Email Features Research Articles

Anomaly Detection and Prevention in SMTP

Emails are a vital part of communication in organizations, but they are increasingly at risk of threats like data breaches and cyberattacks. Protecting sensitive information shared through emails is essential for maintaining data security and complying with regulations. However, manually checking emails for anomalies or confidential information can be tedious and impractical, especially in large-scale setups.To address this issue, we propose a system that detects and classifies email anomalies in real time. The system uses advanced machine learning algorithms to analyze email content and sending patterns, effectively identifying unusual activities. By leveraging a pre-trained dataset like the Enron email dataset, it compares email features to detect anomalies. Additionally, it integrates with an SMTP server using Python libraries, allowing it to monitor outgoing emails and flag any suspicious behavior in email-sending patterns.This system provides real-time oversight, ensuring that threats are quickly identified and managed. With a simple and intuitive interface, users can easily monitor and address flagged emails, enabling proactive security measures. In the end, this solution strengthens email security, ensures compliance with data protection rules, and creates a more secure and trustworthy communication environment. Index Terms— Mail Security, Machine Learning , Anomaly Detection , Data Protection

A Classifier Model to Detect Phishing Emails Using Ensemble Technique

Phishing attacks usually take advantage of weaknesses in the way users behave. An attacker sends an email to the recipient that mimics a genuine email with phishing links. When the recipient clicks on the embedded links, the attacker can harvest critical information like credit card numbers, usernames or passwords as a result of entering the compromised account. Online surveys have put phishing attacks as the leading attack for web content, mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attacks is about $1.5 billion annually. This is a global threat to information security, and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these losses and reputation injury. This research paper explores and reports the use of multiple machine learning models by using an algorithm called Random Forest and using more phishing email features to improve the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier to detect and prevent phishing emails and test the model with existing data. A dataset consisting of benign and phishing emails will be used to conduct supervised learning by the model. Expected accuracy is 99.9%, with a rate of less than 0.1% for False Negatives (FN) and False Positives (FP).

Detecting Phishing Emails Using Random Forest and AdaBoost Classifier Model

Phishing attack occurs when a phishing email which is a legitimate-looking email, designed to lure the recipient into believing that it is a genuine email to open and click malicious links embedded into the email. This leads to user reveal sensitive information such as credit card number, usernames or passwords to the attacker thereby gaining entry into the compromised account. Online surveys have put phishing attack as the leading attack for web content mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attack is about $1.5 billion per year. This is a global threat to information security and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these loses and reputation injury. This research paper explores and reports the use of a combination of machine learning algorithms; Random Forest and AdaBoost and use of more phishing email features in improving the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier which can detect phishing and prevent phishing emails and test the model with existing data. A dataset consisting of both benign and phishing emails will be used to conduct a supervised learning by the model. Expected accuracy is 99.9%, False Negative (FN) and False Positive (FP) rates of 0.1% and below.

“The language is very formal and appropriate”: L2 learners’ in/appropriateness evaluations and metapragmatic judgments in student-faculty emails

“The language is very formal and appropriate”: L2 learners’ in/appropriateness evaluations and metapragmatic judgments in student-faculty emails

Generic characteristics of patients' e-consultations with general practitioners.

Electronic communication between patients and general practitioners (GPs), known as e-consultations, was introduced in Denmark as a cost-effective and convenient means for patients to access their GP and receive test results as well as answers to quick questions. Research on e-consultations internationally has found that patients are generally very positive, while doctors are more hesitant. Most studies of e-consultations have involved survey or interview methodologies, while studies of their content, form or language are limited. In this study, we address this gap by proposing that genre may be a productive concept for comprehending textual aspects of e-consultations. We explore the following two questions: (1) what are the generic characteristics of the e-consultation?; and (2) do e-consultations belong to the email genre? Based on an analysis of two Danish corpora of patients' first turns in e-consultations with their GPs, we identify an underlying move structure: subject, opening, update, problem presentation, request, argumentation, closing. We argue that the e-consultation is an example of an email genre with identifiable conventions, which are both specific to the communicative function of e-consultations and aligned with generic features of emails.

A Comprehensive Overview on Intelligent Spam Email Detection

Abstract: Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years. Nowadays, automatic email filtering appears to be the most successful strategy for preventing spam. Only several years ago most of the spam could be reliably dealt with by blocking e-mails coming from certain addresses or filtering out messages with certain subject lines. Spammers started employing a number of cunning strategies to get beyond filtering techniques, such as utilizing random sender addresses and/or adding random characters to the message subject line's beginning or conclusion. Machine learning techniques now a days are used to automatically filter the spam e-mail in a very successful rate. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Understanding, observing, and providing knowledge about a statistical occurrence are all terms used here. In the first place, data collection and representation are typically problem-specific (i.e., for email messages), and in the second place, e-mail feature selection and feature reduction aim to lower the dimensionality (i.e. the number of features).Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set. Machine Learning approach includes lots of algorithms that can be used in e-mail filtering like Naïve Bayes, K-nearest neighbour, Support VSector Machine, classifiers. In conclusion, we try to summarize the performance results of the few machine learning methods in terms of spam precision and accuracy.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body text automatic classification into phishing and email spam. This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network (IFFO-SRRNN) based on Applied Linguistics for Email Classification. The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails. At the preliminary level, the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation. Next, the SRRNN method can be useful in recognizing and classifying spam emails. As hyperparameters of the SRRNN model need to be effectually tuned, the IFFO algorithm can be utilized as a hyperparameter optimizer. To investigate the effectual email classification results of the IFFO-SRDL technique, a series of simulations were taken placed on public datasets, and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

FEATURE ENGINEERING WITH SENTENCE SIMILARITY USING THE LONGEST COMMON SUBSEQUENCE FOR EMAIL CLASSIFICATION

Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.

Usability of Language Features of Emails for Detecting Misrepresentations

ABSTRACTThe study experimentally examines the usability of a participant's writing style in email inquiries for fraud (misrepresentation) risk assessment by determining whether auditors can detect appropriate language-based risk factors in an email. The results suggest risk factors are present in emails, but auditors fail to use them for risk assessment. For example, shorter emails arouse skepticism when, in fact, this language feature is an insignificant risk factor. More fitting linguistic-based risk factors are lack of specifics and overuse of qualitative descriptions. Guidance may improve auditors' recognition of appropriate linguistic-based risk factors in email text. Alternatively, risk factors may be too vague to perceive, suggesting a need for techniques to strengthen them in an email inquiry.

Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.

Email thread sentiment sequence identification using PLSA clustering algorithm

• SentiWordNet lexicon is used for generating sentiment features of email data. • Probabilistic Latent Semantic Analysis algorithm is used for clustering. • The sentiment sequence of thread and thread size are identified. • Evaluated the proposed model using accuracy, precision, recall and F-measure. Email messaging is the most common way of providing effective communication between internauts. Consequently, the total sent and received emails count will be increased. But, the internaut can't remember all such emails. Even though email thread identification approaches give satisfactory benefits to the internauts, but they may fail to alert them for a cause to identify the sentiments behind an email thread. To address, this issue Probabilistic Latent Semantic Analysis clustering algorithm has been used in this paper to identify the email sentiment thread sequence. The sentiment and the thread sequence within the emails have been discovered as clustering sentiment polarity and temporal categories with the help of PLSA clusters. At the initial stage, we used three feature extraction methods, latent semantic analysis (LDA), bag of words (BoW), TF-IDF and SentiWordNet (SWN) lexicon for generating sentiment features of email. Next, Probabilistic Latent Semantic Analysis algorithm is used to form email clusters based on sentiment features. Thus, it helps to identify thread sentiment and sequence of sentiment threads. Email threads give a mechanism by which any user will be able to find out the sequence in the thread on the basis of sentiment analysis of email related to a specific set of communication during a specific time period. Various parameters evaluation measures have been considered in this work to evaluate the proposed model such as accuracy, precision, recall and F-measure, and the proposed algorithm is compared with other standard algorithms. Furthermore, a statistical test has also been performed.

The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions

Code review is an important quality assurance activity for software development. Code review discussions among developers and maintainers can be heated and sometimes involve personal attacks and unnecessary disrespectful comments, demonstrating, therefore, incivility. Although incivility in public discussions has received increasing attention from researchers in different domains, the knowledge about the characteristics, causes, and consequences of uncivil communication is still very limited in the context of software development, and more specifically, code review. To address this gap in the literature, we leverage the mature social construct of incivility as a lens to understand confrontational conflicts in open source code review discussions. For that, we conducted a qualitative analysis on 1,545 emails from the Linux Kernel Mailing List (LKML) that were associated with rejected changes. We found that more than half (66.66%) of the non-technical emails included uncivil features. Particularly, frustration, name calling, and impatience are the most frequent features in uncivil emails. We also found that there are civil alternatives to address arguments, while uncivil comments can potentially be made by any people when discussing any topic. Finally, we identified various causes and consequences of such uncivil communication. Our work serves as the first study about the phenomenon of in(civility) in open source software development, paving the road for a new field of research about collaboration and communication in the context of software engineering activities.

Email Fraud Attack Detection Using Hybrid Machine Learning Approach

Background: E-mail is an efficient way to communicate. It is one of the most commonly used communication methods, and it can be used for achieving legitimate and illegitimate activities. Many features that can be effective in detecting email fraud attacks, are still under investigation. Method: This paper proposes an improved classification accuracy for fraudulent emails that is implemented through feature extraction and hybrid Machine Learning (ML) classifier that combines Adaboost and Majority Voting. Eleven ML classifiers are evaluated experimentally within the hybrid classifier, and the performance of the email fraud filtering is evaluated by using WEKA and R tool on a data set of 9298 email messages. Results: The performance evaluation shows that the hybrid model of Voting using Adaboost outperforms all other classifiers, with the lowest Error Rate of 0.6991%, highest f1-measure of 99.30%, and highest Area Under the Curve (AUC) of 99.9%. Conclusion: The utilized proposed email features with the combination of Adaboost and Voting algorithms prove the efficiency of fraud email detection.

Analysing Generical Moves in Pedagogical Sales Reply Emails

This study aimed to investigate the generic features in the business emails written by the undergraduates of Universiti Malaysia Kelantan (UMK). Besides that, this study intended to propose a standard generic structure in writing a business email. The emails were gathered from the respondents' assignment that comprised of two email-based questions; however, this study analysed the generic structure of sales reply emails. The emails were compared with Bhatia's promotional sales letter moves. The methodology of this study was computer-assisted corpus analysis (CACA) by Manvender (2011). The data were analysed using a genre-based technique by Bhatia (1993). The particular moves and steps in both types of texts were recognised and categorised individually. The rhetorical structure of sales reply emails consists of three major moves: introducing product, product details, offering incentives. Although the respondents showed similarities in their moves, there were some differences such as the number and content of the moves as well as the contextual patterns.

Trends and Features of Russian Business Email: Contrastive Analysis Based on Materials from Business Communication Textbooks

Trends and Features of Russian Business Email: Contrastive Analysis Based on Materials from Business Communication Textbooks

What makes phishing emails hard for humans to detect?

This research investigates the email features that make a phishing email difficult to detect by humans. We use an existing data set of phishing and ham emails and expand that data set by collecting annotations of the features that make the emails phishing. Using the new, annotated data set, we perform cluster analyses to identify the categories of emails and their attributes. We then analyze the accuracy of detection in each category. Our results indicate that the similarity of the features of phishing emails to benign emails, play a critical role in the accuracy of detection. The phishing emails that are most similar to ham emails had the lowest accuracy while the phishing emails that were most dissimilar to the ham emails were detected more accurately. Regression models reveal the contribution of phishing email’s features to phishing detection accuracy. We discuss the implications of these results to theory and practice.

A feature-centric spam email detection model using diverse supervised machine learning algorithms

PurposeThis research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection.Design/methodology/approachExisting studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam).FindingsAnalysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%.Originality/valueThis research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

From Feature Engineering and Topics Models to Enhanced Prediction Rates in Phishing Detection

Phishing is a type of fraud attempt in which the attacker, usually by e-mail, pretends to be a trusted person or entity in order to obtain sensitive information from a target. Most recent phishing detection researches have focused on obtaining highly distinctive features from the metadata and text of these e-mails. The obtained attributes are then used to feed classification algorithms in order to determine whether they are phishing or legitimate messages. In this paper, it is proposed an approach based on machine learning to detect phishing e-mail attacks. The methods that compose this approach are performed through a feature engineering process based on natural language processing, lemmatization, topics modeling, improved learning techniques for resampling and cross-validation, and hyperparameters configuration. The first proposed method uses all the features obtained from the Document-Term Matrix (DTM) in the classification algorithms. The second one uses Latent Dirichlet Allocation (LDA) as a operation to deal with the problems of the “curse of dimensionality”, the sparsity, and the text context portion included in the obtained representation. The proposed approach reached marks with an F1-measure of 99.95% success rate using the XGBoost algorithm. It outperforms state-of-the-art phishing detection researches for an accredited data set, in applications based only on the body of the e-mails, without using other e-mail features such as its header, IP information or number of links in the text.

Perceptual representation of spam and phishing emails

SummaryUnderstanding how computer users allocate attention to features of potentially dangerous emails could help mitigate costly errors. Which features are salient? How stable is attention allocation across variation in email features? We attempted to measure the mental salience of several email features common in spam and/or phishing emails. We created two email sets: one in which messages contained company logos and urgent actionable links and one without these features. Participants rated pairwise similarity of emails within each set. Multidimensional scaling (MDS) analysis was conducted to quantify psychological similarity between emails. A separate group rated the same emails for presence of five other features: important downloadable content, collecting personal information, account deletion or suspension, advertisement, and large images with clickable content. Regressing feature ratings onto the MDS coordinates revealed that similarity judgments were influenced mostly by advertisement/large images and collecting personal information, regardless of presence or absence of company logos and urgent actionable links.

Generic Structural Features of Requestive Academic Emails

The aim of the present study is to analyze the generic structural features of requestive academic emails. For this purpose, 182 emails written by 81 Tunisian postgraduate students to their professor were analysed. The collected academic email messages share a main communicative function which is requesting. This study drew on the framework of genre analysis and employed the pattern of move schemata proposed by Swales (1990) and follows the typology suggested by Guffey (2010). The gathered emails are made up of the email’s subject line, an opening, body text and closing. The findings revealed that more than half of the subject lines of emails are either inappropriate, improper or do not exist. This shows that the participants are to some extent not able to write proper subject lines when addressing their professor however they did appear to pay attention to greetings in their emails. By examining the structure of the body of the gathered emails, it is noticed that postgraduate students tended to use the inductive style when emailing their professor. They do not go straight to the request. Instead, they go through some strategies (introduction, self-identification, apologizing, or reminding remarks, pre-request and request). The findings revealed that more than half of the emails either do not display perfect closings or do not display closings while the remaining emails are considered acceptable since they contain both greeting and the sender’s name. Accordingly, this study suggests some useful pedagogical implications. Tunisian EFL learners need to be supplemented with explicit instruction regarding teaching pragmatic issues concerning writing emails. The use of authentic materials in the context of pragmatics instruction is highly recommended Another practical method is to introduce and teach email writing guidelines and etiquettes so that Tunisian students can readily refer to them when writing emails. Keywords : structural features, request, email, moves. DOI : 10.7176/JEP/10-19-03 Publication date :July 31 st 2019