Spam Emails Research Articles

SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES

SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning methods have been used to identify SMS spam. Using data from the University of California, Irvine (UCI), this research examines the application of such strategies. Key Words: MultinomailNaiveBayes, SMS Spam Detection, NLTK, Vectorization, Feature Extraction.

Efficient Email Spam Classification with N-gram Features and Ensemble Learning

In this paper, we present an innovative approach to enhancing email spam classification using N-gram features, TF-IDF weighting, SMOTE oversampling, and ensemble learning techniques such as Decision Trees, Random Forests, and Ensemble Extra Trees. Our methodology involves preprocessing the dataset to extract N-gram features, applying TF-IDF weighting to highlight important terms, and addressing class imbalance through SMOTE. We then train and evaluate multiple classification models and find that the Ensemble Extra Trees algorithm outperforms others in terms of accuracy, precision, recall, and F1-score. Our experiments on benchmark datasets confirm the efficacy of our approach, showcasing significant improvements in spam detection accuracy and highlighting the potential of ensemble learning for email spam classification. This research contributes to the advancement of spam filtering technologies, providing a robust and efficient solution for accurately identifying and categorizing spam emails.

A Comprehensive Study on Email Spam Detection Techniques

The analysis of email spam detection methods is pivotal in the realm of cybersecurity. This studymeticulously explores a variety of techniques utilized for discerning spam emails, encompassing traditionalmethodologies alongside sophisticated approaches such as machine learning algorithms. The examined techniquesinclude Support Vector Machines, Naive Bayes Multinomial Classifier and Random Forest Classifier with a primaryobjective of identifying the most effective approach among them. By scrutinizing each method's strengths andweaknesses, encompassing factors like detection accuracy and computational efficiency, this research seeks to offer acomprehensive overview of their utility in combatting email spam. Additionally, the study investigates the impact ofhuman elements, such as user education and awareness initiatives, in bolstering overall detection performance. Byamalgamating insights from various scholarly endeavors, this inquiry not only enriches the understanding ofcontemporary spam detection practices but also delineates pathways for future research endeavors and informs theformulation of resilient cybersecurity protocols.

Spam email classification based on SVM, Transformer and Naive Bayes

As a matter of fact, with the booming of information and big data, there are too many unwanted e-mails called spam sent to peoples e-mail account in recent years. On this basis, it could lead to a lot of problems including occupying the public resources, causing financial loss and so on. With this in mind, spam filtering technique is in need to solve the problem and address the issues. In reality, based on previous analysis, machine learning methods are very effective in spam filtering. On this basis, this study carried out background research of machine learning algorithms in spam filtering, and find the spam e-mail dataset of Kaggle.com, and implement 3 algorithms on the dataset. According to the analysis, Transformer and SVM work better on the dataset, and SVM is the best. At the same time, the current limitations are discussed as well. In addition, the prospects are demonstrated in the meantime.

Hybrid Classifier for Enhancing Accuracy and Performance of Spam and Ham Email Detection

Hybrid Classifier for Enhancing Accuracy and Performance of Spam and Ham Email Detection

An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification.The recently introduced Sand Cat Swarm Optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security.

Application and challenges of artificial intelligence in cybersecurity

This paper delves into the multi-faceted role of Artificial Intelligence (AI) in the field of cybersecurity. It conducts an in-depth analysis of two common security problems, spam email and DDos/Dos attacks. It focuses on the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS). Furthermore, this article outlines and discusses five key challenges that AI may encounter throughout its development, including issues related to reliability, unidentified threats, data privacy, ethical considerations, and the importance of an explainable AI. The paper underscores the remarkable potential of AI to improve our defences against emerging cybersecurity threats. It highlights the growing importance of addressing ethical and legal issues in the evolving technological environment. Finally, the article offers a comprehensive summary and valuable insights into the future prospects of AI in the realm of cybersecurity, confirming its crucial role in shaping the security landscape in the digital era.

An Analysis of Solicitations From Predatory Journals in Ophthalmology

PurposeTo evaluate trends associated with email communication from potentially predatory publishers to faculty in ophthalmology. DesignCross sectional study MethodsOphthalmologists (n=14) from various subspecialties and institutions were recruited to participate. Participants identified unsolicited emails they had received originating from publishers in May 2021. Information collected included details on email contents and publisher organizations. Trends in communications from predatory publishers were evaluated. ResultsOver a 30-day study period, a total of 1813 emails were received from 383 unique publishers and 696 unique journals with a mean (SD) of 4.73 (2.46) emails received per day per participant. Of the 1813 emails identified, 242 (13%) emails were invitations to conferences, whereas 1440 (80%) were solicitations for article submissions to open-access pay-to-publish journals. A total of 522 (29.0%) emails were related to ophthalmology, and reference to a prior publication of the participant occurred in 262 emails (14%). Of the 696 unique journals identified, 174 (25%) journals were indexed on PubMed and 426 (61%) were listed on Beall's list. When comparing journals listed on PubMed versus those that were not, PubMed indexed journals had a higher impact factor (2.1 vs 1.5, p=0.002), were less likely to use “greetings” (76% vs 91%, p<0.001), had fewer spelling/grammar errors (40% vs 51%, p=0.01), and were less likely to offer rapid publication (16% vs 25%, p=0.02). ConclusionUnsolicited requests to publish occur frequently and may diminish the quality of the scientific literature. We encourage individuals in ophthalmology to be aware of these trends in predatory publishing.

Use of Artificial Intelligence on Cyber Security and the New-generation Cyber-attacks

Cybersecurity is a fast-growing and evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Not only have there been a lot more cyberattacks in recent years, but they have also gotten much more advanced. Therefore, developing a cyber-resilient strategy is most significant. In the event of a cyberattack, traditional security measures are insufficient to prevent data leaks. Cybercriminals have mastered the use of cutting-edge methods and powerful tools for data intrusion, hacking, and assault. In this, we are proposing applications of artificial intelligence (AI) technology in the creation of intelligent models for securing systems against attackers. AI technologies can quickly advance to meet complicated problems, making them useful as fundamental cybersecurity tools to identify malware attacks, AI-based systems can provide efficient and robust cyber security against phishing and spam emails, network intrusions, and data breach capabilities and alert the security during the impact. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review

In this paper, we presented a review of the state-of-the-art hybrid machine learning algorithms that were being used for email effective computing. For this reason, three research questions were formed, and the questions were answered by studying and analyzing related papers collected from some well-established scientific databases (Springer Link, IEEE Explore, Web of Science, and Scopus) based on some exclusion and inclusion criteria. The result presented the common Hybrid ML algorithms used to enhance email spam filtering. Also, the state-of-the-art datasets used for email and malware spam filtering were presented.

Spam analysis and classification of the dynamic message using a vectorizing technique with multi-model machine learning algorithm

Spam analysis and classification of dynamic messages is an essential task in order to combat the ever-increasing volume of unsolicited and malicious emails. One effective approach is to employ a vectorizing technique along with a multi-model machine learning algorithm. This approach involves representing email messages as high-dimensional vectors, capturing various features such as word frequencies, presence of specific keywords, and structural characteristics. By transforming the text into numerical representations, the machine learning algorithm can then learn patterns and make predictions based on these representations. The use of a multi- model algorithm allows for the integration of different classification models, each with its own strengths and weaknesses, to enhance the overall performance. This approach can achieve high accuracy by leveraging diverse learning methods and combining their predictions. Furthermore, the approach is dynamic in nature, meaning that it can adapt to new forms of spam and evolving attack strategies. The key challenge lies in selecting appropriate features and tuning the parameters of the algorithm to ensure optimal performance. The results of this study can contribute to the development of more effective and efficient spam detection systems, helping users to filter out unwanted and potentially harmful messages. Keywords: spam analysis, dynamic messages, classification, vectorizing techniques, multi-model machine learning algorithm, email filtering, unsolicited emails, malicious emails, feature extraction, pattern recognition.

Efficient genetic algorithm for spam mail detection and classification

E-mail is the quickest tool to convey information from one to others. Individual users and organizations have become more reliant on e-mails as technology advances. At the moment, all email inboxes are swamped with spam and these cause significant and diverse difficulties, such as the loss of crucial information and the theft of the recipient’s identity. Organizations may suffer significant losses. As a result, users cannot avoid spam emails, which come in various formats such as advertisements and messages. Spam filtering removes spam messages and prevents them from being accessed. This paper focuses on categorization of e-mails by using genetic algorithm. The proposed approach uses entropy to evaluate information gain, which is subsequently employed by the genetic algorithm classifier to choose important features from the spam database. The model performance is then assessed by using the testing dataset with all 57 features, a crossover probability of 0.3, and a mutation probability of 1.0 and obtained good accuracy.

An intelligent multi-layer framework with SHAP integration for botnet detection and classification

In increasingly interconnected digital world, the threat of cyber-attacks and data breaches are a pervasive and growing concern. The Botnets are the most dangerous threats that launch a wide range of cyberattacks such as distributed denial of service (DDoS) attacks, sending spam emails, spreading malware, and stealing sensitive information. The identification and categorization of botnets has become a highly complex and critical process due to the massive amount of network traffic generated every second. This paper presents a multilayer botnet detection and classification framework based on explainable machine learning algorithms. The proposed approach comprises of three distinct layers: Feature Selection Layer, Botnet Detection Layer and Botnet Classification Layer. The Feature Selection Layer reduces the dataset into six essential features, that enhance the accuracy and efficiency of the framework. The botnet detection layer detects botnet activity by filtering the reduced dataset into normal packets and botnet packets. The filtered botnet packets further examined by botnet classification layer to classify the botnet packets into different types of botnets. Moreover, SHAP (SHapley Additive exPlanations) technique is utilized to provide transparency to the model's decision-making process. To evaluate the effectiveness of the proposed model, NCC-2 and CTU-13 datasets are examined. 10-fold cross-validation technique is applied to validate the experimental findings. The average accuracy achieved by proposed approach in botnet detection stands impressively at 99.98%, while the average accuracy achieved in classifying botnet families is 99.30%, exhibiting an exceptional level of performance. The comparative analysis is performed which demonstrates its superiority over existing methods in terms of accuracy, precision, recall, F1-Score.

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device’s activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48.

Random Forest Machine Learning for Spam Email Classification

This research discusses the crucial role of email as a main element in digital communication, facilitating information transfer and serving as an advertising platform. However, the problem of email spam, which involves sending unsolicited commercial messages, has had negative impacts such as consuming large amounts of resources and disrupting user experience. With its affordable cost and ease of sending messages to thousands of recipients, email spam includes product promotions, pornographic material, viruses and irrelevant content. The impact includes loss of time and damage to the user's computer resources. To address this problem, email services provide advanced spam filters that use email content analysis and machine learning techniques. This research focuses on the use of the Random Forest Classification algorithm as a basis for filtering spam emails. Although Random Forest is known to have strong classification capabilities, the risk of overfitting is a challenge. Therefore, this study adopts the Randomized Search CV method to identify the best parameter combination, ensuring the reliability of the model in dealing with the complexity of diverse email datasets. With this approach, this research contributes to the development of effective solutions to reduce the impact of email spam in digital communications.

Email Spam Detection in the Age of Cyber Threats: a Comprehensive Approach Integrating Cleaning, Analysis, and Modeling

With the escalating sophistication of cyber threats, the imperative task of safeguarding email communication from spam has become increasingly challenging. This research presents a holistic approach to email spam detection, incorporating meticulous data cleaning, in-depth exploratory data analysis (EDA), and advanced modeling techniques. The study delves into the performance evaluation of diverse machine learning classifiers, including Gaussian Naive Bayes, Multinomial Naive Bayes, and Bernoulli Naive Bayes. Exploratory visualizations, such as word clouds and bar plots, contribute to a comprehensive understanding of the textual content in spam and ham messages.

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.

Email spam detection: a comparison of svm and naive bayes using bayesian optimization and grid search parameters

Spam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89.

Filtering and Detection of Real-Time Spam Mail Based on a Bayesian Approach in University Networks

With the advent of digital technologies as an integral part of today’s everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails.

Can ChatGPT identify predatory biomedical and dental journals? A cross-sectional content analysis

ObjectivesTo assess whether ChatGPT can help to identify predatory biomedical and dental journals, analyze the content of its responses and compare the frequency of positive and negative indicators provided by ChatGPT concerning predatory and legitimate journals. MethodsFour-hundred predatory and legitimate biomedical and dental journals were selected from four sources: Beall's list, unsolicited emails, the Web of Science (WOS) journal list and the Directory of Open Access Journals (DOAJ). ChatGPT was asked to determine journal legitimacy. Journals were classified into legitimate or predatory. Pearson's Chi-squared test and logistic regression were conducted. Two machine learning algorithms determined the most influential criteria on the correct classification of journals. ResultsThe data were categorized under 10 criteria with the most frequently coded criteria being the transparency of processes and policies. ChatGPT correctly classified predatory and legitimate journals in 92.5 % and 71 % of the sample, respectively. The accuracy of ChatGPT responses was 0.82. ChatGPT also demonstrated a high level of sensitivity (0.93). Additionally, the model exhibited a specificity of 0.71, accurately identifying true negatives. A highly significant association between ChatGPT verdicts and the classification based on known sources was observed (P <0.001). ChatGPT was 30.2 times more likely to correctly classify a predatory journal (95 % confidence interval: 16.9–57.43, p-value: <0.001). ConclusionsChatGPT can accurately distinguish predatory and legitimate journals with a high level of accuracy. While some false positive (29 %) and false negative (7.5 %) results were observed, it may be reasonable to harness ChatGPT to assist with the identification of predatory journals. Clinical significance statementChatGPT may effectively distinguish between predatory and legitimate journals, with accuracy rates of 92.5 % and 71 %, respectively. The potential utility of large-scale language models in exposing predatory publications is worthy of further consideration.