Email Users Research Articles

Classification of Unwanted E-Mails (Spam) with Turkish Text by Different Algorithms in Weka Program

Abstract: Today, with the widespread use of the Internet, electronic communication tools have also been widely used. One of these tools is e-mails. E-mails are easy to use and provide the opportunity to reach thousands of people at the same time. This advantage causes some bad uses. E-mail users are faced with dozens of unsolicited mails (spam) against their will. In this study, 1017 mails collected from about 20 different Gmail and Hotmail accounts were classified as spam or regular e-mail using the algorithms in the Weka program, and the success of the algorithms was compared. In the study, 45 different algorithms were tested. The highest classification success was obtained with the NavieBayesMultinominal and NavieBayesMultinominalUpdateable algorithms with 94.7886% correct classification. Among other classifier algorithms, Trees RandomForest algorithm 93.6087%, Meta. MultiClassClassifier and Functions SGD 92.4287%, Functions SMO 91.7404%, Meta RandomCommittee 91.0521%, Bayes NavieBayes and Bayes NavieBayesUpdateable 90.3638% classification success.

IMPROVING INTERNATIONAL STUDENTS’ COMMUNICATION SKILLS AND INTEGRATION AT THE UNIVERSITY OF MISKOLC BY THE COMBINATION OF LEADERSHIP AND TECHNOLOGY ACCEPTANCE MODEL

Recent studies in multicultural societies have begun to integrate sociological ideas into the modeling of personal behavior. In particular, this new approach emphasizes how social interdependence influences the way a person behaves and communicates. By developing communication skills, international students can reduce individual stress, improve their academic achievements, and also become successful in the job market by integrating and cooperating better with people who have different cultural backgrounds. In addition, digitalization has become a widespread global phenomenon and the main driving force in this era of mankind. Introducing new technologies to any organization doesn’t mean it’s easy and involves many challenges, like the acceptance and adoption of new technologies by employees and customers. In this field, Davis (1985) introduced the technology acceptance model to determine the individual usage of technology. Therefore, the objective of our research is to analyze the factors that affect international students’ communication skills via the usage of email at the University of Miskolc, Hungary, based on applying the technology acceptance model and transformational leadership. Unfortunately, we could not access all the international students because of COVID-19 and restrictions. Therefore, our sample size is not big enough.

An Integrated Approach of Proposed Pruning Based Feature Selection Technique (PBFST) for Phishing E-mail Detection

Introduction: The entire world is shifting towards electronic communication through Email for fast and secure communication. Millions of people, including organization, government, and others, are using Email services. This growing number of Email users are facing problems; therefore, detecting phishing Email is a challenging task, especially for non-IT users. Automatic detection of phishing Email is essential to deploy along with Email software. Various authors have worked in the field of phishing Email classification with different feature selection and optimization techniques for better performance. Objective: This paper attempts to build a model for the detection of phishing Email using data mining techniques. This paper's significant contribution is to develop and apply Feature Selection Technique (FST) to reduce features from the phishing Email benchmark data set. Methods: The proposed Pruning Based Feature Selection Technique (PBFST) is used to determine the rank of feature based on the level of the tree where feature exists. The proposed algorithm is integrated with already developed Bucket Based Feature Selection Technique (BBFST). BBFST is used as an internal part to rank features in a particular level of the tree. Results : Experimental work was carried out with open source WEKA data mining software using a 10-fold cross-validation technique. The proposed FST was compared with other ranking based FSTs to check the performance of C4.5 classifier with Phishing Email data set. Conclusion: The proposed FST reduces 33 features out of 47 features which exist in phishing Email dataset and C4.5 algorithm produces remarkable accuracy of 99.06% with only 11 features and it has been found to be better than other existing FSTs.

Voice Based Email for Visually Challenged

Abstract: The inception of Internet has caused a dramatic revolution in many fields. Internet, being a global computer network, has made life of people easier, as they could access any information they want, more efficiently. Communication is one of the main fields that Internet has revolutionized. Communication has become so easy due to the integration of communication technologies with the internet. E-mails are considered to be the most reliable way of Internet communication, for sending or receiving some important information. Visually challenged person feels difficulty in using these technologies as it requires visual perception. Around 250 million people in this world, are unaware about the usage of Internet or E-mail. The only way by which a visually impaired person can use the current email application is that, they require the help of a third person who would send mail on the behalf of the them.But this does not guarantee privacy and security for that person. This gave the idea of developing a voice-based email which requires only less training. It makes use of mouse operations and speech recognition. It could be used by both visually impaired and also by a normal person Index Terms: Feature extraction, MFCC, GMM, Speech recognition, Google API

Errors, Irregularities, and Misdirection: Cue Utilisation and Cognitive Reflection in the Diagnosis of Phishing Emails

The study aimed to examine the role of, and potential interplay between, cue utilisation and cognitive reflection in email users’ ability to accurately (and efficiently) differentiate between phishing and genuine emails. 145 participants completed the Cognitive Reflection Test (CRT), a phishing diagnostic task, and the Expert Intensive Skill Evaluation (EXPERTise 2.0) battery, which provided a gauge of users’ cue utilisation in the domain. The results revealed an interaction between users’ cognitive utilisation and cue reflection, whereby users low in both facets performed significantly worse in diagnosing phishing emails than all other groups. Further, those participants with both higher cue utilisation and cognitive reflection took significantly longer to make their diagnosis. It is concluded that a high level of cognitive reflection was able to compensate for a lower level of cue utilisation, and vice versa. Participants reported using seven types of cue during diagnosis, however, there was no significant relationship between the types of cues used and users’ level of cue utilisation. Taken together, the findings have implications to the design of user-level interventions in relation to the identification of vulnerable users, as well as the need to consider training approaches that extend beyond the use of simple cue inventories.

Blocking Of Spam Mail Using K-Means Clustering Algorithm

Email is a method of exchanging digital messages between people using digital devices such as computers, tablets and mobile phones. Email spam also known as junk email is unsolicited bulk messages send through email. The use of spam has been growing in popularity since 1990s and is a problem faced by most email users. Recipients of spam often have had their email addresses obtained by spambots which are automated programs that crawl the internet looking for email addresses. Origin blacklisting is used to detect and filter these kinds of emails. The sources of emails are provided by origin detection. In this origin identity, the spam blocking system finds the source and if any source matches with the user identity, the spammer is blocked to send email. But the system cannot check the contents of spam. The Propose system verifies the mail contents. If the spam content matches with the spam database cluster, then it blocked while sending to receiver. In this system blocks the mail and prevents the flow of data in a network. The porter stemmer and k-means clustering algorithms are using in this propose system.

Blocking Of Spam Mail Using K-Means Clustering Algorithm

Email is a method of exchanging digital messages between people using digital devices such as computers, tablets and mobile phones. Email spam also known as junk email is unsolicited bulk messages send through email. The use of spam has been growing in popularity since 1990s and is a problem faced by most email users. Recipients of spam often have had their email addresses obtained by spambots which are automated programs that crawl the internet looking for email addresses. Origin blacklisting is used to detect and filter these kinds of emails. The sources of emails are provided by origin detection. In this origin identity, the spam blocking system finds the source and if any source matches with the user identity, the spammer is blocked to send email. But the system cannot check the contents of spam. The Propose system verifies the mail contents. If the spam content matches with the spam database cluster, then it blocked while sending to receiver. In this system blocks the mail and prevents the flow of data in a network. The porter stemmer and k-means clustering algorithms are using in this propose system.

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.

Editorial

Editorial

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.

Identification of atypical behavior of bank employees when using e-mail to prevent information leakages

The article presents the results of using machine learning methods to identify atypical behavior of bank employees when using e-mail. A feature space is formed that characterizes the behavior of e-mail users. The objects were previously clustered using the density-based spatial clustering of applications with noise (DBSCAN) and the fuzzy logic elements. The objects were marked using the inbuilt business rules, and the training sample was formed in the absence of marked data. The most informative features are selected, and a model of classification of e-mail users by the type of their behavior is constructed. A feature space is formed that defines the characteristics of a particular message to identify messages that are the information security incidents. Preliminary data processing was performed by removing the duplicates and encoding the categorical variables. A model of messages classification is constructed. The best combination of the machine learning method and the feature selection algorithm was determined using quality metrics. The constructed models allow specialists of cybersecurity departments of banks to identify employees with abnormal behavior and possibly involved in information leaks. A software tool in Python was developed that makes it easier to identify the final status of a message by partially replacing its manual detection for an automatic one.

Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study

Emails have become the most economical and fastest communication forms. However, during the past few years, the increment of email users has dramatically increased spam emails. Various anti-spam techniques have been developed to minimize if not eliminate the spam problem. In this paper, we study the disparity in the effectiveness of using different decision tree algorithms in email classification and combat spam problems. For that, we have chosen Universiti Utara Malaysia emails as a case study. To achieve the best possible classification accuracy, we compared all chosen algorithms’ performance, which are Random Forest, LMT, Decision Stump, J48, Random Tree, and REP Tree. The experimental results showed that the Decision Stump algorithm is more effective to be used in classifying the emails, and the F-measures, Precision, and recall score for the Decision Stump algorithm are higher than the other comparison algorithms.

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.

Mask and Temperature Detection and Automatic Sanitizer dispenser Using Raspberry Pi

Due to Covid-19, there are currently no effective face mask detection applications, which are in high demand for transportation, densely populated areas, large-scale manufacturers, residential districts, and other organizations to assure safety. Also, the absence of large datasets of images with mask has made task more cumbersome and challenging. This project proposes a method for identifying people without wearing using face recognition technique by using python programming and from Open CV library, Keras and tensor flow. Standalone embedded device has been developed by using Raspberry Pi electronic development board and can be operated on Battery power supply, wireless internet connectivity by using USB modem. Automatic e-mail notification has been achieved by sending security alert mail to the user e-mail id when the person without mask tries to enter the premises. This proposed system is more effective, reliable, and this system consumes very less data and power compared to the other existing systems.

Leading in times of crisis: How perceived COVID-19-related work intensification links to daily e-mail demands and leader outcomes.

The COVID‐19 crisis brought numerous challenges to work life. One of the most notable may be the acceleration of digital transformation, accompanied by an intensification of e‐mail usage and related demands such as high e‐mail workload. While research quickly started to examine the implications of these changes for employees, another important group of stakeholders has been overlooked: leaders. We focus on leaders during the COVID‐19 crisis and examine how COVID‐19‐related work intensification links to leaders' e‐mail overload appraisal and finally exhaustion and transformational leadership, a leader behaviour especially needed in times of crisis. In a 5‐day diary study in September 2020, 84 leaders responded to daily surveys on 343 days. Results of multilevel analysis showed that perceived COVID‐19‐related work intensification was positively linked to worktime spent dealing with e‐mail and appraised e‐mail overload. E‐mail overload appraisal was positively related to leaders' exhaustion, but unrelated to their transformational behaviour. Day‐specific time spent dealing with e‐mail, however, was negatively related to transformational leadership. E‐mail overload appraisal mediated the relationship between COVID‐19‐related work intensification and exhaustion. Turning the focus on leaders during the COVID‐19 crisis, our study has important implications for the design of work of leaders in times of crisis and beyond.

Using Machine Learning Techniques for Predicting Email Spam

The email has become one of the most efficient and cost-effective methods of communication in recent years. However, as the number of email users grows, so does the number of spam emails. Email management has become a big and rising concern for both people and companies as a consequence of its sensitivity to abuse. Spam, or the unsolicited sending of unwanted email messages, is one example of misuse. Spam is defined as unsolicited bulk email, or email sent to a large number of people without their consent. Half of the users receive 10 or more spam emails each day, while some users receive hundreds of unwanted emails per day. Online spiders are used by many spammers to discover email addresses on web pages. Because spam emails can fill up the storage space of a file server quickly, they could cause a very severe problem for many websites with thousands of users for this in this study; we present a method for spam filtering using some machine learning techniques to predict whether an email is spam or no.

An Algorithm for Improving Email Security on the Android Operating System in the Industry 4.0 Era

The world is attesting a tremendous change today, which is remarkably coined as industry 4.0. With several technologies shaping industry 4.0 epoch, notably, its cybersecurity entails the security of communication and network operations activities. The most common form of communication in organisations and business today is electronic mails (email). One of the major threats to email communication is the lack of confidentiality for emails accessed via Android mobile devices due to the weaknesses of the Android operating system (OS) platform. In this study, an algorithm was designed and implemented on an Android application that allows an email sender to compose an email and set the time the email stays in the receiver inbox before it automatically wipes off. Primary data were collected from email users using tightly structured questionnaires and respondents comprised of those with email technical background and typical email users, while secondary data from scholarly journals and articles informed the study design. The designed algorithm was tested and evaluated through expert opinion. The result of the study indicates that the autowipe algorithm addresses the confidentiality issues and threats on Android email clients.

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.