Email Users Research Articles

Integration between Moodle and Academic Information System using Restful API for Online Learning

During the current pandemic, it is encouraging educational institutions to carry out distance learning, so many learning management system (LMS) platforms can be used to support distance learning. Each LMS has a different process flow but has the same goal of making it easier to manage learning content. When an LMS is implemented in an educational institution, it requires matching data for courses, students and lecturers that are available in the academic information system (AIS) at the institution, this is one of the weaknesses of all LMS because the data are not interrelated between AIS and LMS. The purpose of this research is to create an integrated system to equalize data between AIS and LMS using the synchronization method through the Application Programming Interface (API). The results of this application will combine data from AIS and LMS which will then be tested for automatic course creation according to class data, courses, lecturers and students at AIS. The test results of this system are said to be successful because each function that is designed has been running well without any fatal errors. The most important thing that needs to be considered when synchronizing is that there is a link between the data on the AIS and LMS, failure occurs on some courses because the email users in the AIS and LMS are different.

A maximum entropy classification scheme for phishing detection using parsimonious features

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM).

Email Classification for Forensic Analysis by Information Gain Technique

One of the most interesting fields nowadays is forensics. This field is based on the works of scientists who study evidence to help the police solve crimes. In the domain of computer science, the crimes within computer forensics are usually network attacks, and most attacks are over the email (the case of this study). Email has become a daily means of communication which is mainly accessible via internet. People receive thousands of emails in their inboxes and mail servers (in which people can find emails in those lists). The aim of this study is to secure email users by building an automatic checking and detecting system on servers to filter the bad emails from the good ones. In this paper, the authors will do a study based on a new method of emails clustering to extract the bad and good ones. The authors use the gain information technique like an algorithm of clustering, whose principle is to calculate the importance of each attribute (in this study, the authors talk about the attributes that constitute the email) to draw the importance tree and at the end extract the clusters.

Raspberry Pi House Safeguard System with Alarm and Email Alert

This paper is helpful in providing not only safeguard to the house but also alerts the owner through email. This system quickly alerts the presence of an unknown person using Raspberry Pi. This system deploys a Pi camera which takes the picture of the unknown person and includes the image in the email. This system has various areas such as our House s, offices or anywhere, wherever safeguard is necessary, picture captured by using the webcam will send to user email by using SMTP protocol, Email notifications can be sent out to your customers using either Gmail SMTP server or your own SMTP server

Development of Proposed Ensemble Model for Spam e-mail Classification

Spam e-mail documents classification is a very challenging task for e-mail users, especially non IT users. Billionsof people using the internet and face the problem of spam e-mails. The automatic identification and classificationof spam e-mails help to reduce the problem of e-mail users in managing a large amount of e-mails. This work aimsto do a significant contribution by building a robust model for classification of spam e-mail documents using datamining techniques. In this paper, we use Enorn1 data set which consists of spam and ham documents collectedfrom Kaggle repository. We propose an Ensemble Model-1 that is an ensemble of Multilayer Perceptron (MLP),Naïve Bayes and Random Forest (RF) to obtain better accuracy for the classification of spam and hame-mail documents.Experimental results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers aswell as other proposed ensemble models in terms of classification accuracy. The suggested and proposed EnsembleModel-1 produces a high accuracy of 97.25% for classification of spam e-mail documents.

Junk Filtering through Naive Bayesian Algorithm

As the world is seamlessly developing at a very high pace, we have been seeing enormous growth in various sectors of Technology. Networking has played a crucial part in the exchange of technological culture around the globe, and the Internet being the sole medium of Network enhancement has taken over every aspect of our society. Today, most of the professional communications are done through emailing. As far as email has proven to be an efficient, professional and easy way of communication, it also comes with the disadvantage of unwanted bulk bombarding of spam content. This has been a critical concern for email users. Consequently, it has become very difficult for spam filters to efficiently filter the unwanted emails, since nowadays emails are written in such a manner that any existing algorithm cannot give 100% accuracy in predicting spam. This paper deals with Naive Bayesian Classifier that is a Machine Learning algorithm for antispam filtering, which gives satisfactory results by automatically constructing anti-spam filters with extended conduct. The review over the researched performance of Naive Bayes algorithm is done by the investigations of Spam ham csv datasets. The performance of the algorithm is evaluated based on the accuracy, recall and precision it shows on the mentioned datasets. This technique gives 96-97% accuracy and 89% precision on the investigated dataset. The result also highlights that the content of the email and the number of instances of the dataset has an apparent effect on the performance of the algorithm.

Usability of End-to-End Encryption in E-Mail Communication.

This paper presents the results of a usability study focused on three end-to-end encryption technologies for securing e-mail traffic, namely PGP, S/MIME, and Pretty Easy Privacy (pEp). The findings of this study show that, despite of existing technology, users seldom apply them for securing e-mail communication. Moreover, this study helps to explain why users hesitate to employ encryption technology in their e-mail communication. For this usability study, we have combined two methods: 1) an online survey, 2) and user testing with 12 participants who were enrolled in tasks requiring e-mail encryption. We found that more than 60% of our study participants (in both methods) are unaware of the existence of encryption technologies and thus never tried to use one. We observed that above all, users 1) are overwhelmed with the management of public keys and 2) struggle with the setup of encryption technology in their e-mail software. Nonetheless, 66% of the participants consider secure e-mail communication as important or very important. Particularly, we found an even stronger concern about identity theft among e-mail users, as 78% of the participants want to make sure that no other person is able to write e-mail on their behalf.

PERBEDAAN MOTIF PENGGUNAAN BUKU LANGKA TERCETAK DI PERPUSTAKAAN NASIONAL REPUBLIK INDONESIA BERDASARKAN GENDER

The motives for the use of media are different, one of the factors is gender. This study aims to see the motives for using printed rare books in the National Library of Indonesia and whether there is a different motive for using printed rare books between men and women. The population of this research is active e-mail users, registered members of the National Library of Indonesia, and who have read printed rare books. This type of research is quantitative using the Uses and Gratification method. The sample size is 75 respondents. The sampling technique used was proportional stratified random sampling. Based on the results of the analysis, there are 4 motives for using printed rare books, namely Information Motive, Personal Identity Motive, Integrity Motive and Social Interaction, and Entertainment Motive. The total results show that the most dominant motive for using rare books printed in the National Library of Indonesia is information on motives, with a proportion of 87% and 83% to satisfy their curiosity and to learn about events and environmental conditions after reading printed rare books. The lowest motive is the personal identity motive with a proportion of 70% and 67% to adapt the behavior and behavior values obtained when reading rare books printed in everyday life and ready to see information about events or environmental conditions in the past, latest or rare. The results of hypothesis testing show that H1 is rejected and H0 is accepted with a sig 0.05, namely 0.869 0.05, so it can be said that there is no difference in motives for using rare books in the National Library of Indonesia between men and women.

Spam E-Mail Filtering: A Review of Techniques

Every year, the number of uninvited email received by the common email user will increase dramatically. According to IDC, Spam has accounted for 38 percent of the 31 billion emails sent each day in North America in 2004, up from 24 percent in 2002. keeping pace with amount of spam is that the quantity of filtering solutions out there to assist eliminate it. This paper describes in detail how several of the most common spam filtering technologies work, how effective they are at stopping spam, their strengths and weaknesses, and techniques used by spammers to circumvent them.

An Incentive Mechanism for Reporting Phishing E-Mails Based on the Tripartite Evolutionary Game Model

The human is considered as the important link in the phishing attack, and the e-mail security provider encourages users to report suspicious e-mails. However, evidence suggests that reporting is scarce. Therefore, we study how to motivate users to report phishing e-mails in this paper. To solve the problem, a tripartite evolutionary game model among e-mail security providers, e-mail users, and attackers is constructed. We obtain the desired evolutionary stable strategy through solving the replicator dynamics equations. Moreover, the evolution process to the desired evolutionary stable strategy is derived, which can guide the e-mail security provider to make a reasonable incentive mechanism. Lastly, we experiment with a large real-world e-mail network. The experiment results show that our model is effective and practical.

Prediction of Spam Email using Machine Learning Classification Algorithm

Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.

Public-key encryption with bidirectional keyword search and its application to encrypted emails

A traditional Public-key Encryption scheme with Keyword Search (PEKS) allows multiple senders to encrypt keywords under the public key of a receiver such that the receiver can search on these encrypted keywords using his/her searching secret key. In encrypted email systems, an email user not only needs to search on encrypted emails received from other users, but also needs to search on encrypted emails sent to other users. Motivated by this, the paper proposes a cryptographic method to allow these two types of user (i.e., senders and receivers) to search on encrypted keywords, which is called Public-key Encryption with Bidirectional Keyword Search (PEBKS). We give formal definitions of a PEBKS scheme and its indistinguishable security model to capture the scenario that no adversary can efficiently distinguish two ciphertexts of keywords from each other, even if the adversary can adaptively obtain search trapdoors of many keywords. Specifically, we propose a concrete PEBKS scheme, whose security relies on a standard hard problem, i.e., bilinear Diffie–Hellman problem, in the random oracle model. Finally, we simulate the proposed PEBKS scheme to assess its practicability and convinces that its feasibility to be applied to encrypted email systems.

Maladaptive behaviour in response to email phishing threats: The roles of rewards and response costs

Email users are vulnerable to phishing threats and a greater understanding of how to protect them is needed. This research investigates how response costs and rewards influence users’ protective and maladaptive security behaviours in the domain of phishing by testing a model that extends Protection Motivation Theory to more explicitly consider the role of maladaptive behaviour. The results show that rewards influence maladaptive behaviour rather than protective behaviour in response to email phishing threats, and that response costs influence both maladaptive and protective behaviours. That is, any perceived benefits from not performing protective behaviours against email phishing threats will result in an increase in the performance of maladaptive behaviours. Similarly, any increases in costs perceived to be incurred for performing protective behaviours against email phishing threats will result in a decrease in protective behaviour and an increase in maladaptive behaviour. These findings have both practical implications and implications for future research into protections against phishing threats.

So Many Phish, So Little Time: Exploring Email Task Factors and Phishing Susceptibility.

The present studies examine how task factors (e.g., email load, phishing prevalence) influence email performance. Phishing emails are a paramount cybersecurity threat for the modern email user. Research attempting to understand how users are susceptible to phishing attacks has been limited and has not fully explored how task factors (e.g., prevalence, email load) influence accurate detection. In three experiments, participants classified emails as either legitimate or not legitimate and reported on a variety of other categorizations. The first two experiments examined how email load and phishing prevalence influence phishing detection independently. The third experiment examined the interaction of these two factors to determine whether they have compounding effects. All three experiments utilized individual difference variables to examine how cognitive, behavioral, and personality factors may influence classifications. Experiment 1 suggests that high email load can make the task appear more challenging. Experiment 2 indicates that low phishing prevalence can decrease sensitivity for phishing emails. Experiment 3 demonstrates that high levels of email load can decrease classification accuracy under 50/50 prevalence rates. Notably, performance was poor across all experiments, with phishing detection near chance levels and low discriminability for emails. Participants demonstrated poor metacognition with over confidence, low self-reported difficulty, and low perceived threat for the emails. Overall, the present studies suggest that high email load and low phishing prevalence can influence email classifications. Organizations and researchers should consider the influences of both email load and phishing prevalence when implementing phishing interventions.

E-participation decision across different channels

PurposeThis study focuses on understanding how channel features can affect people's intention to continue to use an electronic channel in public affairs and their recommendation behaviors. Specifically, three different channels are focused on: email, microblogs and online meetings.Design/methodology/approachA research model on an e-participation channel based on the channel-disposition framework was developed and an online survey was conducted to collect data from 397 individuals who used three e-participation channels to validate seven hypotheses.FindingsThe study found that information quality, channel interaction quality and the social appearance of other citizens all had a significant impact on users' intention to continue to use an electronic channel, which, in turn, affected their recommendation behaviors. However, the impact differed across the three e-participation channels. Information quality had a stronger impact on microblog and online meeting users' intention to continue to use these channels than on email users' intention to continue using email to participate in public affairs. Channel interaction quality had a stronger impact on email users' intention to continue to use email than on microblog and online meeting users' intention to continue to use these channels in public affairs.Originality/valueThis study helps better explain how various channels and their features can affect participants' use intentions and behaviors in e-participation. It also provides practical guidance for government to better manage e-participation channels and effectively engage citizens in public affairs.

User Motive Factors: The Rudiments to Email Satisfactory Communication

Our study aims to evaluate if user motive factors, comprising; cognitive needs, social integrative needs, and personal integrative needs might influence email satisfactory communication among South-East Nigerian academic staff. The study was proposed because empirical evidence has proven these user motive factors as effective elements of the uses and gratification theory; capable of influencing satisfactory communication on other virtual communication media. However, past studies appeared little or no consideration has been explicitly given towards integrating them on email interaction related-study to attest them. Therefore, this study was conducted via a quantitative research method because it required hypotheses testing to infer the outcomes and generalize them to the targeted population. Regarding the key findings of the study, it showed that South-East Nigerian academic staff email usage patterns differed from one another. However, the salient email usage pattern established that almost the majority of them mostly prefer using their email addresses to the official ones. Furthermore, the direct effects relationships demonstrated that only two dimensions of user motive construct had significant direct effects on email satisfactory communication. Whilst the social integrative needs had no significant direct effects. Nonetheless, personal integrative needs contributed most to email satisfactory communication. Conclusively, it is recommended that South-East Nigerian academic staff and other email users should mostly consider personal integrative needs and cognitive needs if they intend to achieve email satisfactory communication. The outcomes of our study further contributed to the virtual media satisfactory communication domain, especially the asynchronous ones. Keywords: Email usage patterns, satisfactory communication, asynchronous virtual communications, user motive factors, South-East Nigerian academic staff.

Phishing Mitigation Techniques: A Literature Survey

Email is a channel of communication which isconsideredto be a confidential medium of communication for exchange of information among individuals and organisations. The confidentiality consideration about email is no longer the case as attackers send malicious emails to users to deceive them into disclosing their private personal information such as username, password, and bank card details, etc. In search of a solution to combat phishing cybercrime attacks, different approaches have been developed. However, the traditional exiting solutions have beenlimited in assisting email users to identify phishing emails from legitimate ones. This paper reveals the different email and website phishing solutions in phishing attack detection. It first provides a literature analysis of different existing phishing mitigation approaches. It then provides a discussion on the limitations of the techniques, before concluding with anexplorationintohow phishing detection can be improved.

Android Mobile Based Voting System through Facial Recognition

Due to the increase in population, identification of fake voting is getting more complicated. The only proof of identification is just a voter id. Due to this there has been difficulty in identifying fake voters. To overcome this ssue, an approach based on facial recognition system using android mobile is proposed. This system consists of three phases. In first phase the details of voter like name, adhaar number and email id will be verified. In the second phase, user login through android phone where mobile application captures the voter’s face and will be sent to the server where the facial recognition takes place, if the match is found then the OTP (One Time Password) will be sent to the users email address. In the third phase, the user has to enter the OTP and if the OTP is correct, voter will be allowed to vote. This proposed system will be secure and eliminate any fake voters. Voters can also avoid standing in the queue in order to vote. Lot of benefits can be achieved by using this proposed system.

Android Mobile Based Voting System through Facial Recognition

Due to the increase in population, identification of fake voting is getting more complicated. The only proof of identification is just a voter id. Due to this there has been difficulty in identifying fake voters. To overcome this ssue, an approach based on facial recognition system using android mobile is proposed. This system consists of three phases. In first phase the details of voter like name, adhaar number and email id will be verified. In the second phase, user login through android phone where mobile application captures the voter’s face and will be sent to the server where the facial recognition takes place, if the match is found then the OTP (One Time Password) will be sent to the users email address. In the third phase, the user has to enter the OTP and if the OTP is correct, voter will be allowed to vote. This proposed system will be secure and eliminate any fake voters. Voters can also avoid standing in the queue in order to vote. Lot of benefits can be achieved by using this proposed system.

WITHDRAWN: Data science based analytics of real time data and key dimensions with emotions evaluations

Abstract The approach of analysing massive data types, that are big data, is the analysis of hidden trends, unexplained interactions, market conditions, customer preferences and other important business information. The analysis is focused on big data analysis. The empirical findings would continue to maximise the performance and productivity of marketing, new sales perspectives, better service for consumers and improved organisational performance. The key goal of big data analysis is to help companies select best by empowering data scientists, statistical modellers and other researchers to assess large quantities of transaction data and Other sources of data which traditional business intelligence systems could not take advantage of. These include logs of webservers and clickstream data, social media material and social network notifications, user e-mail text and survey ansverses, telephone call reports and device data from sensors connected to the Internet of Things. Few may only compare Big data on semi-organised and unstructured information such as this, but also analyst companies such as Gartner Inc. and Forrester Research Inc. intrinsics to Big-Data analytics applications of transactions and other structured data.