Spam Emails Research Articles

Email Spam Detection using Naïve Bayes Algorithm

Abstract: Email Spam has become a vital issue currently, with high-speed growth of internet users. Some people are using them for illegal conducts, phishing and fraud. Sending malicious link through spam emails which can harm our system and may also they will seek into our system. The need of email spam detection is to prevent spam messages from lagging into user’s inbox so it’ll improve user experience. This project will identify those spam emails by using machine learning approach. Machine learning is one amongst the applications of Artificial Intelligence that allow systems to read and improve from experience without being specific programmed. This paper will discuss the machine learning algorithm which is Naïve Bayes. It is a probabilistic classifier, which means it predicts on the idea of the probability of an object and it is selected for the email spam detection having best precision and accuracy.

Trigonometric words ranking model for spam message classification

AbstractThe significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when the word order changes. In this paper, a new framework is proposed for anti‐spam filtering that does not depend on the word's position in the message, called the Trigonometric Words Ranking Model (TWRM). The proposed TWRM is based on restricting spammers over the network by measuring a theta angle, which is a relationship between message weight and spam. TWRM classifies messages by calculating the rank of each word that places the corresponding message in the correct class. The rank of words is derived from their frequency in the entire data category. The proposed method is applied to three datasets of spam messages: UCI spam email, Enron spam, and TREC spam data. The proposed model is proven as more efficient than the Minhash and vector space models. Moreover, the TWRM performance provided better retrieval time and defence, which is reflected in the accuracy of (99.64%), which is higher than that of Minhash (88.79%) and vector space (92.59%).

A Review of Artificial Intelligence Applications in Machine Learning in Mordren World

The newest and most fascinating technique in artificial intelligence is machine learning. Many of the applications we use employ everyday learning techniques. Because Google or Microsoft have incorporated a learning mechanism by which online pages are ranked, they operate effectively as search engines. This learning also recognizes the photographs of pals while browsing Facebook. The learning system also aids in recognizing spam emails, saving the user the hassle of having to waste time sorting through the volume of spam emails. Therefore, this study has explored how machine learning functions as well as how it might be useful in the future.

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique.

e-mail service providers and consumers find it challenging to distinguish between spam and nonspam e-mails. The purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public. Various spam identification techniques have been suggested and evaluated in the past, but the results show that the more research in this regard is required to enhance accuracy and to reduce training time and error rate. Thus, this research proposes a novel machine learning-based hybrid bagging method for e-mail spam identification by combining two machine learning methods: random forest and J48 (decision tree). The proposed framework categorizes the e-mail into ham and spam. The database is split into multiple sets and provided as input to each method in this procedure. Moreover, tokenization, stemming, and stop word removal are performed in the preprocessing stage. Further, correlation feature selection (CFS) is employed in this research to select the required features from the preprocessed data. The effectiveness of the presented method is evaluated in terms of true-negative rates, accuracy, recall, precision, false-positive rate, f-measure, and false-negative rate; the outcomes of three studies are compared. According to the results, the presented hybrid bagged model-based SMD technology achieved 98 percent accuracy.

Using Support Vector Machine for Classification and Feature Extraction of Spam in Email

We provide an overview of recent and successful content-based e-mail spam filtering algorithms in this article. Our main focus is on spam filters based on machine learning and variants influenced by them. We report on significant ideas, methodologies, key endeavors, and the field's current state-of-the-art. The initial interpretation of previous work demonstrates the fundamentals of spam filtering and feature engineering in e-mail. We finish by looking at approaches, procedures, and evaluation standards, as well as exploring intriguing offshoots of recent breakthroughs and proposing directions of future research.

Performance Evaluation of Machine Learning Algorithms on Textual Datasets for Spam Email Classification

Abstract: Email is one of the most popular modes of communication we have today. Billions of emails are sent every day in our world but not every one of them is relevant or of importance. The irrelevant and unwanted emails are termed email spam. These spam emails are sent with many different targets that range from advertisement to data theft. Filtering these spam emails is very essential in order to keep the email space fluent in its functioning. Machine Learning algorithms are being extensively used in the classification of spam emails. This paper showcases the performance evaluation of some selected supervised Machine Learning algorithms namely Naive Bayes Classifier, Support Vector Machine, Random Forest, & XG-Boost for spam email classification on a combination of three different datasets. For feature extraction, both Bag of Words & TF-IDF models were used separately and performance with both of these approaches was also compared. The results showed that SVM performed better than all the other algorithms when trained with TF-IDF feature vectors. The performance metrics used were accuracy, precision, recall, and f1-score, along with the ROC curve.

Sistem Deteksi Surel SPAM Dengan DNSBL Dan Support Vector Machine Pada Penyedia Layanan Mail Marketing

Mail marketing is an effective communication medium for users and internet providers. Many companies use email as a mean of communication with customers to ensure customers are not left behind with the latest information, and at once provide personalized offers to specific customers. However, not all emails that are sent reach mail inbox as expected. There are several factors as the cause including content that does not comply with the writing rules and tends to have SPAM signatures, invalid e-mail addresses, the sender domains are registered in the blacklist and so forth. Mail marketing service providers such as MTarget and Mailchimp must ensure that emails sent by their customers have no potential to become spam, because it can affect all of their mail marketing services will be blacklisted, thus promotional goals will not be achieved. In that case, a system is needed to check the e-mail that will be sent by the customer, to ensure that the e-mail will not detected as a spam by email service applications such as Gmail. This research produces an email validator system that can prevent sending emails that have the potential to become SPAM, so as to reduce the risk of a mail marketing service provider being blacklisted which results in delays in promotion via email and a decrease in marketing turnover. The proposed method used in this research is the Domain Name System-Based Blackhole List (DNSBL) to check the IP and the sending domain and the Support Vector Machine (SVM) to check the content of the email to be sent. The system developed has been functioning as expected and has an accuracy rate of 97.54% in detecting SPAM emails.

Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails

Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches.

Deteksi Malware Dridex Menggunakan Signature-based Snort

Currently malware is a dangerous application and continues to grow so that it becomes a threat when using internet services. One of the most dangerous malware in 2020 is Dridex which targets and steals banking credentials and personal information regarding a person's financial records. Dridex makes use of email spam and social engineering for its distribution. It is noted that this malware has made a loss of up to $100 million. This study focuses on analyzing Dridex activity through a network traffic dataset and then developing snort rules based on the Dridex signatures that have been found. This study has developed 12 (twelve) rules that are implemented on Snort to detect the presence of Dridex signatures. Testing the success of Dridex detection was carried out using confusion matrix techniques and resulted in an accuracy value of 88.5%, a recall or decision rate of 100%, and a precision value of 84.75%.

Natural language processing: state of the art, current trends and challenges.

Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.

A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

The increasing volume of unsolicited bulk emails has become a major threat to global security. While a significant amount of research has been carried out in terms of proposing new and better algorithms for email spam detection, relatively less attention has been given to evaluation metrics. Some widely used metrics include accuracy, recall, precision, and F-score. This paper proposes a new evaluation metric based on the concepts of fuzzy logic. The proposed metric, termed μO, combines accuracy, recall, and precision into a multi-criteria fuzzy function. Several possible evaluation rules are proposed. As proof of concept, a preliminary empirical analysis of the proposed scheme is carried out using two models, namely BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long short-term memory) from the domain of deep learning, while utilizing three benchmark datasets. Results indicate that for the Enron and PU datasets, LSTM produces better results of μO, with the values in the range of 0.88 to 0.96, whereas BERT generates better values of μO in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric.

Phishing: A Way of Attacking the Privacy

The Internet provides a fantastic platform for ordinary people to communicate. Criminal minds have figured out a means to steal personal information without having to meet the person and with the least danger of getting detected. It’s known as phishing. Phishing is a significant threat to the ecommerce sector. Customers’ trust in ecommerce is shattered, and electronic service providers suffer significant financial losses as a result. As a result, understanding phishing is critical. This document educates readers on phishing assaults and anti-phishing the recipient, the auction site will have a good yield. Anti-phishing strategies and online security rules have been recommended by both academia and industry practitioners in order to protect customers’ interests. Some commercial anti-spam and anti-phishing products block email from ”blacklisted” sites that they claim deliver spam and phishing emails, but allow email from software. Because of the obvious usability issues ”whitelisted” sites that they claim are known not to send it. This strategy appears to be anticompetitive because it unfairly discriminates against smaller and lesser-known sites.

Spam Email Classification by Hybrid Feature Selection with Advanced Machine learning Algorithm – Future Perspective

Recently, email has become a common way for people to communicate and share information both officially and personally. Email may be used by spammers to transmit harmful materials to Internet users. The data must be protected from unauthorized access, which necessitates the development of a reliable method for identifying spam emails. As a result, a variety of solutions have been devised. An innovative hybrid machine learning strategy for effectively detecting spam emails has been discussed in this study. This means that identifying spam and non-spam email is a difficult process. Spam email categorization has undergone a significant evolution in recent years, as shown by the research given below. For locating spam, this study uses a mixed approach. Different email categorization algorithms are used to rank them for future perspective.

Embedding Reflective Practice in a Feedback Focused Assessment Design in a Master of TESOL Program

This study reports findings about an assessment design, with embedded reflective practices tied to feedback, and its capacity to enable conceptual change for professional learning in a Master of TESOL in Australia. The assessment design was initially developed to respond to the lack of supervised field experience. The majority of students in the course were future teachers of EFL in Asia. The study used Gunstone and Northfield’s (1994/2007) phases in conceptual change in combination with Farrell’s (2018) stages of reflection and assessment as its theoretical framing. Data gathered from past and current students included interviews, unsolicited student emails and posts, and extracts from a final assessment task. Findings suggest that the embedding of reflective practices develops reflection for professional learning beyond the course. However, the assessment tasks need to be designed with a specific set of design features for real conceptual change to occur. They need to: 1) be centrally focused on interaction; 2) recognize the need for students to gain from the diverse experiences and backgrounds of peers through collaborative, real world assessment tasks that also develop their professional English language; 3) explicitly model and make reflection a criterion for assessment; 4) permit professional and personal application of learning beyond the course through optional opportunities such as presenting at professional conferences so that collegial mentoring can continue; and 5) allow students to develop a (digital) professional portfolio in which they collate the completed assessment tasks and activities to present at interviews.

DETECTING SPAM EMAIL WITH MACHINE LEARNING OPTIMIZED WITH BIO-INSPIRED METAHEURISTIC ALGORITHMS

Groups and individuals used email regularly, for sending and receiving messages. When spammers get to know any valid email address, then they can exploit it easily by sending unsolicited emails, which will directly reach our mail inbox. Detecting spam using the machine learning technique is a known way, but we want to improve the accuracy, so the ML algorithms were optimized with bio-inspired methods to achieve better accuracy. The research was done to implement ML models using many algorithms, such as Naive Bayes, Random Forest, Support Vector Machine, and Decision Tree on a particular email dataset, along with feature extraction and pre-processing. The techniques occurring in nature can be used to achieve better accuracy, viz. Genetic Algorithm and Particle Swarm Optimization.

Nanoscale: Society's Deep Impact on Science, Technology and Innovation in India by Pankaj Sekhsaria, and: Instrumental Lives: An Intimate Biography of an Indian Laboratory by Pankaj Sekhsaria (review).

Reviewed by: Nanoscale: Society's Deep Impact on Science, Technology and Innovation in India by Pankaj Sekhsaria, and: Instrumental Lives: An Intimate Biography of an Indian Laboratory by Pankaj Sekhsaria Hyungsub Choi (bio) Nanoscale: Society's Deep Impact on Science, Technology and Innovation in India By Pankaj Sekhsaria. Greater Noida: AuthorsUpFront, 2020. Pp. 182 Instrumental Lives: An Intimate Biography of an Indian Laboratory By Pankaj Sekhsaria. Oxon: Routledge, 2019. Pp. 126. As faculty member of a technical university in South Korea, I frequently receive unsolicited emails from newly minted Indian Ph.D.s in science and engineering. The standardized messages invariably come with CVs attached. Typically, the scientists possess degrees from respectable institutions in India, complete with peer-reviewed publications in indexed periodicals. Some successfully secure postdoctoral positions in South Korean universities, contributing to R&D projects funded by the national government or hightech industries. This is but a narrow slice of the global and transnational enterprise called contemporary science. The experience made me wonder about the vast scale and scope of India's scientific workforce. Thankfully, Pankaj Sekhsaria provides a valuable glimpse into the underbelly of Indian scientific research. The two books under review are products of Sekhsaria's doctoral research at Maastricht University in the Netherlands. He has conducted fieldwork in five nanoscience laboratories in Pune, Hyderabad, and Chennai, all in India. Sekhsaria's focus on nano may have been opportunistic, as at the time nanotechnology was the leading buzzword around the world. However, the survey opens a window for observing the state of scientific research in contemporary India. Nanoscale is a more popular rendition, succinctly outlining the research activities in the five labs; Instrumental Lives is a more scholarly version, focusing on one of them. In Instrumental Lives, Sekhsaria shines a spotlight on the scientific life of C. V. Dharmadhikari, a physicist at the University of Pune credited with the indigenous development of the scanning tunneling microscope (STM). Dharmadhikari completed his Ph.D. at Pune in 1979 and, like many good Indian scientists, continued his research career in the United States. By the mid-1980s, he was positioned squarely within the surface science community as the STM took off as a major scientific instrument (Mody, Instrumental Communities). When Dharmadhikari returned to Pune as faculty member, he set out to make his own STM there, successfully completing the first version by 1988. The city of Pune provided a fertile environment for Dharmadhikari to pursue his goals. Located 90 miles inland, in southeast Mumbai, Pune was [End Page 859] home to many educational and research institutions. It was also bustling with manufacturing and commercial activities in "thriving small and medium industry enterprises" and booming "junk markets" (pp. 43, 52). The lively urban environment shaped Dharmadhikari's scientific practice. Not only could he acquire diverse parts from bungee cords and stepping motors to tuning forks and bobbins (p. 40), he also had access to a wide range of experts in nearby institutions who shared his interest and would become "users" of his microscope. In short, Dharmadhikari utilized whatever resources were available to create his version of the STM, which he then used to produce scientific knowledge and skilled scientists of the nanoscale. In analyzing this case, Sekhsaria is primarily interested in identifying the nature of Dharmadhikari's work. Was it a "quintessentially Indian" (p. 10) mode of innovation, or a mere replica of original work by Western scientists? At this juncture, the recent debate on jugaad is useful. While difficult to translate into English, the term usually refers to "creative improvisation" (p. 30). An archetypal example of jugaad is the low-cost quadricycle made of wooden planks and a repurposed diesel engine. On one hand, these kuddukkas represent the ingenuity and resourcefulness of Indians under severe financial constraints. On the other hand, they are looked down on as something that falls short of "genuine innovation" (p. 32). By acknowledging Dharmadhikari's microscope as "technological jugaad" (ch. 5), Sekhsaria emphasizes the role of science and technology as solutions to mundane problems, eschewing the idolization of innovation as pursuit of high-tech agendas disengaged from everyday life. Scientists and technologists in the "underdeveloped" world (one could say postcolonial or non-Western, depending...

Spam Mail Detection using Machine Learning

Abstract: Spam email is one of the most serious problems in the online world. Nowadays, a large portion of the population relies on available emails or communications from strangers. As a result, the fact that anyone can leave an email or a message opens the door for spammers to compose spam messages concerning our various interests. Spam fills up our inbox with unnecessary messages, slowing down our internet connection and stealing valuable information such as our contact information and accurate information. Detecting spammers and spam content is a major issue of research and time-consuming tasks. Email spam is when someone sends out a large number of emails in a short period of time. The purpose of spam filtering is to determine whether an email is spam or ham. With this proposed system the specified mail can be detected as spam or ham and also IP address of mail.

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

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

Applications of Artificial Intelligence in Machine Learning: Review

Artificial Intelligence has been growing in all the technologically relevant fields but it's also spreading within the areas where nobody had imagined it to be. This may sound sort of a progress but it is often equally disruptive in future. It is believed that AI may be a very sensitive issue and if not handled with care, it could find itself imparting ‘Superintelligence’ to machines which might make them even more intelligent than humans. Machine learning is one among the foremost exciting recent technologies in AI. Learning algorithms in many applications that we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is employed and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to go through plenty of spam emails, that's also a learning algorithm. In this paper, a quick review and future prospect of the vast applications of machine learning has been made.

Spam Detction Using Information Retrieval And Machine Learning

In the 21st century, World has become a large ball stuffed with large amounts of information. People habituated to this type of communication, they're messages, emails and call etc. Keeping of these things in mind, it's obvious to induce spam messages, most of them are commercial, but repeatedly such emails or messages may contain some phishing links that have malware. so as to beat from this problem, we might prefer to propose a mechanism to detector identity such spam emails so time and memory space of the system may be stored-up to an excellent extent. during this paper we've got come up with an answer to detect the spam, involving the concepts and algorithms of data retrieval and Machine Learning.