Spam Emails Research Articles

Filtering Out the User?: Are Users Complacent with Phishing Emails Due to Automated Filters?

The present study investigated if users are complacent when interacting with phishing emails because they trust automated filters to remove any potential threats. Participants were asked to examine 50 email classifications made by a simulated email filter. Participants were either assigned a high or low reliability pseudo-filter that “classified” all of the emails as legitimate, resembling an email inbox, or all of the emails as not legitimate, resembling a spam folder. Participants who believed the filter operated at the instructed reliability were more likely to make email classifications that were consistent with their assigned filter, regardless of the emails’ true nature, and demonstrated poorer classification abilities when told their filter acted with higher reliability. These results suggest that some email users may be more vulnerable to phishing emails due to their over trust in automated filters, particularly when they believe the filter operates with high reliability.

An Optimized Approach for Detection and Classification of Spam Email’s Using Ensemble Methods

AbstractSince the advent of email services, spam emails have been a major concern because users’ security depends on the classification of emails as ham or spam. It’s a malware attack that has been used for spear phishing, whaling, clone phishing, website forgery, and other harmful activities. However, various ensemble Machine Learning (ML) algorithms used for the detection and filtering of spam emails have been less explored. In this research, we offer a ML-based optimized algorithm for detecting spam emails that have been enhanced using Hyper-parameter tuning approaches. The proposed approach uses two feature extraction modules, namely Count-Vectorizer and TFIDF-Vectorizer that provide the most effective classification results when we apply them to three different publicly available email data sets: Ling Spam, UCI SMS Spam, and the Proposed dataset. Moreover, to extend the performance of classifiers we used various ML methods such as Naive Bayes (NB), Logistic Regression (LR), Extra Tree, Stochastic Gradient Descent (SGD), XG-Boost, Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perception (MLP), and parameter optimization approaches such as Manual search, Random search, Grid search, and Genetic algorithm. For all three data sets, the SGD outperformed other algorithms. All of the other ensembles (Extra Tree, RF), linear models (LR, Linear-SVC), and MLP performed admirably, with relatively high precision, recall, accuracies, and F1-score.

Image Spam Detection through Pattern Recognition with IP Tracing Technique

Image spam is a kind of E-mail spam where the message text of the spam is presented as a picture in an image file. The basic rationale behind image spam is difficult to detect using text spam filtering methods which is designed to detect patterns in text in the plain-text E-mail body or attachments. A new trend in email spam is the emergence of image spam. The most previous works of image spam detection focused on filtering the image spam on the client side. This proposed system considered more desirable comprehensive solution which embraces the both server side filtering and client side detection methods. The spectral clustering algorithm is introduced to similarity measure for cluster analysis of spam images to filter the attack activities of spammers and fast trace back the spam sources. The active learning algorithm is limited where the learner guides the users to label as few images as possible while maximizing the classification accuracy.

Supporting Organizations in Improving Employee Bulk E-mail --- A Tool Design and Evaluation Study

Organizations often send bulk emails to employees to make them aware of policy changes, organization plans, and events. Many of these emails, however, are long digests with many separate messages that waste employees' time and reduce their awareness. This study introduces CommTool--a prototype tool to help organizational communicators better understand their emails' performance and cost. We first interviewed 5 communicators and identified the need to measure the performance of each message within bulk email. Then we iteratively designed and deployed an organizational bulk email evaluation platform (CommTool), which enables communicators to get diverse message-level metrics such as reading time, relevance rate, comments, etc. We evaluated these designs through a 2-month field deployment with 5 communicators and 149 organization employees. We found that 1) the message-level metrics, such as reading time and relevance rate, helped communicators understand their audience and design bulk emails; 2) the cost and reputation metrics did not influence the organization leaders' decisions. We summarize with suggestions on designing organizational bulk email evaluation platforms that provide message-level performance and cost information.

XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection

XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

Influence of social media and the digital environment on international migration of health workforce from low- and middle-income countries post COVID-19 pandemic: a scoping review protocol

IntroductionMigration of the health workforce from low- and middle-income countries (LMCIs) is increasingly becoming a phenomenon of interest within migration governance systems. The COVID-19 pandemic aggravated health workforce shortages that...

Gender, Race, and Entrepreneurship: A Randomized Field Experiment on Venture Capitalists and Angels

We study gender and race in high-impact entrepreneurship using a tightly controlled randomized field experiment. We sent out 80,000 pitch emails introducing promising but fictitious start-ups to 28,000 venture capitalists and angels. Each email was sent by a fictitious entrepreneur with randomly assigned gender and race. Female entrepreneurs received 9% more interested replies than males pitching identical projects and Asians received 6% more than Whites. Our results suggest that investors do not discriminate against female or Asian entrepreneurs when evaluating unsolicited pitch emails and that future research on investor biases should focus on networks and in-person interactions. This paper was accepted by Tomasz Piskorski, finance. Funding: The grant is Social Sciences and Humanities Research Council of Canada Insight Development [Grant 430-2019-00306] “Gender and racial discrimination in venture capital” through the University of British Columbia. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4990 .

Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

Is orthodontic research falling prey to obscure and predatory journals? A bibliometric study.

To evaluate where orthodontic research papers are published and to explore potential relationships between the journal of publication and the characteristics of the research study and authorship. An online literature search of seven research databases was undertakento identify orthodontic articles published in English language over a 12-month period (1 January-31 December 2022) (last search: 12 June 2023). Data extractedincluded journal, article, and author characteristics. Journallegitimacy was assessed using a ternary classification scheme including available blacklists and whitelists, cross-checking of indexing claims and history of sending unsolicited emails. The level of evidence (LOE) of all included studies was assessed using a modified Oxford LOE classification scale. Univariable and multivariable ordinal logistic regression analyses were performed to examine possible associations between the level of evidence, journal discipline, and authorship characteristics. A total of 753 studies, published by 246 unique journal titles, were included and further assessed. Nearly two-thirds of orthodontic papers were published in non-orthodontic journals (62.8%) and over half (55.6%) of the articles were published in open-access policy journals. About a fifth of the articles (21.2%) were published either in presumed predatory journals or in journals of uncertain legitimacy. Journal discipline was significantly associated with the level of evidence. Higher-quality orthodontic studies were more likely published in established orthodontic journals (likelihood ratio test P < .001). The identification and classification of predatory journals are challenging due to their covert nature. The majority of orthodontic articles were published in non-orthodontic journals. In addition, approximately one in five orthodontic studies were published in presumed predatory journals or in journals of uncertain legitimacy. Studies with higher levels of evidence were more likely to be published in established orthodontic journals.

A review of black-box adversarial attacks and defenses in machine learning-based malware detection

In recent years, significant advancements have been made in cyber security, particularly through the application of machine learning (ML) methods. ML-based techniques have enhanced system security by effectively distinguishing between malicious and benign objects across various domains, including spam email detection, social media content filtering, intrusion detection systems, and malware detection. This paper focuses on the specific area of ML-based malware detection and its advantages over traditional methods, such as improved accuracy and the ability to generalize to unknown threats. Despite these advancements, ML-based malware detection systems are vulnerable to adversarial attacks, especially in black-box scenarios where the internal workings of the detection model are not accessible. This paper provides a comprehensive review of adversarial attacks on black-box malware detection systems and examines the current defense mechanisms against these attacks. Understanding the challenges and strategies in this field, this review aims to offer insights into enhancing the robustness and security of ML-based malware detection systems.

Classification Model of Spam Emails Based on Data Mining – Deep Learning Techniques

Classification Model of Spam Emails Based on Data Mining – Deep Learning Techniques

Exploring Machine Learning Algorithms for Email Spam Filtering

Spams are irrelevant or inappropriate emails, and they are on the rise across different digital communication channels globally. These unsolicited and unwelcome perilous emails lead to adverse impacts like security risks, time and resources wastage, financial loss, reputation damage, and legal consequences. Therefore, intelligent anti-spam email filters are required to prevent such emails from being delivered to the recipient's mailbox. Artificial Intelligence and Machine Learning algorithms provide trustworthy countermeasures and affordable solutions to tackle this global problem. Machine Learning techniques like Logistics Regression, Decision Tree, Support Vector Machines, K-nearest neighbor, Naïve Bayes Classifier, and Random Forest provide solutions to classify spam emails with reasonable accuracy and precision. This research explores different approaches to identifying spam emails and developing machine-learning algorithms to filter such emails automatically. Results manifest that the Support Vector Machine and Naïve Bayes Model most accurately classified spam emails. The Support Vector Machine exhibited the best balance between high precision and recall, making it highly effective in identifying spam messages with fewer false positives.

Improved machine learning technique for feature reduction and its application in spam email detection

Improved machine learning technique for feature reduction and its application in spam email detection

Amalgamating an intelligent variant of the gravitational search algorithm with decision trees for email spam detection

In today’s interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies.

MultiSURF: Optimal Feature Selection Technique for Spam Mail Detection and Classification

MultiSURF: Optimal Feature Selection Technique for Spam Mail Detection and Classification

Bank Emails: The Language of Legit and Scam

Amidst the pandemic, the surge in unsolicited scam emails has made internet technology a significant concern. Distinguishing between scams and authentic/legit emails has become increasingly difficult. This research aimed to identify typical characteristics and language patterns found in emails. It also aimed to identify the common linguistics and technical features of the scams and legitimate bank emails. Two legitimate emails and two scam or fraudulent emails from different banks in the Philippines (i.e., LandBank and BDO). A qualitative method was used to analyze and interpret the language features of the two emails. This led to the identification of shared characteristics found in scams and legitimate emails. A scam email typically includes embedded malicious links, misspellings, formatting issues, suspicious email domains, frequent use of contractions and redundancy, tautology, and imperative mood. On the other hand, legitimate emails exhibit proper punctuation, accurate email domains, appropriate formatting, absence of spelling mistakes, avoidance of contractions and redundancy, and use indicative mood rather than imperative. Scam emails utilize modal verbs to manipulate recipients into divulging personal information. Recognizing these email patterns is crucial for educating users and reducing phishing risks. It is important to note that the interpretation and findings of this study are based on the entire dataset collected. Keywords: Bank Emails, Legit Emails, Linguistic Features, Scam Emails, Phishing

A semantic-based model with a hybrid feature engineering process for accurate spam detection

Detecting spam emails is essential to maintaining the security and integrity of email communication. Existing research has made significant progress in developing effective spam detection models, but challenges remain in improving classification performance and adaptability to evolving spamming techniques. In this study, we propose a novel spam detection model with a comprehensive feature engineering approach that combines term frequency-inverse document frequency (TF-IDF) vectorizer and word embedding features to optimize the feature space. Our contribution lies in integrating semantic-based word embeddings, leveraging pre-existing knowledge to capture the semantic meaning of words and enhance the representation of email texts. To identify the most suitable word embedding technique for our model, we evaluated GloVe, Word2Vec, and FastText. GloVe was selected for its better performance, which is the result of its pre-training on a large and diverse text corpus. Furthermore, the model was evaluated without word embeddings, which did not exhibit the same effectiveness level as our word embedding-based model. Additionally, we utilized the support vector machine as a classifier and hyperparameter tuning technique to identify our model’s most effective parameter values. The proposed model was tested on two datasets. The experimental results showed that our model outperformed the other models discussed in the literature, achieving an accuracy of 99.5% on the SpamAssassin dataset, and 99.28% on the Enron-Spam dataset.

Unsolicited scam invitations from predatory publications and fraudulent conferences: Radiology-in-training experience

PurposeRadiology faculty across various specialties have been reported to receive an average of 20.7 invitations to submit manuscripts to bogus journals and 4.1 invitations to speak at unsuitable events over a two-week span. Radiology trainees also receive a fair number of unsolicited invitations from unknown senders to submit manuscripts and speak at meetings. Trainees can be more vulnerable to predatory invitations due to potential naivety. We aimed to determine the prevalence of these spam invitations received by radiology trainees. Material and methodsThe designed survey for evaluating the experience of radiology trainees regarding phishing scams of predatory publications and conferences was sent to radiology residency and neuroradiology fellowship program leadership to redistribute amongst their trainees, and was advertised on social media platforms. The survey was first sent out on September 28, 2023, and was closed two weeks later October 12, 2023. Spearman’s correlation, univariable and multivariable linear regression analyses were performed. ResultsOur study included 151 respondents who completed the survey. Of the survey respondents, 53 % reported receiving unsolicited emails from predatory publications (mean = 6.76 ± 7.29), and 32 % reported receiving emails from fraudulent conferences (mean = 5.61 ± 5.77). Significant positive correlation was observed between number of unsolicited email invitations with number of PubMed indexed publications, number as corresponding author, number in open access journals and number of abstract presentations. ConclusionsTrainees in radiology receive many unsolicited invitations to publish papers as well as to present at meetings that are not accredited. This could lead to wasted time and financial resources for unsuspecting trainees.

Deteksi Email Spam dengan Continuous Bag-Of-Words dan Random Forest

Spam email poses a significant cyber threat, as scammers employ various tactics to deceive individuals into divulging sensitive information or downloading harmful content. For instance, in June 2023, Indonesia encountered approximately 6.51 thousand spam attacks, underscoring the widespread nature of this issue. These attacks frequently involve deceptive strategies, such as impersonation or false promises of rewards, to ensnare unsuspecting victims. Succumbing to spam can result in financial losses and other grave repercussions. To address this concern, this research addresses this pressing problem by focusing on email content classification to detect phishing attempts. The proposed solution leverages runtime platforms such as Google Colab and uses Continuous Bag of Words (CBOW) analysis and Random Forest methods. CBOW is selected for its effectiveness in capturing semantic relationships between words, allowing the model to extract meaningful features from the email content. Random Forest, on the other hand, is chosen for its ability to handle imbalanced datasets commonly encountered in email classification tasks, ensuring fair representation of both spam and ham emails during model training. By combining these two techniques, we aim to develop a robust classification model capable of accurately distinguishing between phishing (spam) and legitimate (ham) emails, thus enhancing email security measures. Through our approach, we aim to classify the SpamAssassin dataset into ham or spam categories, with an anticipated precision rate of 0.98, demonstrating the model's effectiveness in accurately identifying phishing emails.