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Automatické rozpoznávání adres elektronické pošty na webových stránkách. / Automatic recognition of e-mail addresses on the websitesMoravcová, Iva January 2011 (has links)
This work is focusing on reasons for protection of email addresses against automatic recognition, which aims to find other recipients of spam. Includes description of how this protection can be increased and these methods are evaluated in terms of level of protection and conservation addresses accessibility of websites. The part of this work is to determine the current status of the website addresses of public universities in terms of utilized methods of record. Status on these sites is investigated using application, which in addition to finding addresses, finding the method of writing on a selected sample sites. The various methods used to write addresses are evaluated on the results of the application and other recommendations for improvement are discussed.
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Measuring the Impact of email Headers on the Predictive Accuracy of Machine Learning TechniquesTout, Hicham Refaat 01 January 2013 (has links)
The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning algorithms to detect phishing attack did increase the predictive accuracy of these learning algorithms. The same research also recommended further investigation of the impact of including an expanded set of email headers on the predictive accuracy of machine learning algorithms.
In addition, research has shown that the cost of misclassifying legitimate emails as phishing attacks--false positives--was far higher than that of misclassifying phishing emails as legitimate--false negatives, while the opposite was true in the case of fraud detection. Consequently, they recommended that cost sensitive measures be taken in order to further improve the weighted predictive accuracy of machine learning algorithms.
Motivated by the potentially high impact of the inclusion of email headers on the predictive accuracy of machines learning algorithms and the significance of enabling cost-sensitive measures as part of the learning process, the goal of this research was to quantify the impact of including an extended set of email headers and to investigate the impact of imposing penalty as part of the learning process on the number of false positives. It was believed that if email headers were included and cost-sensitive measures were taken as part of the learning process, than the overall weighted, predictive accuracy of the machine learning algorithm would be improved. The results showed that adding email headers as features did improve the overall predictive accuracy of machine learning algorithms and that cost-sensitive measure taken as part of the learning process did result in lower false positives.
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Realizing Homomorphic Secure Protocols through Cross-Layer Design Techniques / クロスレイヤ設計による準同型暗号プロトコルの実現Bian, Song 23 May 2019 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21975号 / 情博第703号 / 新制||情||121(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 佐藤 高史, 教授 小野寺 秀俊, 教授 岡部 寿男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
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Mail-Filter-FunktionenLeuschner, Jens 27 February 2002 (has links)
Im Rahmen dieser Studienarbeit wird untersucht, welche Lösungen es momentan zur Filterung von Email mit unerwünschten Schadensfunktionen auf Mailservern gibt. Dabei werden sowohl offene als auch proprietäre Lösungen betrachtet und die momentanen Randbedingungen der TU Chemnitz beachtet.
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Email attacks : Investigation about the vulnerability of the Swedish organizations against email threats.Kour, Jawdat, Ahmed, Hasan January 2020 (has links)
Email is an essential form of communication for organizations. Nevertheless, with so much popularity came many challenges. These emails usually carry sensitive data that might cause significant harm if they get compromised. Besides, spam and phishing emails that continually reach the employees’ inbox masquerading as a trusted entity due to the lack of authentication mechanisms are also considered a significant threat for organizations today. Such threats are phishing using email domain forgery attack, redirecting emails to a mail server that is under the attacker’s control, and connection eavesdropping. The research aimed to investigate the vulnerability of approximately 2000 organizations within Sweden against those attacks. Toward that end, the quantity and quality of the following email security mechanisms SPF, DKIM, DMARC, STARTTLS, DNSSEC, and DANE were examined through a case study. Also, the adoption of these mechanisms was investigated, whether it varies based on different factors such as organization size, sector, and location. The research findings indicated that the average adoption rate by the tested organizations was approximately 50%. Furthermore, the result demonstrated that there were no differences in the adopted mechanisms based on the studied factors that the results were quite similar among the tested groups. It concluded that there is a lack of protection mechanisms, which made the majority of the tested organizations vulnerable to different types of email attacks.
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Email Classification with Machine Learning and Word Embeddings for Improved Customer SupportRosander, Oliver, Ahlstrand, Jim January 2018 (has links)
Classifying emails into distinct labels can have a great impact on customer support. By using machine learning to label emails the system can set up queues containing emails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve the manually defined rule based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct five experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings and how they work together. In this article a web based interface were implemented which can classify emails into 33 different labels with 0.91 F1-score using a Long Short Term Memory network. The authors conclude that Long Short Term Memory networks outperform other non-sequential models such as Support Vector Machines and ADABoost when predicting labels for emails.
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Using AI for Evaluating and Classifying E-mails with Limited Data SetsMalm, Daniel January 2022 (has links)
Denna rapport utvärderar olika metoder för att klassificera och kategorisera email. Mångamail anländer hos människors inkorg varje dag. När tiden går och antalet email ökar blir detsvårare att hitta specifika email. På HDAB arbetar de som konsulter och vill dela upp email iolika mappar beroende på vilket projekt det tillhör. Idag fungerar det genom ett ord-regelbaseratsystem som sorterar email I olika mappar med en precision på cirka 85%. HDAB villta reda på om det går att använda maskininlärning för det nuvarande systemet. Denna rapportpresenterar fyra maskininlärningsalgorimer, beslutsträd, random forest beslutsträd, k-nearestneighbor och naive bayes, som användas för att utvärdera om det är möjligt att kategoriseraemailen.Datan som används till rapporten kommer från HDABs mailserver och är redan kategoriseradtill rätt kaegori. / This report will evaluate methods for classifying e-mails into different categories. A lot ofemails are received in peoples inboxes every day. When the time passes and the amount ofemails increases the ability to find specific emails gets harder. At HDAB they are workingwith consulting and want to separate different emails from different project into separate folders.This is achieved today by using a word based rule system that sorts emails into differentfolders and has a precision about 85%. HDAB wants to know if it is possible to use machinelearning to automatically sort the emails into different folders instead of the current solution.This report presents four machine learning algorithms, decision tree, random forest decisiontree, k-nearest neighbor and naive bayes, which are being used for evaluation of the possibilityto categorize the emails.The data used for the report will be data gathered from HDAB’s mail server and are alreadypre-labeled into their respectively categories.
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Některá specifika komunikace ve stavebnictví / Some specifics of communication in construction engineeringHrtáň, Zbyněk January 2012 (has links)
The aim of the thesis called Some specifics of communication in civil engineering has been to carry out research in construction companies and to find out to what extent they pay attention to corporate image in various respects – if (and eventually how) they make use of the Internet and how they present themselves to the public. On the other hand, part of the research focused on what emphasis do these companies put on internal communication with their employees. To prove or displace the initial hypotheses, several analyses have been made and research in 14 companies of different size carried out. The results have shown that construction companies pay attention to internal communication, however, they do not place much emphasis on presenting themselves to public. This way they underestimate the importance of positive corporate image, particularly in the media of the Internet.
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eCRM PERSONALIZATION STRATEGIES : Influence of content personalization on consumer engagement performance of email marketing campaignsRodriguez, Daniela January 2023 (has links)
Background: As personalization has become a common CRM strategy for companies to create valuable relationships with customers, users are receiving an increased amount of personalized communication, further research is needed on the influence of content personalization in specific channels, to improve customer engagement. Purpose: This paper seeks to analyse the influence of eCRM content personalization strategies on the consumer engagement performance of email marketing campaigns building upon existing knowledge about the benefits and opportunities of personalization strategies. Method: The selected method is quantitative, using A/B testing, based on the comparison of different variations on two identical segments where the only differentiated element is the one being evaluated. Two controlled experiments evaluating subject line and image personalization are performed evaluating four metric of email marketing performance: open rate, click-through rate, conversion rate and unsubscribe rate. Conclusion: The results of the two controlled experiments performed for this research, subject line personalization and image personalization, complement past literature on content personalization strategies (Bertrand et al., 2010; Carlota Rocha et al., 2023; Munz et al., 2020; Sahni et al., 2018) by demonstrating the statistically significant positive influence of content personalization on email marketing performance and reinforcing the importance of familiarity with the brand or product on reducing the probability of bad outcomes.
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You´ve Got Mail! : A quantitative study on Permission-based email marketingsimpact on brand attitudesJonsson, David, Tufvesson, Måns January 2023 (has links)
The study introduces the concept of attitudes and its fundamentals, as well as how it revolvesaround the given attitude object, in this instance brands. Furthermore, it introduces thephenomena of email marketing and how it has evolved its consensual form that is mostcommon today, referred to as PEM (Permission-based email marketing) in this study.Conclusively explaining the important influences between these two as well as the researchgap in this given context. The final purpose of the paper is to explain the impact thatpermission-based email marketing has on brand attitude. The methodology of the study itselfutilized a cross-sectional approach with a self-completion questionnaire via a conveniencesampling through a web-based survey. The final sample consisted of 108 respondents thatwere deemed acceptable. The study concluded that entertaining content and informativecontent seemingly affects brand attitude positively, whereas the impact of frequency couldnot definitively be explained. Finally theoretical and practical implications of the results arediscussed, as well as limitations of the study and recommended future research.
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