Email Content Research Articles

Secured Mailing System for Eliminating Spam and Flood Attacks

Abstract: Threatening to spam considers cement strategiesfor seeing unrequested email from the email content and frameworks for utilizing carrier data. In the event that it will overall be troublesome from the source's IP address of carrier information and the carrier's area name whether theemail ought to be gotten, it is feasible to diminish the treatment of the spam channel by the email content that hasa high separating load for the goal. This study utilizes source confirmation improvement to perceive the carrier of sent email. We consider that the wellspring of this sent email is the embraced email carrier to get, and thinking to utilize these as a permit list. In this paper, we set forth a strategy to furthermore refresh the system we set forth and reduction mix-up of the permit list. We showed that this newmethodology solid by utilizing the log information of the messages really got

Phishing email detection using deep learning algorithms

E-mail is one of the most widely recognised channels of communication, whether for personal or corporate purposes. The worst part about spam emails is that they intrude on user’s privacy without their consent, and the constant bombardment of spam mails fills up the user's entire email space. Additionally, the issue of wasting network capacity and time checking and deleting spam mails makes it an even more concerning issue. Although confrontation tactics are constantly being upgraded, the results of those methods are now unsatisfactory. Furthermore, phishing emails have been on the rise in recent years. To combat the issue of phishing emails, more effective phishing detection technology is required. We intend to create a phishing email detection tool that analyses the email structure first, using an upgraded Convolutional Neural Networks model with multilayer vectors and Long Short-Term Neural Network (LSTM) to model emails at the email header, character level, email content, and word level all at the same time. To assess the efficacy, we'll use an unbalanced dataset with actual phishing and genuine email ratios. The total accuracy of the experiment achieves a high percentage.

Securing Enterprise Emails in Cloud Platform

Cloud-based email is one of the services offered by cloud computing, and the number of users continues to grow year after year. Because of its working environment, cloud computing raises concerns about security and privacy. User authentication in cloud computing is now predicated on the user's credentials, which are typically username and password. User authentication in cloud computing is currently predicated on the credentials possessed by the user, which are primarily username and password. With the growing usage of cloud emails and numerous allegations of large-scale email leakage occurrences, a security attribute known as forward secrecy has become desirable and necessary for both users and cloud email service providers to strengthen the security of their communications. However, due to the failure of email systems to meet both security and practicality requirements at the same time. A fine-grained revocation capacity is available to an email user. A security key will be provided by the user to prevent hacking of such email addresses. The MAES(Modified Advanced Encryption Standard) algorithm encrypts files and a user's email ID to safeguard their data from a third party or hackers to address this issue more efficiently. This proposed hybrid security method secures the content of emails before they are sent through email using an Advanced Cipher Technique (ACT). The study suggests employing substitution and permutation to secure email content, with the fronts offered by email systems acting as keys.

Managerial Accounting for Decision-Making and Planning

Managerial Accounting for Decision-Making and Planning

The use of Big Data Analytics in healthcare

The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Classify the Sentiments of email Contents using Novel Bidirectional Encoder Representation for Transformation over Naïve Bayes Algorithm

Classify the Sentiments of email Contents using Novel Bidirectional Encoder Representation for Transformation over Naïve Bayes Algorithm

Finding light in dark archives: using AI to connect context and content in email

Email archives are important historical resources, but access to such data poses a unique archival challenge and many born-digital collections remain dark, while questions of how they should be effectively made available remain. This paper contributes to the growing interest in preserving access to email by addressing the needs of users, in readiness for when such collections become more widely available. We argue that for the content of email to be meaningfully accessed, the context of email must form part of this access. In exploring this idea, we focus on discovery within large, multi-custodian archives of organisational email, where emails’ network features are particularly apparent. We introduce our prototype search tool, which uses AI-based methods to support user-driven exploration of email. Specifically, we integrate two distinct AI models that generate systematically different types of results, one based upon simple, phrase-matching and the other upon more complex, BERT embeddings. Together, these provide a new pathway to contextual discovery that accounts for the diversity of future archival users, their interests and level of experience.

Machine Learning-Based Detection of Spam Emails

Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu’s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.

Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review.

Mechanisms for sharing information in a disaster situation have drastically changed due to new technological innovations throughout the world. The use of social media applications and collaborative technologies for information sharing have become increasingly popular. With these advancements, the amount of data collected increases daily in different modalities, such as text, audio, video, and images. However, to date, practical Disaster Response (DR) activities are mostly depended on textual information, such as situation reports and email content, and the benefit of other media is often not realised. Deep Learning (DL) algorithms have recently demonstrated promising results in extracting knowledge from multiple modalities of data, but the use of DL approaches for DR tasks has thus far mostly been pursued in an academic context. This paper conducts a systematic review of 83 articles to identify the successes, current and future challenges, and opportunities in using DL for DR tasks. Our analysis is centred around the components of learning, a set of aspects that govern the application of Machine learning (ML) for a given problem domain. A flowchart and guidance for future research are developed as an outcome of the analysis to ensure the benefits of DL for DR activities are utilized.

Analysis of Modern Anti-Virus Software in Cyber Security Tasks

Purpose. The research is aimed at gaining general knowledge about modern anti-virus software. Because it is one aspect of cybersecurity, and is subject to ongoing discussions about its appropriateness. Methodology. To obtain data, the authors conducted a review of world literature on the topic of work using full-text and abstract databases. Signature and heuristic methods of antivirus software operation are considered. As well as the conditional division of these products into programs-detectors, programs-doctors, programs-auditors, programs-filters, programs-immunizers was made. Findings. Content analysis of publications in the direction of anti-virus software proves the number of aspects studied. The question of the ability of such software not only to recognize the threat, but also to be able to destroy it is being studied. To perform this task, the antivirus must have the following functions: regular (or real-time) scanning of system files and programs; scanning the content of messengers and e-mail; full computer scan at the user's command; monitoring of incoming and outgoing network traffic; elimination of the malware operation consequences. In addition, a comparative analysis of the most popular anti-virus software makes it possible to identify the disadvantages and advantages of each of them. Originality. Systematic and generalized analysis of the existing and most common anti-virus software has been further developed, which allows ordinary users to make informed choices about installing such software packages. Practical value. Based on the results obtained, it is possible to adjust your own actions regarding safe work on the Internet. In addition, the article aims to dispel myths suggesting that users do not protect their computers from malware at all. These studies can also be useful in studying the discipline "Fundamentals of Software Testing", the organization of scientific and practical seminars, refresher courses and etc.

Security Enhancement in Data Propagation for Wireless Network

Wireless communication technology is rapidly progressing due to its high quality and high speed of information transfer from one location to another. It is necessary to ensure the safety of wireless sensor networks with this progress. One of the main security concerns in WSNs is eavesdropping, an intrusion that collects information from other devices across the network. Eavesdropping attacks are insidious and it is hard to realise that they happen. When connected with a network, users can feed sensitive information inadvertently, such as passwords, account numbers, browsing, email content, etc. Security improvement in wireless network communication is therefore required.

Security Enhancement in Data Propagation for Wireless Network

Wireless communication technology is rapidly progressing due to its high quality and high speed of information transfer from one location to another. It is necessary to ensure the safety of wireless sensor networks with this progress. One of the main security concerns in WSNs is eavesdropping, an intrusion that collects information from other devices across the network. Eavesdropping attacks are insidious and it is hard to realise that they happen. When connected with a network, users can feed sensitive information inadvertently, such as passwords, account numbers, browsing, email content, etc. Security improvement in wireless network communication is therefore required.

Why students don’t read your e-mails: A critical look at how university email communications guide or confuse students through the student experience and the COVID-19 pandemic

This paper examines the perception that ‘students don’t read e-mail’ through the execution and analysis of a comprehensive e-mail audit conducted by New York University’s Enrollment Management Marketing division from May 2019 through May 2020. By looking at the volume, content and quality of e-mail received from the university, we see how institutional messaging can either support or complicate the student experience. This research includes a human-processed dataset of all incoming e-mail communications received during the audit period, quantitative and qualitative analyses of the communications and focus group testimony from undergraduate students. We examine e-mail communications through both the everyday business of being a student and through high-stakes events like the onset of the COVID-19 pandemic. This paper illustrates that while volume should not be dismissed as an area of concern among university communicators, the underlying practices that cause e-mail to be ignored are more complex and demand a more strategic approach to studentfacing communications.

What can be found in the spam folder? a self-study from junior researchers in psychiatry

AimsThriving on the pressure of “publish or perish” experienced by academicians, the industry of predatory publishers with dubious quality has mushroomed and gained their notoriety. The battle of uncovering predatory publishers, including Beall's list, has proven to be tough given the huge monetary gain generated by the predatory publishers. It may be difficult for an inexperienced junior researcher to identify those predatory publishers’ soliciting emails, which may disguise as a reputable journal's article-commissioning process. To date, there is a limited systematic approach to identify such emails. Hence, this research is aimed to describe the common features of soliciting emails from publishers which appeared to be predatory.MethodThis self-study involved reviewing the content of emails in the spam folder of authors, a team of junior researchers in psychiatry, for a month. Emails included in this study were soliciting emails relevant to publications and the following were reviewed: types of solicitation, sentences used, strategies used, and information available in the public domain of their webpages. Informative types of emails were excluded.ResultThe solicitation could include: 1) request for a manuscript to be published a journal article, 2) request for a thesis to be published as a book, 3) request to write for a book chapter, 4) invitation to be an editorial member or a reviewer with the offer of free publishing, 5) invitation to be a speaker for a conference, and 6) proofreading services. The publisher may cite a published article of the author from another journal, which was the source where they identified the author's email. Common strategies used for solicitation included: 1) promising a fast-tracked and guaranteed publication, 2) using compliments that appeared to be inappropriate, 3) repetitive emails, and 4) using argumentum ad passiones to induce guilt. The common features of the webpages of those publishers included: 1) open access publishing as the only option, 2) extensive list of indexing services excluding well-established indexing agencies, and 3) the publisher has a huge collection of journals in different disciplines.ConclusionIt is hoped that these findings will help junior researchers in psychiatry to stay vigilant to avoid falling into the trap of predatory publishers, which may result in financial loss and loss of work to plagiarism. Total eradication of those predatory soliciting emails is unlikely despite the advancement of spam filtering technology, which necessitates a more united effort from different stakeholders to come out with a probable solution.

Asian/American Women Scholars, Gendered Orientalism, and Racialized Violence: Before, During, and After the 2021 Atlanta Massacre

This article describes how an Asian/American woman leader-scholar (and others un/like her) have processed the Atlanta Massacre of 2021 and other types of racialized violence in and out of the academy by drawing on the analytic frameworks of Orientalism and racialized sexualization. This critical autoethnography involved synthesizing traumatic reflections into concept maps by drawing from the content of author-generated poems, e-mails, institutional statements, and journal entries based on a series of critical incidents that occurred between March 15, 2021 to March 22, 2021, as well as over the past several decades. She describes how many leaders at White-dominated institutions of higher education have perpetually dishonored Asian/Americans and other BIPOC faculty, staff, and students through their in/actions, mis/behaviors, and mis/deeds before, during, and after the Atlanta Massacre March 16, 2021.

‘Potency is important for a real man’: Affective Readings of Shame and Performance Anxiety in Spam Selling Viagra and other Sexuopharmaceuticals

Viagra is a popular topic in spam, but contrary to Pfizer's official marketing of Viagra, little attention has been directed at how the content of spam plays into gendered discourse on masculinity and men's sexual performances. This is not surprising, if we consider how spam is mostly treated as a nuisance to digital infrastructures. Yet, studies have demonstrated how spammers build on, reflect and transmit gender ideologies (e.g. Mullany 2004 ; Paasonen 2009 ; Yu 2014 ). In the present paper, we contribute insights to the existing studies on Viagra and spam, as we examine how spammers promote Viagra and other sexuopharmaceuticals. Taking our point of departure in an online spam archive ( Guenter 2010 ), we combine qualitative and quantitative methods to analyse the textual content of spam emails. Using TagSpheres models, we have produced an overview of the most frequent and relevant terms in spam which we further subject to a qualitative analysis. Drawing on Sara Ahmed (2004) , we engage in an affective reading of selected spam emails, generated by randomised samples. In the analysis, we argue that spammers attempt to move possible consumers away from official and legal sale methods by emphasising shame and anxiety related to procuring Viagra and other sexuopharmaceuticals. While shame moves the consumer away from legal sale methods, anxiety is invoked to encourage the possible consumer to envision a hypermasculine future, in which virility is everlasting. We further discuss our findings in relation to masculinity, arguing that spammers capitalise on existing communication- and pharmaceutical technologies, whilst taking gendered discourse in advertising to their ‘functional extremes’ ( Brunton 2019 : xiv).

CODE-SWITCHING IN WRITTEN DISCOURSES: AN EXPLORATORY STUDY OF SINHALA-ENGLISH HYBRID EMAILS

Code-switching – extrasentential shifts transferring the focus from one language to another – is undoubtedly a characteristic of cross-cultural encounter: it is generally assumed in the literature that bilinguals mix their two languages to considerable degrees. Code-switching in its oral production has largely been researched, devoting attention to its grammatical patterns, structure and meaning, and the like. Nevertheless, very little research focuses on code-switching in writing, particularly on Sinhala-English shifts in written e-communication methods. The overall intent of this study was to examine the use of code-switching in emails employed by Sinhala and English bilinguals. The fourfold question aimed (i) to explore the frequency of code-switching in emails among bilinguals of English and Sinhala languages, (ii) to investigate the possible relation between code-switching and email recipients, (iii) to determine the possible correlation between code-switching and the subject matter in emails, and (iv) to explore the reasons and functions for code-switching in such emails. A questionnaire, a semi-structured interview, and a collection of emails were employed as research instruments in this exploratory study. Over 100 Sinhala-English bilinguals contributed to the questionnaire: while 20 of their emails were analyzed to triangulate the data, and 5 participants were interviewed to ascertain further the data collected. Data analysis was performed both on a statistical test called a Chi-squire test and an evaluation of the content of emails. Evidently, code-switching is highly frequent, and preferred to a single language, in informal emails. It entails diverse socio-cultural and linguistics functions in line with the social solidarity between the two users. English is used to a higher degree in code-switched emails than Sinhala despite the participants’ socio-cultural, educational, professional, and gender diversity. Code-switching in emails frequently constitutes a qualitatively better form of knowledge construction. Nonetheless, future studies are expected to gather a greater number of emails to enhance generalizability.

58993 The Efficacy of Converting an In-person Commercialization Education Course to a Virtual and Flipped Experience

ABSTRACT IMPACT: The successful conversion of an in-person biomedical research commercialization education course to a fully virtual and flipped experience (self-paced) allows greater participation from faculty investigators at CTSA institutions and serves as a model for similar educational programs intended to accelerate the translation of biomedical innovations to products of impact. OBJECTIVES/GOALS: Due to COVID-19, University of Michigan’s Fast Forward Medical Innovation developed new educational resources and leveraged virtual learning tools to convert a successful in-person research commercialization course to a fully virtual, flipped format and evaluated the effectiveness of the converted course compared to the in-person equivalent. METHODS/STUDY POPULATION: Two novel interactive modules (intellectual property and FDA regulation) and five instructional videos (customer discovery, value proposition, opportunity sizing, target product profile, and patent searches) were developed while Constant Contact and Zoom were used for a weekly progression of content delivery and to flip the course: (1) forming/testing value propositions, (2) intellectual property, (3) regulatory, (4) medical reimbursement, (5) business case development. A total of 32 faculty and graduate students completed the virtual, flipped course and submitted a post-course evaluation. Results of the converted course were compared to evaluation results from the in-person course. RESULTS/ANTICIPATED RESULTS: Open rates for the weekly email content were: (1)61%, (2)67%, (3)65%, (4)67%, and (5)59 %. Total views for the modules and videos were: IP-28, regulation-19, customer discovery-62, value proposition-21, opportunity sizing-66, target product profile-11, and patent searches-29. Evaluation results from the virtual course (n=22) were compared to mean results from the 5 previous in-person courses (n=42); 86% of virtual course respondents stated the course met the objectives compared to 85% of in-person respondents; 87% of virtual respondents stated the course met their expectations compared to 100% of in-person; 87% of virtual respondents said they would participate in a follow-up program compared to 94% in-person; 91% of virtual respondents would recommend the course to others compared to 97% of in-person. DISCUSSION/SIGNIFICANCE OF FINDINGS: Email open rates and content views suggest positive flipped participation. Overall, the converted course was comparable to the in-person course at meeting objectives, suggesting the virtual format is effective at delivering the course content and holds the potential for engaging a broader audience.

Goal-oriented Email Stream Classifier with A Multi-agent System Approach

Now-a-days, email is often one of the most widely used means of communication despite the rise of other communication methods such as instant messaging or communication via social networks. The need to automate the email stream management increases for reasons such as multi-folder categorization, and spam email classification. There are solutions based on email content, capable of contemplating elements such as the text subjective nature, adverse effects of concept drift, among others. This paper presents an email stream classifier with a goal-oriented approach to client and server environment. The i* language was the basis for designing the proposed email stream classifier. The email environment was represented with the early requirements model and the proposed classifier with the late requirements model. The classifier was implemented following a multi-agent system approach supported by JADE agent platform and Implementation_JADE pattern. The behavior of agents was taking from an existing classifier. The multi-agent classifier was evaluated using functional, efficacy and performance tests, which compared the existing classifier with the multi-agent approach. The results obtained were satisfactory in all the tests. The performance of multi-agent approach was better than the existing classifier due to the use of multi-threads.

An Unsupervised Approach for Content-Based Clustering of Emails Into Spam and Ham Through Multiangular Feature Formulation

The rapid growth of spam email attacks and the inherent malicious dynamism within those attacks on a range of social, personal and business activities warrants an intelligent and automated anti-spam framework. Attempts like malware propagation, identity theft, sensitive data pilfering, monetary as well as reputational damage are sharply increasing, endangering the privacy of the victim. Current solutions that are rather incomplete when the multidimensional feature range of email, is taken into account. We believe a methodology based on Artificial Intelligence, especially unsupervised machine learning is the way forward. This research attempts to investigating the application of unsupervised learning for the clustering of Spam and Ham emails. The overall goal of the research is to develop an unsupervised framework that solely depends on unsupervised methodologies through a clustering approach that includes multiple algorithms, primarily using the email content (body) and the subject header. The clustering has been done on a novel binary dataset of 22,000 entries of ham and spam emails, composed of ten features (reduced from eleven to ten after the feature reduction). Seven out of these ten features are unique to this study, engineered to represent impactful analytical email characteristics from a multiangular point of view. Out of five different clustering algorithms investigated in this work, OPTICS produced the optimum clustering demonstrating a 0.26% higher average efficacy than its nearest performer DBSCAN. The average balanced accuracy for OPTICS and DBSCAN was found to be ≈75.76%.