comme Facebook, Twitter, etc.) sont quotidiennement attaquées par un énorme nombre d'utilisateurs mal intentionnés. Cependant, les techniques de filtrage adoptées par les OSM se sont avérées inefficaces dans le traitement de ce type d'information bruitée, nécessitant plusieurs semaines ou voir plusieurs mois pour filtrer l'information spam. En effet, plusieurs défis doivent être surmontées pour réaliser une méthode de filtrage de l'information bruitée . Les défis majeurs sous-jacents à cette problématique peuvent être résumés par : (i) données de masse ; (ii) vie privée et sécurité ; (iii) hétérogénéité des structures dans les réseaux sociaux ; (iv) diversité des formats du UGC ; (v) subjectivité et objectivité. Notre travail s'inscrit dans le cadre de l'amélioration de la qualité des contenus en termes de messages partagés (contenu spam) et de profils des utilisateurs (spammeurs) sur les OSM en abordant en détail les défis susmentionnés. Comme le spam social est le problème le plus récurant qui apparaît sur les OSM, nous proposons deux approches génériques pour détecter et filtrer le contenu spam : i) La première approche consiste à détecter le contenu spam (par exemple, les tweets spam) dans un flux en temps réel. ii) La seconde approche est dédiée au traitement d'un grand volume des données relatives aux profils utilisateurs des spammeurs (par exemple, les comptes Twitter)The popularity of OSM is mainly conditioned by the integrity and the quality of UGC as well as the protection of users' privacy. Based on the definition of information quality as fitness for use, the high usability and accessibility of OSM have exposed many information quality (IQ) problems which consequently decrease the performance of OSM dependent applications. Such problems are caused by ill-intentioned individuals who misuse OSM services to spread different kinds of noisy information, including fake information, illegal commercial content, drug sales, mal- ware downloads, and phishing links. The propagation and spreading of noisy information cause enormous drawbacks related to resources consumptions, decreasing quality of service of OSM-based applications, and spending human efforts. The majority of popular social networks (e.g., Facebook, Twitter, etc) over the Web 2.0 is daily attacked by an enormous number of ill-intentioned users. However, those popular social networks are ineffective in handling the noisy information, requiring several weeks or months to detect them. Moreover, different challenges stand in front of building a complete OSM-based noisy information filtering methods that can overcome the shortcomings of OSM information filters. These challenges are summarized in: (i) big data; (ii) privacy and security; (iii) structure heterogeneity; (iv) UGC format diversity; (v) subjectivity and objectivity; (vi) and service limitations In this thesis, we focus on increasing the quality of social UGC that are published and publicly accessible in forms of posts and profiles over OSNs through addressing in-depth the stated serious challenges. As the social spam is the most common IQ problem appearing over the OSM, we introduce a design of two generic approaches for detecting and filtering out the spam content. The first approach is for detecting the spam posts (e.g., spam tweets) in a real-time stream, while the other approach is dedicated for handling a big data collection of social profiles (e.g., Twitter accounts)
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Henke, Márcia. "Deteção de Spam baseada na evolução das características com presença de Concept Drift." Universidade Federal do Amazonas, 2015. http://tede.ufam.edu.br/handle/tede/4708.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Electronic messages (emails) are still considered the most significant tools in business and personal applications due to their low cost and easy access. However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users. Among the many problems caused by spam messages, we may highlight the fact that it is currently the main vector for the spread of malicious activities such as viruses, worms, trojans, phishing, botnets, among others. Such activities allow the attacker to have illegal access to penetrating data, trade secrets or to invade the privacy of the sufferers to get some advantage. Several approaches have been proposed to prevent sending unsolicited e-mail messages, such as filters implemented in e-mail servers, spam message classification mechanisms for users to define when particular issue or author is a source of spread of spam and even filters implemented in network electronics. In general, e-mail filter approaches are based on analysis of message content to determine whether or not a message is spam. A major problem with this approach is spam detection in the presence of concept drift. The literature defines concept drift as changes occurring in the concept of data over time, as the change in the features that describe an attack or occurrence of new features. Numerous Intrusion Detection Systems (IDS) use machine learning techniques to monitor the classification error rate in order to detect change. However, when detection occurs, some damage has been caused to the system, a fact that requires updating the classification process and the system operator intervention. To overcome the problems mentioned above, this work proposes a new changing detection method, named Method oriented to the Analysis of the Development of Attacks Characteristics (MECA). The proposed method consists of three steps: 1) classification model training; 2) concept drift detection; and 3) transfer learning. The first step generates classification models as it is commonly conducted in machine learning. The second step introduces two new strategies to avoid concept drift: HFS (Historical-based Features Selection) that analyzes the evolution of the features based on over time historical; and SFS (Similarity-based Features Selection) that analyzes the evolution of the features from the level of similarity obtained between the features vectors of the source and target domains. Finally, the third step focuses on the following questions: what, how and when to transfer acquired knowledge. The answer to the first question is provided by the concept drift detection strategies that identify the new features and store them to be transferred. To answer the second question, the feature representation transfer approach is employed. Finally, the transfer of new knowledge is executed as soon as changes that compromise the classification task performance are identified. The proposed method was developed and validated using two public databases, being one of the datasets built along this thesis. The results of the experiments shown that it is possible to infer a threshold to detect changes in order to ensure the classification model is updated through knowledge transfer. In addition, MECA architecture is able to perform the classification task, as well as the concept drift detection, as two parallel and independent tasks. Finally, MECA uses SVM machine learning algorithm (Support Vector Machines), which is less adherent to the training samples. The results obtained with MECA showed that it is possible to detect changes through feature evolution monitoring before a significant degradation in classification models is achieved.
As mensagens eletrônicas (e-mails) ainda são consideradas as ferramentas de maior prestígio no meio empresarial e pessoal, pois apresentam baixo custo e facilidade de acesso. Por outro lado, os e-mails tornaram-se um grande problema devido à elevada quantidade de mensagens não desejadas, denominadas spam, que lotam as caixas de emails dos usuários. Dentre os diversos problemas causados pelas mensagens spam, destaca-se o fato de ser atualmente o principal vetor de propagação de atividades maliciosas como vírus, worms, cavalos de Tróia, phishing, botnets, dentre outros. Tais atividades permitem ao atacante acesso indevido a dados sigilosos, segredos de negócios ou mesmo invadir a privacidade das vítimas para obter alguma vantagem. Diversas abordagens, comerciais e acadêmicas, têm sido propostas para impedir o envio de mensagens de e-mails indesejados como filtros implementados nos servidores de e-mail, mecanismos de classificação de mensagens de spam para que os usuários definam quando determinado assunto ou autor é fonte de propagação de spam e até mesmo filtros implementados em componentes eletrônicos de rede. Em geral, as abordagens de filtros de e-mail são baseadas na análise do conteúdo das mensagens para determinar se tal mensagem é ou não um spam. Um dos maiores problemas com essa abordagem é a deteção de spam na presença de concept drift. A literatura conceitua concept drift como mudanças que ocorrem no conceito dos dados ao longo do tempo como a alteração das características que descrevem um ataque ou ocorrência de novas características. Muitos Sistemas de Deteção de Intrusão (IDS) usam técnicas de aprendizagem de máquina para monitorar a taxa de erro de classificação no intuito de detetar mudança. Entretanto, quando a deteção ocorre, algum dano já foi causado ao sistema, fato que requer atualização do processo de classificação e a intervenção do operador do sistema. Com o objetivo de minimizar os problemas mencionados acima, esta tese propõe um método de deteção de mudança, denominado Método orientado à Análise da Evolução das Características de Ataques (MECA). O método proposto é composto por três etapas: 1) treino do modelo de classificação; 2) deteção de mudança; e 3) transferência do aprendizado. A primeira etapa emprega modelos de classificação comumente adotados em qualquer método que utiliza aprendizagem de máquina. A segunda etapa apresenta duas novas estratégias para contornar concept drift: HFS (Historical-based Features Selection) que analisa a evolução das características com base no histórico ao longo do tempo; e SFS (Similarity based Features Selection) que observa a evolução das características a partir do nível de similaridade obtido entre os vetores de características dos domínios fonte e alvo. Por fim, a terceira etapa concentra seu objetivo nas seguintes questões: o que, como e quando transferir conhecimento adquirido. A resposta à primeira questão é fornecida pelas estratégias de deteção de mudança, que identificam as novas características e as armazenam para que sejam transferidas. Para responder a segunda questão, a abordagem de transferência de representação de características é adotada. Finalmente, a transferência do novo conhecimento é realizada tão logo mudanças que comprometam o desempenho da tarefa de classificação sejam identificadas. O método MECA foi desenvolvido e validado usando duas bases de dados públicas, sendo que uma das bases foi construída ao longo desta tese. Os resultados dos experimentos indicaram que é possível inferir um limiar para detetar mudanças a fim de garantir o modelo de classificação sempre atualizado por meio da transferência de conhecimento. Além disso, um diferencial apresentado no método MECA é a possibilidade de executar a tarefa de classificação em paralelo com a deteção de mudança, sendo as duas tarefas independentes. Por fim, o MECA utiliza o algoritmo de aprendizagem de máquina SVM (Support Vector Machines), que é menos aderente às amostras de treinamento. Os resultados obtidos com o MECA mostraram que é possível detetar mudanças por meio da evolução das características antes de ocorrer uma degradação significativa no modelo de classificação utilizado.
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Prasse, Paul [Verfasser], and Tobias [Akademischer Betreuer] Scheffer. "Pattern recognition for computer security : discriminative models for email spam campaign and malware detection / Paul Prasse ; Betreuer: Tobias Scheffer." Potsdam : Universität Potsdam, 2016. http://d-nb.info/1218793066/34.
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Henek, Jan. "Proxy servery v síti Internet." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241977.
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The goal of this paper is to analyze the representation of proxy servers in cyber attacks conducted by Internet. For this purpose I used method which compares tested IP address with database of open proxy servers. I assembled a list of IP address taken from the blacklist of cyber attacks committed in 2015. Then I checked this list with the created program Proxy checker and compared them with a database of open proxy servers. By measurement I demonstrate the inefficacy of this method for reverse detection of proxy servers in the IP list of past attacks.
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Alghamdi, Bandar Abdulrahman. "Topic-based feature selection and a hybrid approach for detecting spammers on twitter." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204112/1/Bandar%20Abdulrahman%20A_Alghamdi_Thesis.pdf.
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This thesis is an application of text mining techniques on Twitter to detect harmful users known as spam users. It examines users' posted content and characteristics to understand harmful activities and detect them. The thesis proposed methods to identify a set of new features that can accurately represent users' behavior, and also proposed a novel two-stage approach to detect spam users based on the features. The experiments conducted in the thesis work showed the effectiveness of the proposed features and the two-stage approach in detecting spam users. The thesis work made contributions to creating more safe and healthy social networks.
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Andla, Christoffer. "Detecting Spam Emails." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200517.
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Shekar, Chandra. "Detecting Spam in Microblogs." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1311113194.
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Younis, Zaki Mohamed. "An ontological approach for monitoring and surveillance systems in unregulated markets." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/an-ontological-approach-for-monitoring-and-surveillance-systems-in-unregulated-markets(056f8010-08b2-4eb0-a75d-5301b899ec90).html.
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Ontologies are a key factor of Information management as they provide a common representation to any domain. Historically, finance domain has suffered from a lack of efficiency in managing vast amounts of financial data, a lack of communication and knowledge sharing between analysts. Particularly, with the growth of fraud in financial markets, cases are challenging, complex, and involve a huge volume of information. Gathering facts and evidence is often complex. Thus, the impetus for building a financial fraud ontology arises from the continuous improvement and development of financial market surveillance systems with high analytical capabilities to capture frauds which is essential to guarantee and preserve an efficient market.This thesis proposes an ontology-based approach for financial market surveillance systems. The proposed ontology acts as a semantic representation of mining concepts from unstructured resources and other internet sources (corpus). The ontology contains a comprehensive concept system that can act as a semantically rich knowledge base for a market monitoring system. This could help fraud analysts to understand financial fraud practices, assist open investigation by managing relevant facts gathered for case investigations, providing early detection techniques of fraudulent activities, developing prevention practices, and sharing manipulation patterns from prosecuted cases with investigators and relevant users. The usefulness of the ontology will be evaluated through three case studies, which not only help to explain how manipulation in markets works, but will also demonstrate how the ontology can be used as a framework for the extraction process and capturing information related to financial fraud, to improve the performance of surveillance systems in fraud monitoring. Given that most manipulation cases occur in the unregulated markets, this thesis uses a sample of fraud cases from the unregulated markets. On the empirical side, the thesis presents examples of novel applications of text-mining tools and data-processing components, developing off-line surveillance systems that are fully working prototypes which could train the ontology in the most recent manipulation techniques.
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Goh, Kwang Leng. "Methods for demoting and detecting Web spam." Thesis, Curtin University, 2013. http://hdl.handle.net/20.500.11937/1481.
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Web spamming has tremendously subverted the ranking mechanism of information retrieval in Web search engines. It manipulates data source maliciously either by contents or links with the intention of contributing negative impacts to Web search results. The altering order of the search results by spammers has increased the difficulty level of searching and time consumption for Web users to retrieve relevant information. In order to improve the quality of Web search engines results, the design of anti-Web spam techniques are developed in this thesis to detect and demote Web spam via trust and distrust and Web spam classification.A comprehensive literature on existing anti-Web spam techniques emphasizing on trust and distrust model and machine learning model is presented. Furthermore, several experiments are conducted to show the vulnerability of ranking algorithm towards Web spam. Two public available Web spam datasets are used for the experiments throughout the thesis - WEBSPAM-UK2006 and WEBSPAM-UK2007.Two link-based trust and distrust model algorithms are presented subsequently: Trust Propagation Rank and Trust Propagation Spam Mass. Both algorithms semi automatically detect and demote Web spam based on limited human experts’ evaluation of non-spam and spam pages. In the experiments, the results for Trust Propagation Rank and Trust Propagation Spam Mass have achieved up to 10.88% and 43.94% improvement over the benchmark algorithms.Thereafter, the weight properties which associated as the linkage between two Web hosts are introduced into the task of Web spam detection. In most studies, the weight properties are involved in ranking mechanism; in this research work, the weight properties are incorporated into distrust based algorithms to detect more spam. The experiments have shown that the weight properties enhanced existing distrust based Web spam detection algorithms for up to 30.26% and 31.30% on both aforementioned datasets.Even though the integration of weight properties has shown significant results in detecting Web spam, the discussion on distrust seed set propagation algorithm is presented to further enhance the Web spam detection experience. Distrust seed set propagation algorithm propagates the distrust score in a wider range to estimate the probability of other unevaluated Web pages for being spam. The experimental results have shown that the algorithm improved the distrust based Web spam detection algorithms up to 19.47% and 25.17% on both datasets.An alternative machine learning classifier - multilayered perceptron neural network is proposed in the thesis to further improve the detection rate of Web spam. In the experiments, the detection rate of Web spam using multilayered perceptron neural network has increased up to 14.02% and 3.53% over the conventional classifier – support vector machines. At the same time, a mechanism to determine the number of hidden neurons for multilayered perceptron neural network is presented in this thesis to simplify the designing process of network structure.
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Rajdev, Meet. "Fake and Spam Messages: Detecting Misinformation During Natural Disasters on Social Media." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4462.
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During natural disasters or crises, users on social media tend to easily believe contents of postings related to the events, and retweet the postings, hoping that the postings will be reached by many other users. Unfortunately, there are malicious users who understand the tendency and post misinformation such as spam and fake messages with expecting wider propagation. To resolve the problem, in this paper we conduct a case study of the 2013 Moore Tornado and Hurricane Sandy. Concretely, we (i) understand behaviors of these malicious users; (ii) analyze properties of spam, fake and legitimate messages; (iii) propose at and hierarchical classification approaches; and (iv) detect both fake and spam messages with even distinguishing between them. Our experimental results show that our proposed approaches identify spam and fake messages with 96.43% accuracy and 0.961 F-measure.
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Ferreira, Uchoa Marina. "Detecting Fake Reviews with Machine Learning." Thesis, Högskolan Dalarna, Mikrodataanalys, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-28133.
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Many individuals and businesses make decisions based on freely and easily accessible online reviews. This provides incentives for the dissemination of fake reviews, which aim to deceive the reader into having undeserved positive or negative opinions about an establishment or service. With that in mind, this work proposes machine learning applications to detect fake online reviews from hotel, restaurant and doctor domains. In order to _lter these deceptive reviews, Neural Networks and Support Vector Ma- chines are used. Both algorithms' parameters are optimized during training. Parameters that result in the highest accuracy for each data and feature set combination are selected for testing. As input features for both machine learning applications, unigrams, bigrams and the combination of both are used. The advantage of the proposed approach is that the models are simple yet yield results comparable with those found in the literature using more complex models. The highest accuracy achieved was with Support Vector Machine using the Laplacian kernel which obtained an accuracy of 82.92% for hotel, 80.83% for restaurant and 73.33% for doctor reviews.
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Kolan, Prakash. "System and Methods for Detecting Unwanted Voice Calls." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc5155/.
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Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packet-based IP networks. However, this migration is successful only if the present problems in IP networks are addressed before deploying VoIP infrastructure on a large scale. One of the important issues that the present VoIP networks face is the problem of unwanted calls commonly referred to as SPIT (spam over Internet telephony). Mostly, these SPIT calls are from unknown callers who broadcast unwanted calls. There may be unwanted calls from legitimate and known people too. In this case, the unwantedness depends on social proximity of the communicating parties. For detecting these unwanted calls, I propose a framework that analyzes incoming calls for unwanted behavior. The framework includes a VoIP spam detector (VSD) that analyzes incoming VoIP calls for spam behavior using trust and reputation techniques. The framework also includes a nuisance detector (ND) that proactively infers the nuisance (or reluctance of the end user) to receive incoming calls. This inference is based on past mutual behavior between the calling and the called party (i.e., caller and callee), the callee's presence (mood or state of mind) and tolerance in receiving voice calls from the caller, and the social closeness between the caller and the callee. The VSD and ND learn the behavior of callers over time and estimate the possibility of the call to be unwanted based on predetermined thresholds configured by the callee (or the filter administrators). These threshold values have to be automatically updated for integrating dynamic behavioral changes of the communicating parties. For updating these threshold values, I propose an automatic calibration mechanism using receiver operating characteristics curves (ROC). The VSD and ND use this mechanism for dynamically updating thresholds for optimizing their accuracy of detection. In addition to unwanted calls to the callees in a VoIP network, there can be unwanted traffic coming into a VoIP network that attempts to compromise VoIP network devices. Intelligent hackers can create malicious VoIP traffic for disrupting network activities. Hence, there is a need to frequently monitor the risk levels of critical network infrastructure. Towards realizing this objective, I describe a network level risk management mechanism that prioritizes resources in a VoIP network. The prioritization scheme involves an adaptive re-computation model of risk levels using attack graphs and Bayesian inference techniques. All the above techniques collectively account for a domain-level VoIP security solution.
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Ekelund, Anders. "Detection and haemodilutive treatment of cerebral arterial vasospasm and delayed ischaemia after aneurysmal subarachnoid haemorrhage." Lund : Lund University, 1999. http://catalog.hathitrust.org/api/volumes/oclc/68945106.html.
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Loser, Nichole M. "Malingering Detection Measure Utility and Concordance in a University Accommodation-Seeking Student Population." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3668.
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According to the Americans with Disabilities Act, universities and colleges are required to provide accommodative services for students with disabilities. Many studies have examined the role of malingering mental health symptoms in order to obtain psychotropic medications, but very little research has been done on the role of accommodations as secondary gain in students who may malinger learning disabilities. This study sought to examine both the usefulness of implementing specific malingering detection measures in psychological evaluations with university students and the agreement of those measures within the population. Archival data was gathered from a university accommodation clinic that provided free psychological evaluations for consecutively presenting students (N=121). Four malingering detection measures were used: the Test of Memory and Malingering (TOMM), the Word Memory Test (WMT), the WAIS Digit Span (DS) and two cut scores for the MMPI-2 F Scale (F Scale 80 and F Scale 95). Scores for these four malingering detection measures were compared in terms of their agreement rates, their classification rates (at a 10% malingering base rate recommendation), and their sensitivity, specificity, positive and negative predictive powers using both the TOMM and WMT independently as diagnostic criterion. A qualitative examination of the data revealed that different combinations of measures did classify some of the same respondents as malingering. Results indicated that each of these four measures share the ability to detect malingering in its different forms and have similar classification rates. Although the TOMM and WMT likely provide overlapping information, the pragmatic implementation of one of these measures may assist in the evaluation of suspected malingering with accommodation-seeking students.
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Breitenmoser, Sabina. "Evaluation and implementation of neural brain activity detection methods for fMRI." Thesis, Linköping University, Department of Biomedical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-3069.
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Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique used to study brain functionality to enhance our understanding of the brain. This technique is based on MRI, a painless, noninvasive image acquisition method without harmful radiation. Small local blood oxygenation changes which are reflected as small intensity changes in the MR images are utilized to locate the active brain areas. Radio frequency pulses and a strong static magnetic field are used to measure the correlation between the physical changes in the brain and the mental functioning during the performance of cognitive tasks.
This master thesis presents approaches for the analysis of fMRI data. The constrained Canonical Correlation Analysis (CCA) which is able to exploit the spatio-temporal nature of an active area is presented and tested on real human fMRI data. The actual distribution of active brain voxels is not known in the case of real human data. To evaluate the performance of the diagnostic algorithms applied to real human data, a modified Receiver Operating Characteristics (modified ROC) which deals with this lack of knowledge is presented. The tests on real human data reveal the better detection efficiency with the constrained CCA algorithm.
A second aim of this thesis was to implement the promising technique of constrained CCA into the software environment SPM. To implement the constrained CCA algorithms into the fMRI part of SPM2, a toolbox containing Matlab functions has been programmed for the further use by neurological scientists. The new SPM functionalities to exploit the spatial extent of the active regions with CCA are presented and tested.
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Aminmansour, Sina. "Video analytics for the detection of near-miss incidents at railway level crossings and signal passed at danger events." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112765/1/Sina_Aminmansour_Thesis.pdf.
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Railway collisions remain a significant safety and financial concern for the Australian railway industry. Collecting data about events which could potentially lead to collisions helps to better understand the causal factors of railway collisions. In this thesis, we introduced Artificial Intelligence and Computer Vision algorithms which use cameras installed on trains to automatically detect Near-miss incidents at railway level crossings, and Signal Passed at Danger (SPAD) events. A SPAD is an event when a train passes a red signal without authority due to technical or human errors. Our experimental results demonstrate that it is possible to reliably detect these events.
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Riad, Mourad Y. "Detection and analysis of deck cracks in a long span empirically designed bridge deck through embedded sensory systems." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4582.
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Thesis (Ph. D.)--West Virginia University, 2006.Title from document title page. Document formatted into pages; contains xi, 184 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 145-157).
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Zhou, Xinxin. "An InGaAlAs-InGaAs two-colour detector, InAs photodiode and Si SPAD for radiation thermometry." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7462/.
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This work aims to develop infrared detectors and to introduce a new measurement technique for infrared radiation thermometry. It consists of two-colour detectors for ratio thermometry, InAs photodiode for 3.43 m narrow band thermometer and photon counting thermometer using a Si single photon avalanche photodiode (SPAD). In addition to research in these detectors, a Monte Carlo model for modelling impact ionisation in Si was also developed. InGaAlAs is attractive material for multi-colour detection at wavelengths up to 1.7 m, as it is lattice matched to InP substrate. InGaAlAs-InGaAs two-colour detector was evaluated as a ratio thermometer. When compared to a commercial Si-InGaAs detector, the InGaAlAs diode produces slightly higher (lower) output than Si at temperature below (above) 500 °C, while the InGaAs diode in this work also produces slightly higher output than that in the commercial Si-InGaAs detector. The InGaAlAs and InGaAs diodes detect blackbody temperatures as low as 275 and 125 oC, respectively, with signal to noise ratios (SNRs) above 10. As a ratio thermometer, the two-colour InGaAlAs-InGaAs photodetector achieves a temperature error of 12.8 °C at 275 °C, but this improves with temperature to 0.1 °C at 450 °C. If the maximum temperature error of 2 °C is defined, the InGaAlAs-InGaAs is capable of detecting an object temperature down to 325 °C. These results demonstrate the potential of InGaAlAs-InGaAs two-colour photodetector for development of high performance two-colour array detectors for radiation thermometry and thermal imaging of hot objects. The InAs photodiode offers huge potential for infrared sensing applications at wavelengths above 1.7 m. The performance of InAs photodiode was evaluated for use in radiation thermometry at wavelengths beyond InGaAs photodiode. For uncooled InAs, it successfully measured a blackbody temperature of 50 oC with an acceptable error of 0.17 oC. In order to evaluate its performance as a 3.43 m narrow band thermometer, measurements were repeated with a narrow band filter. InAs was demonstrated to have lower temperature error than a commercial PbSe detector. The temperature error was 1.88 oC for InAs at 50 oC compared to 3.78 oC for PbSe. This suggests that InAs is ideally X. ZHOU III suited for applications requiring 3.43 m operating wavelength. Further improvement was achieved by cooling InAs to 200 K. It was found that a temperature as low as 37 oC, with an error of less than 0.5 oC, can be measured indicating its potential for human body temperature sensing. An alternative to using a photodetector with longer wavelength response is to increase the sensitivity of the photodetector via internal gain mechanisms such as impact ionisation. By employing a very high internal gain in SPAD, the photon counting technique was evaluated for radiation thermometry. Photon induced avalanche pulses were successfully measured at temperature as low as 225 oC with an error less than 2 oC using Si SPAD. This is significantly lower than the lower temperature limit of 400 oC in conventional Si photodiode based radiation thermometer. The photon counting technique is therefore demonstrated to be a feasible technique to achieve lower temperature sensing.
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Tsiampali, Julia [Verfasser], and Ingrid [Gutachter] Span. "Label-free detection of brain tumour cell immunogenicity upon mesenchymal transformation using Raman technology / Julia Tsiampali ; Gutachter: Ingrid Span." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2021. http://d-nb.info/1225932017/34.
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TORTAROLO, GIORGIO. "Laser Scanning Microscopy with SPAD Array Detector: Towards a New Class of Fluorescence Microscopy Techniques." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1000654.
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Laser scanning microscopy is one of the most common architectures in fluorescence microscopy. In a nutshell: the objective lens focuses the laser beam(s) and generates an effective excitation spot which is scanned on the sample; for each pixel, the fluorescent image is projected into a single-element detector, which – typically – spatially and temporally integrates the fluorescent light along its sensitive area and the pixel dwell-time, thus providing a single-intensity value per pixel. Notably, the integration performed by the single-element detector hinders any additional information potentially encoded in the dynamic and image of the fluorescent spot. To address this limitation, we recently upgraded the detection unit of a laser scanning microscope, replacing the single-element detector with a novel SPAD (single photon avalanche diode) array detector. We have shown at first that the additional spatial information allows to overcome the trade-off between resolution and signal-to-noise ratio proper of confocal microscopy: indeed, this architecture represents the natural implementation of image scanning microscopy (ISM). We then exploited the single-photon-timing ability of the SPAD array detector elements to combine ISM with fluorescence lifetime imaging: the results show higher resolution and better lifetime accuracy with respect to the confocal counterpart. Moreover, we explored the combination of our ISM platform with stimulated emission depletion (STED) microscopy, to mitigate the non-negligible chance of photo-damaging a sample. Lastly, we showed how the SPAD array-based microscope can be used in the context of single-molecule/particle tracking (SMT or SPT) and spectroscopy. Indeed, we implemented a real-time, feedback based SMT architecture which can potentially correlate the dynamics of a bio-molecule with its structural changes and micro-environment, taking advantage of the time-resolved spectroscopy ability of the novel detector.
We believe that this novel laser scanning microscopy architecture has everything in its favour to substitute current single-element detector approaches; it will enable for a new class of fluorescence microscopy techniques capable of investigating complex living biological samples with unprecedented spatial and temporal characteristics and augmented information content.
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Ambardekar, Aditya. "Analysis of BER in optical direct detection DPSK system in the presence of SPM and chromatic dispersion." abstract and full text PDF (UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1460745.
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Hallman, L. (Lauri). "Single photon detection based devices and techniques for pulsed time-of-flight applications." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526210445.
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Abstract In this thesis, a new type of laser diode transmitter using enhanced gain-switching suitable for use with a single photon avalanche diode (SPAD) detector was developed and tested in the pulsed time-of-flight laser range finding (lidar) application. Several laser diode versions were tested and the driving electronics were developed. The driving electronics improvements enabled a pulsing frequency of up to 1 MHz, while the maximum laser output power was about 5–40 W depending on the laser diode dimensions. The large output power is advantageous especially in conditions of strong photon noise emerging from ambient light outdoors. The length of the laser pulse matches the jitter of a typical SPAD detector providing several advantages. The new laser pulser structure enables a compact rangefinder for 50 m distance measurement outdoors in sunny conditions with sub-centimeter precision (σ-value) at a valid distance measurement rate of more than 10 kHz, for example. Single photon range finding techniques were also shown to enable a char bed level measurement of a recovery boiler containing highly attenuating and dispersing flue gas. In addition, gated single photon detector techniques were shown to provide a rejection of fluorescent photons in a Raman spectroscope leading to a greatly improved signal-to-noise ratio. Photonic effects were also studied in the case of a pulsed time-of-flight laser rangefinder utilizing a linear photodetector. It was shown that signal photon noise has an effect on the optimum detector configuration, and that pulse detection jitter can be minimized with an appropriate timing discriminatorTiivistelmä Tässä työssä kehitettiin uudentyyppinen, tehostettua "gain-switchingiä" hyödyntävä laserdiodilähetin käytettäväksi yksittäisten fotonien avalanche-ilmaisimien (SPAD) kanssa, ja sitä testattiin pulssin lentoaikaan perustuvassa laseretäisyysmittaussovelluksessa. Useita laserdiodiversioita testattiin ja ohjauselektroniikkaa kehitettiin. Ohjauselektroniikan parannukset mahdollistivat jopa 1 MHz pulssitustaajuuden, kun taas laserin maksimiteho oli noin 5–40 W riippuen laserdiodin dimensioista. Suuri lähtöteho on edullinen varsinkin vahvoissa taustafotoniolosuhteissa ulkona. Laserpulssin pituus vastaa tyypillisen SPAD-ilmaisimen jitteriä tarjoten useita etuja. Uusi laserpulssitinrakenne mahdollistaa esimerkiksi kompaktin etäisyysmittarin 50 m mittausetäisyydelle ulkona aurinkoisessa olosuhteessa mm–cm -mittaustarkkuudella (σ-arvo) yli 10 kHz mittaustahdilla. Yksittäisten fotonien lentoaikamittaustekniikan osoitettiin myös mahdollistavan soodakattilan keon korkeuden mittauksen, jossa on voimakkaasti vaimentavaa ja dispersoivaa savukaasua. Lisäksi portitetun yksittäisten fotonien ilmaisutekniikan osoitettiin hylkäävän fluoresenssin synnyttämiä fotoneita Raman-spektroskoopissa, joka johtaa selvästi parempaan signaali-kohinasuhteeseen. Fotoni-ilmiöitä tutkittiin myös lineaarista valoilmaisinta hyödyntävän pulssin kulkuaikamittaukseen perustuvan lasertutkan tapauksessa. Osoitettiin, että signaalin fotonikohina vaikuttaa optimaaliseen ilmaisinkonfiguraatioon, ja että pulssin ilmaisujitteri voidaan minimoida sopivalla ajoitusdiskriminaattorilla
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Lanza, Davide. "Ottimizzazione energetica di un impianto di aria compressa in uno stabilimento industriale: il caso Technogym spa." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
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Il presente elaborato si propone di studiare e analizzare l’intero sistema di aria compressa utilizzato all’interno dei plant produttivi, al fine di determinare soluzioni di ottimizzazione per la riduzione dei consumi energetici e dei costi generati. Tra i principali interventi saranno presentati: l’implementazione di compressori a controllo variabile di velocità, l’eliminazione delle perdite attraverso un processo di leak management, la riduzione di pressione di esercizio, solo per citarne alcuni. Per fare questo, verrà proposta una struttura metodologica capace di guidare passo a passo il progetto verso il raggiungimento degli obiettivi prefissati. A tal proposito, diviene fondamentale la costruzione di un sistema di indicatori di prestazione equilibrato, contenuto nel numero, ma in grado di fornire una descrizione accurata del caso in esame.
L’elaborato è strutturato su tre sezioni, una breve introduzione, una sezione teorica sugli argomenti principali descritti in precedenza e, infine, per evidenziare il carattere pratico ed operativo della tesi, lo studio e l’implementazione sul caso aziendale Technogym spa.
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Vignetti, Matteo Maria. "Development of a 3D Silicon Coincidence Avalanche Detector (3D-SiCAD) for charged particle tracking." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI017/document.
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L’objectif de cette thèse est de développer un détecteur innovant de particules chargées, dénommé 3D Silicon Coincidence Avalanche Detector (3D-SiCAD), réalisable en technologie silicium CMOS standard avec des techniques d’intégration 3D. Son principe de fonctionnement est basé sur la détection en "coïncidence" entre deux diodes à avalanche en mode "Geiger" alignées verticalement, avec la finalité d’atteindre un niveau de bruit bien inférieur à celui de capteurs à avalanche standards, tout en gardant les avantages liés à l’utilisation de technologies CMOS; notamment la grande variété d’offres technologiques disponibles sur le marché, la possibilité d’intégrer dans un seul circuit un système complexe de détection, la facilité de migrer et mettre à jour le design vers une technologie CMOS plus moderne, et le faible de coût de fabrication. Le détecteur développé dans ce travail se révèle particulièrement adapté au domaine de la physique des particules de haute énergie ainsi qu’à la physique médicale - hadron thérapie, où des performances exigeantes sont demandées en termes de résistance aux rayonnements ionisants, "material budget", vitesse, bruit et résolution spatiale. Dans ce travail, un prototype a été conçu et fabriqué en technologie HV-CMOS 0,35µm, en utilisant un assemblage 3D de type "flip-chip" avec pour finalité de démontrer la faisabilité d’un tel détecteur. La caractérisation du prototype a finalement montré que le dispositif développé permet de détecter des particules chargées avec une excellente efficacité de détection, et que le mode "coïncidence" réduit considérablement le niveau de bruit. Ces résultats très prometteurs mettent en perspective la réalisation d’un système complet de détection CMOS basé sur ce nouveau conceptThe objective of this work is to develop a novel position sensitive charged particle detector referred to as "3D Silicon Coincidence Avalanche Detector" (3D-SiCAD). The working principle of this novel device relies on a "time-coincidence" mode detection between a pair of vertically aligned Geiger-mode avalanche diodes, with the aim of achieving negligible noise levels with respect to detectors based on conventional avalanche diodes, such as Silicon Photo-Multipliers (SiPM), and, at the same time, providing single charged particle detection capability thanks to the high charge multiplication gain, inherent of the Geiger-mode operation. A 3D-SiCAD could be particularly suitable for nuclear physics applications, in the field of High Energy Physics experiments and emerging Medical Physics applications such as hadron-therapy and Proton Computed Tomography whose future developments demand unprecedented figures in terms of material budget, noise, spatial resolution, radiation hardness, power consumption and cost-effectiveness. In this work, a 3D-SiCAD demonstrator has been successfully developed and fabricated in the Austria Micro-Systems High-Voltage 0.35 μm CMOS technology by adopting a “flip-chip” approach for the 3D-assembling. The characterization results allowed demonstrating the feasibility of this novel device and validating the expected performances in terms of excellent particle detection efficiency and noise rejection capability with respect to background counts
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El, Refaei Ehab Ahmed Mohamed [Verfasser]. "Value of 3-D High Resolution Magnetic Resonance Imaging in Detecting the Offending Vessel in Hemifacial Spasm: Comparison with Intraoperative High Definition Endoscopic Visualization / Ehab Ahmed Mohamed El Refaei." Greifswald : Universitätsbibliothek Greifswald, 2013. http://d-nb.info/1043405194/34.
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Zhang, Wu Xian, and 張伍賢. "Instagram Spam Detection." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/2ppj9n.
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POONIA, SANDEEP. "EMAIL SPAM DETECTION." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16193.
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Internet has opened new channels of communication; enabling an e-mail to be sent to a relative thousands of kilometers away. This medium of communication opens doors for virtually free mass e-mailing, reaching out to hundred of thousands users within seconds. However, this freedom of communication can be misused. In the last couple of years spam has become a phenomenon that threatens the viability of communication via e-mail.
It is difficult to develop an accurate and useful definition of spam, although every e-mail user will quickly recognize spam messages. Merriam-Webster Online Dictionary1 defines spam as “unsolicited usually commercial e-mail sent to a large number of addresses”. Some other than commercial purposes of spam are to express political or religious opinions, deceive the target audience with promises of fortune, spread meaningless chain letters and infect the receivers’ computer with viruses. Even though one can argue that what is spam for one person can be an interesting mail message for another, most people agree that spam is a public frustration.
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鍾在豐. "Chinese Spam Detection and Implementation." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/93312610924109849787.
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Hu, Chen-Chieh, and 胡箴潔. "Detection of redirection spam pages." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/62114119948413140772.
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碩士元智大學
資訊管理學系
94
As the Internet scope and business profit grow, many web site administrators make well-designed ways in their homepages. In that way rose SEO and web spam pages. Meanwhile the search engines put the illegal web pages in black lists. In web spam pages the main model is cloaking, but now redirection is increasingly becoming the one of main stream. Our research is going to detect redirection spam pages. First we collected web pages using popular queries and then filtered them by modified browser programs to leave redirection ones alone. Second we manually checked the redirection spam pages and analyzed how the redirection time affected the spam pages. Third we counted the ratio of cloaking used in redirection web pages and the term sizes and link quantities as well. Finally we counted the thresholds of terms and links as judgments by statistics. We hope to rise efficiency and effect to detect redirection spam pages by the ways we mentioned. And the search engines could take more redirection spam pages away to form the black lists.
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Nayak, D., and M. R. Sahoo. "Spam detection in collaborative tagging." Thesis, 2014. http://ethesis.nitrkl.ac.in/5959/1/e-126.pdf.
Full textAbstract:
The algorithm proposed will be able to identify the spammers and demote their ranks cocooning the users from their malicious intents and gives popular and relevant resources in a collaborative tagging system or in online dating sites, or any other online forum where there are discussions like quora, amazon feedbacks etc. by a suitable algorithm on lines of an existing one but with multifaceating dimensions as against them. We have taken the assumption that there are two factors on which the virtuosity of a user with reference to a resource or a document depends on. First and foremost an expert should have a rich content resource in his repertoire and his dexterity to find good resources, however the paraphernalia for rich resource is virtuosity of users who tagged it. Secondly, an expert should be first to identify intriguing or riveting documents.
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Brown, Kevin Alan. "A comparison of statistical spam detection techniques." 2006. http://digital.library.okstate.edu/etd/umi-okstate-1741.pdf.
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Hsu, Te-en, and 徐得恩. "A Study of Collaborative Spam Mail Detection." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/84250260377456329835.
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碩士國立雲林科技大學
資訊管理系碩士班
92
Email has become one of the most widely used methods of communication, due to its speed, low cost and ease of use. However, with this medium also comes the problem of Unsolicited Bulk Email, or called “spam mail”; the medium is just as useful legitimate user as it is for corporations wanting to advertise. This has is turn led to the requirement that these unwanted emails should be automatically filtered to leave just the relevant ones for the user. This study proposed a method based on multiagent system to collaboratively filter spam from users’ mail stream. The method composed of three parts: a na��ve bayesian classifier for single detection; a voting scheme for computing spam probability from each agent in the committee to final spam probability and a modified reinforcement learning algorithm used for updating reward table. Then, the method was tested by simulation. The result showed that detection rate in collaborative spam detection is higher than single detection and false positive rate is less than single detection.
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Chen, Tsung-Hsin, and 陳宗欣. "Implement A SIP-based Spam Detection System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/74385283329957932175.
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碩士中華大學
資訊工程學系(所)
96
As Voice over IP (VoIP) is gaining popularity, VoIP has become an important communication tool in the present time. Thus, the security problem of Spam over Internet Telephony (SPIT) will become an important issue. SPIT cannot be treated as the same way of anti-spam problem, since the SPIT is a real-time service; it is more difficult to guard the SPIT than anti-spam. Besides, the lower cost of VoIP communication combined with traditional phone fraudulence will bring more serious social problems. In order to solve the SPIT problem, we proposed an effective SIP-based spam detection system (Social Network based SPIT Detection System；SNSDS). In proposed architecture, we adopt the idea of the relationship of trust between peoples, and integrated the six degrees of separation with the concept of the social network. Also, we apply breadth-first algorithm to search and calculate the confidence degree of caller, and compared with a predefined threshold to decide whether the caller is a legitimate VoIP Callers or no. In the experiment, we can find that the proposed architecture has better performance compared with the method presented by Ram.
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Singh, Smriti. "Improved Techniques for Online Review Spam Detection." Thesis, 2015. http://ethesis.nitrkl.ac.in/7972/1/656.pdf.
Full textAbstract:
The rapid upsurge in the number of e-commerce websites, has made the internet, an extensive source of product reviews. Since there is no scrutiny regarding the quality of the review written, anyone can basically write anything which conclusively leads to Review Spams. There has been an advance in the number of Deceptive Review Spams - fictitious reviews that have been deliberately fabricated to seem genuine. In this work, we have delved into both supervised as well as unsupervised methodologies to identify Review Spams. Improved techniques have been proposed to assemble the most effective feature set for model building. Sentiment Analysis and its results have also been integrated into the spam review detection. Some well known classifiers have been used on the tagged dataset in order to get the best performance. We have also used clustering approach on an unlabelled Amazon reviews dataset. From our results, we compute the most decisive and crucial attributes which lead us to the detection of spam and spammers. We also suggest various practices that could be incorporated by websites in order to detect Review Spams.
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Narayan, Rohit. "Review Spam Detection Using Machine Learning Techniques." Thesis, 2016. http://ethesis.nitrkl.ac.in/8587/1/2016_MT_214CS2155_Rohit_Narayan.pdf.
Full textAbstract:
Nowadays with the increasing popularity of internet, online marketing is going to become more and more popular. This is because, a lot of products and services are easily available online. Hence, reviews about these all products and services are very important for customers as well as organizations. Unfortunately, driven by the will for profit or promotion, fraudsters used to produce fake reviews. These fake reviews written by fraudsters prevent customers and organizations reaching actual conclusions about the products. Hence, fake reviews or review spam must be detected and eliminated so as to prevent deceptive potential customers. In our work, supervised and semi-supervised learning technique have been applied to detect review spam. The most apt data sets in the research area of review spam detection has been used in proposed work. For supervised learning, we try to obtain some feature sets from different automated approaches such as LIWC, POS Tagging, N-gram etc., that can best distinguish the spam and non-spam reviews. Along with these features sentiment analysis, data mining and opinion mining technique have also been applied. For semi-supervised learning, PU-learning algorithm is being used along with six different classifiers (Decision Tree, Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Random Forest, Logistic Regression) to detect review spam from the available data set. Finally, a comparison of proposed technique with some existing review spam detection techniques has been done.
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Hembram, Punya Prava. "Singleton Review Spam Detection Using Semantic Similarity." Thesis, 2016. http://ethesis.nitrkl.ac.in/9106/1/2016_MT_PPHembram.pdf.
Full textAbstract:
Reviews available online plays a vital role in today’s online shopping world. Consumers continuously review the products online. As a result the websites containing the users reviews are being targeted by opinion or review spammers. As the spam reviews are widely spread all over the e-commerce website, the consumers can be easily misguided to buy cheap-quality products. On the other hand the decent stores can be denigrated by false harmful or negative reviews. It has been observed that, in real-life, a very huge portion about more than 90% of the consumers tends to write only a single review. Such kind of reviews that are written by a person who has written only a single review is known as singleton review. These singleton reviews are so immense that they have the power to determine the rating and impression of a store.
So far the existing methods have generally ignored these singleton reviewers. To address such problem, we observe that the same users writes many fake reviews under different profile names and aim to use semantic similarity metrics to compute the relatedness between these one-timer review writers. This method depicts a new point of view towards opinion spam detection. This method is meant to record more subtle information than a simple text similarity measure can capture. It is focused on spam reviewers who use different anonymous profiles to post reviews by again using their previous review that they already wrote, replacing the main feature words with its synonyms, thereby keeping the overall sentiment of the review the same.
The method used here is based on an obvious but important assumption – that the imagination of any human being is limited which also includes the spammers, eventually they will run out of ideas and thus will be unable to write an imaginary experience differently in their reviews every single time. That leads the spammers to very likely reuse the contents of his previous reviews.
In this thesis we discuss a complete different solution to detect review spam written by the one-time review writers by using semantic similarity. It also proposes a modified version of the above mention semantic similarity and tests this hypothesis on the real-life reviews and thereby comparing the proposed method with the existing vectorial similarity models.
Experimental results shows that semantic similarity can outperform the vectorial model in detecting the fraudulent reviews by capturing even more fine textual clues. The precision score of the review classifier showed high results, high enough to make the method viable to be integrated into a production detection system.