Spam Emails Research Articles

A Review Article On Enhancing Email Spam Filter’s Accuracy Using Machine Learning

In today’s era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.

Startups’ demand for accounting expertise: evidence from a randomized field experiment

AbstractWe conduct a randomized field experiment (RFE) to assess whether startup firms perceive accounting expertise as an important investor credential. We send 13,358 unsolicited and unique emails to active startup firms across the US, showing an interest in them with a proposition to meet a bogus investor. The experiment has high response rates, with 4,535 (33.94%) opened emails and 828 (6.19%) website visits, reflecting investors’ proliferating practice of outbound origination to contact new startups. Our RFE compares startup reactions to fictitious investors with certified public accountant (CPA) designations versus two control groups: investors without credentials and those with other professional licenses. Startup firms are 48% likelier to read unsolicited emails from CPA-bearing investors and 47% likelier to visit their websites, relative to investors with a medical license. We document an analogous preference for CPA-bearing investors even when we separately analyze startups in medical-related industries. This gap persists when investors pose as angels, venture capitalists (VCs), or without professional licenses. The relatively low percentage (2.5%) of email bounces and spam reports makes it unlikely that spam algorithms drive the findings. Further tests reveal that the response rates differ by firm age, which is inconsistent with spam filter explanations but congruent with startup firms’ demand for accounting expertise. Finally, we undertake a follow-up experiment with 3,443 new startups to distinguish between accounting and general business expertise using a master’s in business administration (MBA). Startups are 13.8% likelier to read emails from a CPA-bearing investor than from an MBA-credentialed investor and 22.6% more likely to visit the CPA-bearing investor’s website.

A Lightweight Machine Learning-Based Email Spam Detection Model Using Word Frequency Pattern

This Spam emails have become a severe challenge that irritates and consumes recipients' time. On the one hand, existing spam detection techniques have low detection rates and cannot tolerate high-dimensional data. Moreover, due to the machine learning algorithm's effectiveness in identifying mail as solicited or unsolicited, their approaches have become common in spam detection systems. This paper proposes a lightweight machine learning-based spam detection model based on Random Forest (RF) algorithm. According to the empirical results, the proposed model achieved a 97% accuracy on the spambase dataset. Furthermore, the performance of the proposed model was evaluated using standard classification metrics such as Fscore, Recall, Precision, and Accuracy. The comparison of Our model with state-of-the-art works investigated in this paper showed the model performs better, with an improvement of 6% for all metrics.

Spam Email Detection Using Machine Learning Techniques

This paper focuses on the security of electronic mail, using machine learning algorithms. Spam email is unwanted messages, usually commercial, sent to a large number of recipients. In this work, an algorithm for the detection of spam messages with the aid of machine learning methods is proposed. The algorithm accepts as input text email messages grouped as benevolent (“ham”) and malevolent (spam) and produces a text file in csv format. This file then is used to train a bunch of ten Machine Learning techniques to classify incoming emails into ham or spam. The following Machine Learning techniques have been tested: Support Vector Machines, k-Nearest Neighbour, Naïve Bayes, Neural Networks, Recurrent Neural Networks, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression and Decision Trees. Testing was performed using two popular datasets, as well as a publicly available csv file. Our algorithm is written in Python and produces satisfactory results in terms of accuracy, compared to state-of-the-art implementations. In addition, the proposed system generates three output files: a csv file with the spam email IP addresses (of originating email servers), a map with their geolocation, as well as a csv file with statistics about the countries of origin. These files can be used to update existing organisational filters and blacklists used in other spam filters.

ChatGPT Not Yet Ready for Clinical Practice

Back to table of contents Previous article Next article ViewpointsFull AccessChatGPT Not Yet Ready for Clinical PracticeDarlene King, M.D.Darlene KingSearch for more papers by this author, M.D.Published Online:16 Jun 2023https://doi.org/10.1176/appi.pn.2023.07.7.56AbstractChatGPT is the fastest growing app in the world, surpassing 1 million users five days after launch, according to DemandSage. The website has been visited over 1 billion times in the last month. It can write poetry, essays, summaries, and more. When it comes to technology, we want it to help more than harm, but sometimes new technologies are rapidly adopted, and it isn’t until later that we learn the ramifications. We see this with social media platforms that we now know are addictive and associated with anxiety, depression, and even physical ailments.Psychiatrists are already saying that ChatGPT has the potential to be a valuable tool in psychiatric practice and that students should use it to enhance clinical acumen and increase their skill at integrating technology into patient care. While I agree that ChatGPT is a revolutionary tool with remarkable potential, there are several important concerns to address before utilizing ChatGPT for psychiatric practice.First, let’s consider the sources and quality of information that ChatGPT uses to generate its answers. The large language models underlying ChatGPT are pre-trained with text—16% of the text comes from books and news articles, and 84% comes from webpages. The webpage data include high-quality text such as Wikipedia but also low-quality text like spam mail and Reddit, according to a paper by Wayne Xin Zhao and colleagues published in May in arXiv, a Cornell University publication. Knowing the source of information provides insight into not only the accuracy of the information but also what biases may exist. Bias can originate from data sets and become magnified through the machine learning development pipeline, leading to bias-related harms. For example, false news and racist viewpoints are often shared on Reddit. Another way to think of ChatGPT is as a user interface that packages internet information that existed before 2021. While these sources can be accurate at times, there are more reliable and high-quality resources for students to access. Taking excerpts from a reliable text and asking ChatGPT to summarize it may be a way around this limitation.Second, let’s consider privacy violations. The OpenAI privacy policy states that it collects all conversation history and that data collected trains the algorithm, which users may opt out of. Personal identifiers, commercial information, network activity information, geolocation data, and account login credentials may be disclosed to “affiliates, vendors and service providers, law enforcement, and parties involved in Transactions.” Given this privacy policy, it is ill advised to use ChatGPT to assist with patient documentation or the handling of patient information until safeguards for health care data are in place.Finally, it is difficult to say who is accountable for what and to whom under which circumstances. In computerized systems, “bugs” or accuracy ranges can divert accountability and chalk up an error to just falling within the margin of error. The problem with this is that sometimes the margin of error is a result of human-made decisions along the machine learning pipeline that could be better optimized, blurring accountability, according to a paper by A. Feder Cooper et al. published in arXiv in May. A lack of transparency in chosen models, data sources, and training methods further obscures the picture. Additionally, there is room for influencing decision-making when an algorithm decides which information to present or exclude.Ultimately, while this technology has amazing potential in psychiatry, these mainstream, large language models are not built with adequate sources, privacy protections, or robust enough models to prevent unforeseen harm. Regulation may help increase transparency, accuracy, accountability, and privacy. Striking a balance between innovation and regulation is needed if we want to enjoy this technology and limit the harm. When that day comes, I will sing its praises!An opposing viewpoint to this article is posted here. ■ResourcesChatGPT statistics for 2023“A Survey of Large Language Models”The OpenAI privacy policy“Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning”Darlene King, M.D., is an assistant professor in the Department of Psychiatry at UT Southwestern Medical Center, deputy medical information officer at Parkland Health, and the chair of APA’s Committee on Mental Health Information Technology. She graduated from the University of Texas at Austin with a degree in mechanical engineering prior to attending medical school and residency at UT Southwestern. ISSUES NewArchived

Detection of Cyberbullying Spam Mails

Detection of Cyberbullying Spam Mails

Spam/Ham email classification using BERT

Email is a popular method for communicating with each other. However, as sending email is free of charge as long as an email server and a domain name are available, spam mail is becoming a critical problem in the email network. Conventionally, the industry uses spam filters based on rules and Bayesian inference to counteract spam mail, reaching an accuracy of 98.76%, which is far from satisfactory. Hence, to better protect email users from unsolicited messages containing advertisements, sensitive content, phishing content, and viruses, a new approach is proposed, in which email content is filtered by a spam detector using bidirectional encoder representations from transformers (BERT). BERT is a new language representation model published by Google that has achieved great success because of its powerful capabilities in understanding natural language. After the model is trained on a corpus from Kaggle, the spam detector equipped with the BERT model reaches a binary accuracy of 99.40% when classifying spam mail.

Using Machine Learning to Protect Social Media Accounts from Spam Emails

Spam refers to all forms of bulk, unsolicited, unwelcome digital communication. Spam can be transmitted by text messages, phone calls, social media, or email, which is how it is frequently done. The rise of social media websites like Facebook, Twitter, YouTube, etc. has given spammers increased access to people. Most spam filtering solutions employ text-based algorithms. The fundamental difficulty is classification because it is a significant one. In this study, features are extracted from emails using classifications of rules. To automatically categorize emails, numerous systems have been created, a few of them include systemic decision-making. The use of Bayesian classifiers, networked neurons, support vector machines, and sample-based approaches. A sizable and time-consuming labelled dataset is required for the supervised model training phase. SVM and Naive Bayes, two supervised learning algorithms, outperform other models in terms of spam identification. It provides comprehensive explanations of these methods in addition to several likely areas for future research in spam filtering and detection. Because most spammers employ obfuscation techniques, the rules set need ongoing updating and improvement. Refinement is occasionally necessary, and in most situations, it is automated, so end users have less bother

String Kernel-Based Techniques for Native Language Identification

In recent years, Native Language Identification (NLI) has shown significant interest in computational linguistics. NLI uses an author’s speech or writing in a second language to figure out their native language. This may find applications in forensic linguistics, language teaching, second language acquisition, authorship attribution, identification of spam emails or phishing websites, etc. Conventional pairwise string comparison techniques are computationally expensive and time-consuming. This paper presents fast NLI techniques based on string kernels such as spectrum, presence bits, and intersection string kernels incorporating different learners such as a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting-XGBoost (XGB). Feature sets for the proposed techniques are generated using different combinations of features such as n-word grams and noun phrases. Experimental analyses are carried out using 8235 English as a second language articles from 10 different linguistic backgrounds from a typical NLP benchmark dataset. The experimental results show that the proposed NLI technique incorporating a spectrum string kernel with an RF classifier outperformed existing character n-gram string kernels incorporating SVM, RF, and XGB classifiers. Also, comparable results were observed among different combinations of string kernels. Interestingly, the random forest classifier outperformed SVM and XGB classifiers with different feature sets. All the proposed NLI techniques demonstrated promising results with significant improvement in training time, with the best result attaining more than a 95 percent decrease in training time. The reduced training time of proposed techniques makes it well suited to scale NLI applications for production.

Comparison of spam classification methods based on machine learning

Wapid development of the Internet, people's use of mail continues to expand, anti-spam has become a top priority. According to the statistics of relevant departments, in 2006, the total amount of spam mails received by netizens was 50 billion, which caused an economic loss of about 10.431.5 billion yuan to the national economy. In 2007, netizens received 69.4 billion pieces of junk mail, with a loss of 18.84 billion yuan. The growth rate was 38.8 percent. Spam is the main culprit that consumes network resources. Of course, preventing spam is a long way to go. Among the types of spam received by users, the top three are online shopping spam, online money-making spam and sex toys spam, which account for 17.57%, 12.55% and 9.21% respectively. Followed by attack spam, spam containing viruses, etc. At present, anti - spam main technology and method means.

Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm

Phishing emails called spam have created a need for reliable and intelligent spam filters. Machine-learning techniques are effective, but current methods such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) sometimes produce low detection rates and struggle with large amounts of data. Motivated by these concerns, we propose an efficient spam-filtering approach, OAOS-LR, combining an improved Atomic Orbital Search (AOS) algorithm with an LR classification model. To remove the deficiency of low detection rate produced by the standard LR method due to the utilization of the gradient descent technique, we train it with our proposed OAOS approach, which uses AOS and Orthogonal learning to enhance the search capabilities of the conventional algorithm. In the experimental study, we first evaluated the performance of OAOS over the IEEE Congress on Evolutionary Computation (CEC’20) benchmarks against five different metaheuristics to prove its effectiveness in improving its convergence rate and reducing the probability of falling in local optima. After that, the proposed technique LR-OAOS was applied to the spam filtering problem using CSDMC2010 and Enron datasets and tested against the most recent machine learning and metaheuristic approaches using standard statistical measures and plots. OAOS-LR significantly outperformed other methods with an average F1-score success rate of 95.45% and 96.30% on CSDMC2010, and 74.80% and 78.33% on Enron, respectively with the number of feature spaces equal to 500 and 1000.

SPAM EMAIL DETECTION USING DIFFERENT CLASSIFIERS

Due to the widespread use of social media, the amount of unwanted emails has increased, necessitating the implementation of a reliable system to filter them out. Spam emailsare now the most common issue on the internet, despite email beingone of the fastest and most cost-effective forms of communication. Over the past few years, there has been a significant increase in spam emails due to the growing number of email subscribers. This study employs four classifiers (Random Forest, XG Boost, Naïve Bayesian) to classify email data, with varying data and feature sizes. The final classification result is '1' if the email is spam and '0' if it is not. The study was conducted using Python and implemented in a Jupyter notebook. This technique can also be utilized in monitoring social media and brand activity. The paper presents a machine learning approach for identifying spam emails by detecting spam content within the message. With machine learning, computers can learn how to perform a task without being explicitly programmed. This method uses data to generate a program that performs a task, such as classification. Unlike knowledge engineering, machine learning techniques require pre-classified data to create a training dataset that is used to fit the learning algorithm in the machine learning studio. The objective of this study is to train, test, and compare various classifiers. The rest of the paper is organized as follows: Section 2 defines the researchers' contribution in this field. Section 3 describes the experimentation framework, dataset, procedures, andlibraries. Section 4 summarizes the findings, and Section 5 concludes the paper's potential for future research.

Text classification algorithms exploration on sentiment analysis

Internet provides us with an abundance of useful tools and data. However, it also generates a vast quantity of data that may bewilder us. There must be a technique for automatically processing these data. Here, text classification becomes useful. Text classification is the algorithm-based process of categorizing data inputs into distinct labels. For instance, email software utilizes it to assess if an email should be filtered into the spam folder, social media forums use it to classify postings into labels that are relevant to the topic, etc. Text categorization is utilized in a variety of businesses, including search engines, sentiment analysis, emergency response systems, chatbots, etc. Review websites have emerged in recent years where customers may share their opinions on a business or a product. The review is extremely emotive but crucial to the company. It is possible to accurately assess the reviews for the sentiment they present through text classification. This paper compares the efficacy of various text classification algorithms for sentiment analysis.

Machine learning algorithms review

Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.

Spam mail classification using back propagation neural networks

Mail classification methods based on machine learning have been introduced to combat spams. However, few researches focus on the most powerful machine learning model that is neural networks. In this paper, the author trains BP neural networks to detect spams. The inputs of the neural networks are only information about words, punctures, signs, numbers and illegal words. Five neural networks which are different in number of neurons and number of layers are experimented on. All networks apply Rectified Linear Unit (ReLU) functions and Momentum learning technology. The results show that the network with four hidden layers enjoys the best classifying accuracy of 97.0%. In networks with two hidden layers, when the number of neurons in each layer is above 300, the accuracy is between 95.5% and 96.0%; and 100 neurons in each layer result in an accuracy of 93.8%. Although the training only captures information of words, punctures and signs, the networks have achieved high accuracy, and the author suggests that making the computer understand sentences as well as other kinds of improvements can lead to even higher performance.

FineTuning-based BERT for Spam Emails Identification

In this world of information explosion, people require more effective ways to filter useful information from millions of data. Email as one of the most frequently used form of communication, carries important messages, yet along with messages of fake news, misinformation and scams known as spam emails. Manually categorizing them from non-spam emails requires a lot of time and money and other human along with material resources. In order to deal with this, deep learning, or natural language processing models in particular, is introduced to categorize emails faster and cheaper. The Natural Language Processing model used here is called Bidirectional Encoder Representations from Transformers (BERT). Since BERT is already a pre-trained model, the main task is to do the Fine-Tune part on it, with a dataset that contains around 5000 emails (85% spam emails and 15% non-spam ones). After that the model is tested on a group of 5 emails including 3 commercials/spams and 2 non-spam emails. The result shows that this model could separate them by giving commercials scores closer to 1 (spread from 0.5 to 0.7) and non-spam emails scores close to 1(spread from 0 to 0.1). Therefore, it can be concluded that this model works on small sets of data.

E-Mail Spam Detection by using NLP and Naïve Bayes Classification Through Machine Learning

E-Mail Spam Detection by using NLP and Naïve Bayes Classification Through Machine Learning

Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering

Spammers have created a new kind of electronic mail (e-mail) called image-based spam to bypass text-based spam filters. Unfortunately, these images contain harmful links that can infect the user’s computer system and take a long time to be deleted, which can hamper users’ productivity and security. In this paper, a hybrid deep neural network architecture is suggested to address this problem. It is based on the convolution neural network (CNN), which has been enhanced with the convolutional block attention module (CBAM). Initially, CNN enhanced with CBAM is used to extract the most crucial information from each image-based e-mail. Then, the generated feature vectors are fed to the support vector machine (SVM) model to classify them as either spam or ham. Four datasets—including Image Spam Hunter (ISH), Annadatha, Chavda Approach 1, and Chavda Approach 2—are used in the experiments. The obtained results demonstrated that in terms of accuracy, our model exceeds the existing state-of-the-art methods.

LBP Özellik Çıkarma ve İstatistiksel Havuzlama Tabanlı Görüntü Spam Tespit Modeli

Email, which stands for electronic mail, is a form of digital communication between two or more individuals. These technological instruments that facilitate communication can have a positive and negative impact on our lives due to junk e-mails, widely known as spam mail. These spam messages, which are typically delivered for commercial purposes by organizations/individuals for indirect or direct benefits, not only distract people but also consume a significant amount of system resources such as processing power, memory, and network bandwidth. In this study, a method based on LBP (Local Binary Patterns) feature extraction and statistical pooling is proposed to classify spam or raw (non-spam) images. Two datasets are used to test the proposed method. The ISH dataset is widely used in the literature and contains 1738 images. In addition to this dataset, the dataset our collect consists of 1015 images in total. Feature extraction was performed on these images. Obtained features were classified by SVM (Support Vector Machine) algorithm. In the proposed method, 98.56% and 79.01% accuracy were calculated for the ISH dataset and our collected dataset, respectively. The results obtained were compared with the studies in the literature.

Email Spam Detection Using Supervised Algorithms

Email Spam Detection Using Supervised Algorithms