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Nurlina, Nurlina, and Irmayana Irmayana. "Studi Banding Spam-Assassin Mail Server Dengan dan Tanpa Filter di Sisi Mail Client." Creative Information Technology Journal 1, no. 2 (2015): 77. http://dx.doi.org/10.24076/citec.2014v1i2.12.
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Filter spam yang disediakan oleh situs penyedia layanan email seperti yahoo, gmail, aim mail, windows live hotmail, dan masih banyak lagi yang lainnya, merupakan fasilitas yang sangat bermanfaat bagi para usernya. Filter spam tidak akan berfungsi sebagaimana yang diharapkan oleh user client. Pada saat alamat email para client sudah pernah di subscribe dengan tujuan tertentu seperti misalnya untuk registrasi mailing list, newsgroup, dan lain sebagainya, maka alamat emailnya itu sudah tidak aman lagi dari para spammer. Pada dasarnya para admin mail server hanya menggunakan secara langsung filter spam yang disediakan oleh mail server yang diinstal, tanpa memberikan penyettingan tertentu yang dibutuhkan client sama sekali. Para user sendiri yang seharusnya lebih aktif dalam menyaring spam pada email yang digunakan dengan banyak cara. Penelitian ini memanfaatkan aplikasi mail client Thunderbird untuk menyaring spam dengan metode Bayesian sebagai kelanjutan dari menyaring spam yang sudah tersaring sebelumnya pada sisi mail server dan bertujuan menganalisis hasil pengklasifikasian email ham dan email spam pada mail server dan mail client. Disimpulkan bahwa nilai akurasi dan error filter spam pada mail server berhubungan dengan filter Spam-Assassin yang tidak disetting dan dikonfigurasi oleh adminnya menunjukkan hasil yang tidak memuaskan dibandingkan dengan filter spam metode bayesian pada mail client yang sudah di-training.Spam filters provided by your email service provider websites such as yahoo, gmail, AIM mail, windows live hotmail, and many others, is a very powerful feature for the user. The spam filter will not work as expected by the client user. at the time of the email address of the client has been ever subscribe to a specific purpose such as for registration, mailing lists, newsgroups, and so forth, then the email address is no longer safe from spammers. Basically the admin mail server directly using only the spam filter provided by the mail server is installed, without giving a specific setting it takes the client at all. The users themselves are supposed to be more active in the spam filter on the email that is used in many ways. This study utilizes Thunderbird mail client application to filter spam with Bayesian methods as a continuation of the spam filter that has been previously filtered on the mail server and to analyze the results of the classification of ham and spam e-mail on the mail server and mail client. It was concluded that the accuracy and error spam filter on the mail server associated with the filter Spam-Assassin is not be set and configured by the admin showed unsatisfactory results compared with the Bayesian method to filter spam mail client that is already in-training
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Shirwadkar, Aryan, and Samuel Jacob. "Spam Mail Classifier." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 862–67. http://dx.doi.org/10.22214/ijraset.2022.48051.
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Abstract: Email is the worldwide use of communication application. It is because of the ease of use and faster than other communication application. However, its inability to detect whether the mail content is either spam or ham degrade its performance. Nowadays, lot of cases have been reported regarding stealing of personal information or phishing activities via email from the user. This project will discuss how machine learning help in spam detection. Machine learning is an artificial intelligence application that provides the ability to automatically learn and improve data without being explicitly programmed. Binary classifierwill be used to classify the text into two different categories: spam and ham. The algorithm will predict the score more accurately. The objectiveof developing this model is to detect and score word faster and accurately.
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Hemalatha, M., Sriharsha Katta, R. Sai Santosh, and Priyanka Priyanka. "E-MAIL SPAM DETECTION." International Journal of Computer Science and Mobile Computing 11, no. 1 (2022): 36–44. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.006.
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E-mail is the most important form of communication. Used for a wide range of people including individuals and organizations. But these people using this e-mail they find it difficult to use because of spam mail. These spam emails are also called unsolicited bulk mail or junk mail. Spam emails are available randomly sent messages to people by anonymous users. Sites are trying to steal yours personal, electronic and financial information. An increase in spam emails leads to crime of theft of sensitive information, reduced productivity. Spam detection is dirty. The line between spam and non-spam messages is blurred, and the condition changes over time. From various attempts to automate spam detection, machine learning has so far proven to be the most effective and popular method of email providers. While we still see spam emails, a quick look at the trash folder will show how many spam is removed from our inbox daily due to machine learning algorithms. It is estimated that 40% of emails are spam mail. These spam wastes time, storage the space and width of the communication band. There are a few ways to receive spam emails but spam senders make it difficult for you to send users from a random sender address or by adding special characters at the beginning or end of the email. There are several machine learning methods for filtering spam emails including Naïve Bayes classifier, Vector support equipment, Neural Networks, Close Neighbour, Rough Sets and Random Forests. In this project we use the Naïve Bayes classifier to identify spam mail. The vast majority of people depend on what is available email or messages sent by a stranger. Possibly anyone can leave an email or message provide gold the opportunity for spam senders to write a spam message about us different interests. Spam fills in the inbox with a number of funny things mails. Slow down our internet speed. Theft useful information such as our details on our contact list. Identifying these people who post spam and spam content can be a a hot topic for research and strenuous activities. Email Spam is functionality of mass mailings. From the cost of Spam is heavily censored by the recipient, it is a successful post proper advertising. Spam email is a form of commercial advertising economically viable because email can be costly effective sender method. With this proposed model some message may be declared spam or not use Bayes' theorem and Naive Bayes’ Classifier and IP addresses of sender is usually found.
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Avijit, Mallik, Ahmad Sabbir, Arman Arefin Md., and Hosen Sarwar. "SPAM E-MAIL CHARACTERIZATION: AN EXPERIMENTAL PERFORMANCE COMPARISON OF MACHINE LEARNING." International Journal of Advanced Engineering and Science 6, no. 2 (2017): 44–51. https://doi.org/10.5281/zenodo.1016645.
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The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters. Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful data for all the machine learning calculations considered to detect spam e-mail.
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Upadhyay, Rohitkumar R. "E-Mail Spam Filtering." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1265–69. http://dx.doi.org/10.22214/ijraset.2021.39004.
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Abstract: E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches. Keywords: e-mail spam, unsolicited bulk email, spam filtering methods.
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Saito, Takamichi, Akio Morii, Tadashi Komori, Toshiyuki Kito, and Fumio Mizoguchi. "Anti-spam mail system." Systems and Computers in Japan 37, no. 13 (2006): 99–108. http://dx.doi.org/10.1002/scj.10627.
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Journal, IJSREM. "E-MAIL SPAM DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26878.
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Emails have become a ubiquitous means of personal and professional communication, often containing sensitive and confidential information. However, they are also prime targets for cybercriminals who employ techniques such as phishing to obtain private data. This paper proposes an intelligent and efficient email spam detection system that leverages data mining algorithms for classification and association. By extracting multiple features from email content, we improve classification accuracy and efficiency. The system integrates various machine learning algorithms and achieves a 30% reduction in the error rate compared to existing methods. Our approach enhances email spam detection by combining support vector machines with multi-feature extraction and classification. Key Words: Support vector machines, Email Spam, Phishing Detection, Machine Learning, Multi-Feature Extraction.
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Gong, Song Jie, and Xue Mei Zhang. "Design and Implementation of Intelligent Spam Filtering System." Advanced Materials Research 846-847 (November 2013): 1624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1624.
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With the rapid development of Internet, E-mail has been widely applied, and along goes a great deal of useless and harmful information. In the face of todays rampant spam developments, anti-spam mechanism is the mail filtering technology has gradually become the focus of information security. While the technical performance of spam filtering is good or bad, the key lies in the amount of spam sample collection, study and analysis. Through the analyzing and processing of spam, the paper designs and implements the intelligent spam filtering system. It brings forward some new theories. Based on analyzing actuality, origin and characteristic of spam, the paper also mainly expounds several filtering technique applied in E-mail.
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Fogel, Joshua, and Viju Raghupathi. "Spam E-mail Advertisements for Cosmetics / Beauty Products and Consumer Behavior." Journal of Business Theory and Practice 1, no. 1 (2013): 28. http://dx.doi.org/10.22158/jbtp.v1n1p28.
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Consumers receive spam e-mail solicitations for cosmetics and beauty products. We analyze responses<br />from 200 college students with regard to opening and reading this spam e-mail and also clicking<br />through and purchasing the product advertised in this spam e-mail. With regard to opening and reading<br />spam email about cosmetics/beauty products, women and also increasing scores for learning more<br />information online about cosmetics/beauty products were both significantly associated with increased<br />odds for opening and reading this spam e-mail. With regard to purchasing the cosmetics/beauty product<br />advertised in the spam e-mail, increasing scores for trust in the Internet to provide accurate<br />information about cosmetics/beauty products was significantly associated with increased odds for<br />purchasing. Marketers who use ethical approaches and are interested in sending e-mail information to<br />prospective college student customers about cosmetics/beauty products should keep in mind the<br />importance of conveying trust.
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Rose, Chris. "Finding A Recipe For Spam." Review of Business Information Systems (RBIS) 8, no. 2 (2004): 19–26. http://dx.doi.org/10.19030/rbis.v8i2.4494.
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The prevalence of unsolicited e-mail, otherwise called spam, continues to haunt every user of the Internet. The overwhelming response to the governments do-not-call registry in which persons could register their telephone numbers in a database that will restrict telemarketers from calling, is an indication that people are becoming increasingly resentful of unwanted intrusions into their personal lives. It is estimated that more than a half of all e-mail, or over one trillion pieces of spam will reach the inboxes of Internet users this year but the problems of controlling spam are many since:(a) spam is virtually free for the sender (b) the SMTP protocol which governs the transmission of e-mail on the Internet was not designed to handle the complexities of deception and mistrust on a large network and (c) many major corporations are surreptitiously involved in spam. Although the development of a social conscience might keep some large corporations from engaging in spam, but spam, as we know it, would cease to exist only if either the cost of sending e-mail increased or a new secure protocol to exchange e-mail was developed. Of the two options, the quickest and easiest remedy would be to eliminate the reverse economics of sending spam by introducing a computing cost for sending e-mail.
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Damanik, Janner. "EKSISTENSI ALAT BUKTI EMAIL DALAM PERKARA PERDATA." Juripol 4, no. 1 (2021): 409–16. http://dx.doi.org/10.33395/juripol.v4i1.11176.
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Perkembangan serta tingkat kemudahan dalam penggunaannya (easy of use), beberapa orang mulai berpikir untuk menggunakan fasilitas email untuk kegiatan spamming. Pelaku spamming biasa disebut spammer. Spammer melakukan spamming, sehingga menghasilkan Spam Mail. Spam mail didefinisikan sebagai e-mail yang berisi hal-hal yang tidak kita inginkan dan kadang dikirimkan oleh orang yang tidak dikenal (unsolicited commercial e-mail). Spam e-mail disebut pula Bulk atau Junk e-mail. Spam atau junk e-mail berkembang sangat besar beberapa tahun terakhir. Riset yang dilakukan oleh IDC menyatakan bahwa sepertiga (32%) traffic e-mail yang beredar sekarang ini adalah spam Dengan adanya perkembangan teknologi yang semakin pesat dan perkembangan telekomunikasi tersebut sangat memudahkan seseorang berkirim surat melalui e-mail sebab penggunaan e-mail tersebut dianggap murah dan cepat. Penggunaan e-mail juga sangat berperan sekali dalam berbagai kegiatan pendidikan, bisnis, perdagangan, sosial dan berbagai kegiatan lainnya. Untuk itu perlu adanya pengertian baru mengenai alat bukti yang dapat digunakan dalam proses persidangan dalam bentuk e-mail tersebut. Perkembangan pesat internet juga telah menimbulkan berbagai sengketa dan konflik hukum yang cukup serius bagi pemakainya. Banyak berbagai persoalan yang tidak terduga sebelumnya, dalam beberapa tahun terakhir ini ternyata bermunculan Hal tersebut tidak lain akibat pesatnya akselerasi teknologi informatika. Salah satunya terjadinya kemajuan yang tak terduga dalam bentuk-bentuk e-commerce termasuk e-governance. Perkembangan dunia maya nyatanya tak mungkin dicegah. Bahkan saja lintas wilayah, tapi batas negarapun ditembusnya. Borderless merupakan sifat dari internet itu sendiri.
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Latreche, Abdelkrim, and Kadda Benyahia. "A New Bio-Inspired Method for Spam Image-Based Emails Filtering." International Journal of Organizational and Collective Intelligence 11, no. 2 (2021): 29–50. http://dx.doi.org/10.4018/ijoci.2021040102.
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Electronic mail has become one of the most popular and frequently used channels for personal and professional online communication. Despite its benefits, e-mail faces a major security problem, which is the daily reception of a large number of unsolicited electronic messages, known as “spam emails.” Today, most electronic mail systems have simple spam filtering mechanisms based on text spam filtering technologies. To circumvent these filters, spammers are introducing new techniques of embedding spam messages in the image attached to the mail. In this article, the authors propose a new method for spam image filtering. The proposed system can distinguish between legitimate images from spam images based on the texture characteristics of the image attached to an email. From each image, around 20 characteristics can be extracted from the gray level co-occurrence matrix (GLCM). Then, to classify the images as spam or ham, the authors use a new metaheuristic nature-inspired model for building classifiers based on the social worker bees and enhanced nearest-centroid classification method.
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Krishna, Mr B. "E-Mail Spam Classification using Naive Bayesian Classifier." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5209–14. http://dx.doi.org/10.22214/ijraset.2021.36153.
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— E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it’s an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naïve Bayesian Classifier for spam classification. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the Lingspam dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature. The result is to increase the accuracy of the system.
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patil, Niraj. "Spam Mail Detection Using Blockchai." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34520.
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The role played by email communication in our lives nowadays has been such a tremendous one especially when it comes to fast exchange of information. Nevertheless, this convenience is marred by the omnipresent threat of email spam that not only disrupts channels of communication but also present serious security and privacy concerns. Traditional models of spam detection which are based on rules or heuristics tend to fail because they do not adapt quickly enough to the new techniques employed by spammers. In response to these challenges, this paper proposes an inventive solution to the problem—integration of blockchain technology into the process of detecting email spams.Email spam is often defined as an unwanted and usually malicious form of correspondence, thus it has continued being a notable cyber security worry. The conventional mechanisms for discovering them are prone to false positives and negatives at times. Additionally, such systems have centralized data which can be interfered with and accessed without permission. Weighing up the limitations inherent in existing methods, this research examines how blockchain may change email spam detection. Keywords— Blockchain technology, ethereum, Spam, email
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Abhinav, Chode. "Spam Mail Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2327–29. http://dx.doi.org/10.22214/ijraset.2022.44315.
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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.
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Karyawati, AAIN Eka, Komang Dhiyo Yonatha Wijaya, I. Wayan Supriana, and I. Wayan Supriana. "A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 8, no. 2 (2023): 91–97. http://dx.doi.org/10.33480/jitk.v8i2.2463.
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Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively.
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Mukhtar, Harun, Daniel Adi Putra Sitorus, and Yulia Fatma. "Analisa Dan Implementasi Security Mail Server." JURNAL FASILKOM 10, no. 1 (2020): 25–32. http://dx.doi.org/10.37859/jf.v10i1.1906.
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Mail server is one of the most widely used server functions in the company. This discusses e-mail itself which can reduce mailing costs, is more efficient than manual communication and can be used as attachments that are useful as a supplement and additional documents related to the contents of e-mail. Zimbra is a mail server application that provides complete features and also makes it easy to install mail server management, also mail server security issues are a factor that must be considered by the system administrator. The security design for e-mail servers addresses the importance of being able to prevent spam e-mail attacks that can fill e-mail servers and make mail server performance faster. Because a good mail server security can optimize the performance of the mail server itself. In this final project, the work and implementation of the zimbra mail server security will be carried out specifically for handling email spam. The zimbra email server will analyze its security against spam email attacks, so that it can function as an email server on the company.
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Wu, Chengrong, and Jianlin Wang. "Comparison of spam classification methods based on machine learning." Applied and Computational Engineering 6, no. 1 (2023): 269–75. http://dx.doi.org/10.54254/2755-2721/6/20230786.
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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.
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Xie, Mengjun, Heng Yin, and Haining Wang. "Thwarting E-mail Spam Laundering." ACM Transactions on Information and System Security 12, no. 2 (2008): 1–32. http://dx.doi.org/10.1145/1455518.1455525.
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Zhang, Siwei. "Spam/Ham email classification using BERT." Applied and Computational Engineering 6, no. 1 (2023): 1197–203. http://dx.doi.org/10.54254/2755-2721/6/20230571.
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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.
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Sulaeman, Nana Suarna, Abdul Ajiz, Agus Bahtiar, and Fathurrohman. "Perbandingan Kinerja Algoritma Naïve Bayes Dan C.45 Dalam Klasifikasi Spam Email." KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer 6, no. 1 (2022): 8–14. http://dx.doi.org/10.32485/kopertip.v6i1.130.
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Antispam dengan algoritma tertentu yang dapat memisahkan antara spam-mail dengan non spam mail. Perbandingan kinerja antara algoritma naïve bayes, dan decision tree yang memakai algoritma C4.5 membuktikan bahwa decision tree dengan algoritma C4.5 lebih efisien dan paling sederhana jika dibandingkan dengan algoritma yang lain. Dengan kesederhanaannya, algoritma C4.5 memberikan hasil yang lebih baik untuk klasifikasi spam-mail. Algoritma Naïve Bayes dan C4.5 mempunyai kinerja yang baik dalam mengidentifikasi apakah suatu email adalah spam atau non-spam. Namun, belum diketahui algoritma mana diantara keduanya yang lebih unggul kinerjanya. Oleh karena itu kedua algoritma ini perlu dibandingkan Berdasarkan hasil akurasi algoritma Naïve Bayes menghasilkan akurasi sebesar 96,70% dengan rincian yaitu Prediksi Ham dan true Ham sebanyak 3385 Data, Prediksi Ham dan true Spam sebanyak 165 Data, Prediksi Spam dan true Ham sebanyak 0 Data, Prediksi Spam dan true Spam sebanyak 1448 Data. Berdasarkan hasil akurasi algoritma C.45 menghasilkan akurasi sebesar 96,68% dengan rincian yaitu Prediksi Ham dan true Ham sebanyak 3385 Data, Prediksi Ham dan true Spam sebanyak 166 Data, Prediksi Spam dan true Ham sebanyak 0 Data Prediksi Spam dan true Spam sebanyak 1447 Data. Berdasarkan hasil uji komparasi diperoleh hasil algoritma terbaik dengan mengukur tingkat hasil akurasi sehingga dapat diperoleh algoritma C.45 memiliki nilai sebesar 96.68% Kemudian pada penerapan model algoritma naïve bayes menjelaskan bahwa tingkat hasil akurasi dapat diperoleh dari algoritma naïve bayes dengan nilai sebesar 96.79%. bisa di artikan bahwa algoritma naïve bayes data dikategorikan sebagai pedoman pengambilan keputusan
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Teja, P. Sai. "Prediction of Spam Email using Machine Learning Classification Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1107–12. http://dx.doi.org/10.22214/ijraset.2021.35226.
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Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.
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S, Santhi, and Mohan M. "Image Spam Detection through Pattern Recognition with IP Tracing Technique." International Research Journal of Computer Science 11, no. 10 (2024): 601–4. http://dx.doi.org/10.26562/irjcs.2024.v1110.01.
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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.
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ZHOU, YAN, MADHURI S. MULEKAR, and PRAVEEN NERELLAPALLI. "ADAPTIVE SPAM FILTERING USING DYNAMIC FEATURE SPACES." International Journal on Artificial Intelligence Tools 16, no. 04 (2007): 627–46. http://dx.doi.org/10.1142/s0218213007003473.
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Unsolicited bulk e-mail, also known as spam, has been an increasing problem for the e-mail society. This paper presents a new spam filtering strategy that 1) uses a practical entropy coding technique, Huffman coding, to dynamically encode the feature space of the e-mail collected over time and, 2) applies an online algorithm to adaptively enhance the learned spam concept as new e-mail data becomes available. The contributions of this work include a highly efficient spam filtering algorithm in which the input space is radically reduced to a single-dimension input vector, and an adaptive learning technique that is robust to vocabulary change, concept drifting and skewed class distributions. We compare our technique with several existing off-line learning techniques including support vector machine, logistic regression, naïve Bayes, k-nearest neighbor, C4.5 decision tree, RBFNetwork, boosted decision tree and stacking. We demonstrate the effectiveness of our technique by presenting the experimental results on the e-mail data that is publicly available. A more in-depth statistical analysis on the experimental results is also presented and discussed.
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Priti, Dr, and Uma Uma. "Performance Analysis, Comparative Survey of Various Classification Techniques in Spam Mail Filtering." Oriental journal of computer science and technology 10, no. 3 (2017): 698–702. http://dx.doi.org/10.13005/ojcst/10.03.21.
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One of the most common methods of communication involves the use of e-mail for personal messages or for business purposes. One of the major concerns of using the emails is the problem of e-mail spam. The worst part of the spam emails is that, these are invading the users without their consent and bombarding of these spam mails fills up the whole email space of the user along with that, the issue of the wasting the network capacity and time consumption in checking and deleting the spam mails makes it even more concerning issue. With the increasing demand of removing the e-mail spams the area has become magnetic to the researchers. This paper intends to present the performance comparison analysis of various pre-existing classification technique. This paper discusses about spam mails in section (I), In section (II) various feature selection methods are discussed , In section (III) classification techniques concept in spam filtering has been elaborated, In section (IV) existing algorithms for classification are discussed and are compared. In section (V) concludes the paper giving brief summary of the work.
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Nallabariki, Jayasree, T. Keerthi Chandana, D. Sai Tejaswi, and Dr M. Y. Yesu Babu. "A Comprehensive Overview on Intelligent Spam Email Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1702–7. http://dx.doi.org/10.22214/ijraset.2023.49529.
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Abstract: Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years. Nowadays, automatic email filtering appears to be the most successful strategy for preventing spam. Only several years ago most of the spam could be reliably dealt with by blocking e-mails coming from certain addresses or filtering out messages with certain subject lines. Spammers started employing a number of cunning strategies to get beyond filtering techniques, such as utilizing random sender addresses and/or adding random characters to the message subject line's beginning or conclusion. Machine learning techniques now a days are used to automatically filter the spam e-mail in a very successful rate. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Understanding, observing, and providing knowledge about a statistical occurrence are all terms used here. In the first place, data collection and representation are typically problem-specific (i.e., for email messages), and in the second place, e-mail feature selection and feature reduction aim to lower the dimensionality (i.e. the number of features).Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set. Machine Learning approach includes lots of algorithms that can be used in e-mail filtering like Naïve Bayes, K-nearest neighbour, Support VSector Machine, classifiers. In conclusion, we try to summarize the performance results of the few machine learning methods in terms of spam precision and accuracy.
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Suhail Najam, Shahad, and Karim Hashim AL-Saedi. "Spam classification by using association rule algorithm based on segmentation." International Journal of Engineering & Technology 7, no. 4 (2018): 2760. http://dx.doi.org/10.14419/ijet.v7i4.18486.
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Email is a most widespread and active communication technique. The major purpose behind the success of email is the vast availability, facility of utilize, and affordability. Therefore this technology has be a susceptible to malicious attacks; Email is the most frequently applied delivery technique for malware. E-mail spam is one of the major problems of the Internet today, and get financial harm to companies and individual users is uncomfortable. Spam mail can be harmful as they may include malware & links to phishing Web sites. So necessary to divide spam from mail messages to a separate folder. In this paper utilize one of datamining mechanism is association rule algorithm.in association rule; pattern discovered based on relationship between item-sets. The dataset utilized in proposed system is Enron dataset is divided into two parts: spam and non-spam. For extract features from dataset used Term Frequency Invers Term Frequency (TFIDF) method. For reduce dimensionality of feature space use Information Gain (IG) method.
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Zhang, Dai Yuan, and Lei Yang. "Implementation of Mail Classification Using Neural Networks of the Second Type Spline Weight Functions." Applied Mechanics and Materials 513-517 (February 2014): 687–90. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.687.
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How to effectively filter out spam is a topic worthy of further study for the growing proliferation of spam. The main purpose of this paper is to apply a new neural network algorithm to the classification of spam. In this paper, we introduce a second type of spline weight function neural network algorithm, as well as e-mail feature extraction and vectorization, and then introduced the mail sorting process. Experiments show that it can get a relatively high accuracy and recall rate on the spam classification. Therefore, with this new algorithm, we can achieve better classification results.
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Kim, So Yeon, and Kyung-Ah Sohn. "Graph-Based Spam Image Detection for Mobile Phone Spam Image Filtering." International Journal of Software Innovation 3, no. 4 (2015): 72–86. http://dx.doi.org/10.4018/ijsi.2015100106.
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Spam images in mobile phones have increasingly appeared these days. As the spam filtering systems become more sophisticated, spams are being more intelligent. Although detection of email-spams has been quite successful, there have not been effective solutions for detecting mobile phone spams yet, especially, spam images. In addition to the expensive image processing time, insufficient spam image data in mobile phones makes it challenging to train a general model. To address this issue, the authors propose a graph-based approach that utilizes graph structure in abundant e-mail spam dataset. The authors employ different clustering algorithms to find a subset of e-mail spam images similar to phone spam images. Furthermore, the performance behavior with respect to different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is extensively investigated. The authors' results highlight that the proposed idea is fairly meaningful in increasing training data size, thus effectively improving image spam detection performance.
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Chirra, Venkata RamiReddy, Hoolda Daniel Maddiboyina, Yakobu Dasari, and Ranganadhareddy Aluru. "Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks." Review of Computer Engineering Studies 7, no. 4 (2020): 91–95. http://dx.doi.org/10.18280/rces.070403.
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Spam in email box is received because of advertising, collecting personal information, or to indulge malware through websites or scripts. Most often, spammers send junk mail with an intention of committing email fraud. Today spam mail accounts for 45% of all email and hence there is an ever-increasing need to build efficient spam filters to identify and block spam mail. However, notably today’s spam filters in use are built using traditional approaches such as statistical and content-based techniques. These techniques don’t improve their performance while handling huge data and they need a lot of domain expertise, human intervention and they neglect the relation between the words in context and consider the occurrence of the word. To address these limitations, we developed a spam filter using deep neural networks. In this work, various deep neural networks such as RNN, LSTM, GRU, Bidirectional RNN, Bidirectional LSTM, and Bidirectional GRU are used to a built spam filter. The experimentation was carried out on two datasets, one is a 20 newsgroup dataset, which contains multi-classes with 20,000 documents and the other is ENRON, a dataset contains 5,000 emails. The custom-designed models have performed well on both benchmark datasets and attained greater accuracy.
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Firdausillah, Fahri, Muhammad Hafidz, Erika Devi Udayanti, and Etika Kartikadarma. "Sistem Deteksi Surel SPAM Dengan DNSBL Dan Support Vector Machine Pada Penyedia Layanan Mail Marketing." Journal of Information System Research (JOSH) 3, no. 4 (2022): 618–25. http://dx.doi.org/10.47065/josh.v3i4.1795.
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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.
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Babhulkar, Vaishnavi, Apurva Salphale, Anagha Garkade, Kalyanee Pachghare, Tanvi Sagane, and Prof. Vanisha P. Vaidya. "ML-Driven Technique for Adaptive Email Filtering." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 1 (2024): 50–55. https://doi.org/10.5281/zenodo.10822621.
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<em>Email filtering technology must be developed quickly due to the increase of unsolicited emails, or spam mail. Computer security has struggled with spam emails consistently. They are incredibly expensive economically and exceedingly risky for networks and computers. Spam emails are found and filtered using machine learning techniques. This project mainly focuses on machine learning used to find and remove spam emails. Using the K-nearest neighbour algorithm for email spam detection is one of the simple supervised learning techniques. Initially, the relevant features for filtering the spam messages are extracted from the study and it acts as an antispam filter. It thereby generates a successful corpus list for the detection of spam emails. The experiments are conducted on various email datasets and the results show that the proposed kNN density-based clustering offers improved performance than the other methods. Applications are utilising machine learning techniques in spam filters for email in Gmail, one of the top internet service providers. Regression method used to detect and filter spam mail.</em>
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BAKTIR, Nuriye, and Yılmaz ATAY. "Comparative Analysis of Machine Learning Approaches in the Spam-Mail Classification Problem." Bilişim Teknolojileri Dergisi 15, no. 3 (2022): 349–64. http://dx.doi.org/10.17671/gazibtd.1014764.
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Elektronik posta, kuruluşların, kişilerin sıklıkla kullandıkları dosya paylaşımı gibi çeşitli etkileşimlerin bulunduğu iletişim aracıdır. Bu tür araçların faydalı etkilerinin yanında istenmeyen elektronik posta paylaşımı da söz konusudur. İstenmeyen elektronik postalar ‘Spam’ adı ile etiketlenmektedir. Spam elektronik postalar; istenmeyen reklamlar, virüs etkileşimleri ve oltalama gibi zararlı içeriklere kaynak teşkil edebilmektedir. İletişimde güvenliğin oldukça önemli olduğu bilinmektedir. Bu sebeple elektronik posta sistemlerinin zararlı araçlardan veya yazılımlardan arındırılabilmesi için çeşitli kriterlere göre sınıflandırılması önem arz etmektedir. Literatürde bu tür çalışmalar farklı başlıklar altında sunulmaktadır. Sınıflandırma çalışmalarında makine öğrenmesi algoritmaları etkin bir şekilde kullanılmaktadır. Bu çalışma kapsamında naive bayes, lojistik regresyon, karar ağacı ve k-en yakın komşu algoritmalarının ilgili probleme uyarlanması ve karşılaştırmalı olarak analiz edilmesi amaçlanmıştır. Burada farklı metodolojilere sahip yaklaşımların ilgili problem üzerindeki etkisi detaylı olarak incelenmek istenmiştir. Bu kapsamda algoritmalar çeşitli veri setleri kullanılmıştır. Veri setlerinin farklı büyüklüklerde ve farklı ham/spam oranlarında olması çalışma üzerindeki etkisi tartışılmıştır. Farklı başarım sonuçları elde edilmiştir. Bu başarım sonuçlarının farklı metotlara göre karşılaştırması yapılarak tablolar halinde sunulmuştur. Veri seti sayısının ve spam oranının fazla olması Enron 5 veri setinde etkili sonuçların elde edilmesini sağlamıştır. Farklı özellik seçim yöntemlerinin kullanımıyla Karar ağacı algoritmasının Enron 4 veri seti üzerinde iyi performans göstermesini sağlamıştır. En iyi başarım performanslarının CS440/ECE448 veri seti üzerindeki testlere göre lojistik regresyon ve k-en yakın komşu algoritmalarıyla elde edildiği gözlemlenmiştir.
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Tejal, S. Murkute, K. Choudhari Nitin, and M. Kate Dipalee. "Review on Efficient Spam Detection Technique using Machine Learning." Research and Applications: Embedded System 4, no. 3 (2022): 1–7. https://doi.org/10.5281/zenodo.5878454.
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<em>People's communication methods are being transformed by electronic mail because of its affordability, speed, and simplicity. Due to their widespread exposure, spam emails have become a serious roadblock in electronic communication. The amount of time users sifting through incoming mail and eliminating spam necessitates the implementation of spam detection software. The main objective is to create suitable filters that can correctly recognise these emails and deliver outstanding performance in the majority of cases. This project makes use of Spam Detection to tell spam from valid email. SVM, a machine learning method, is employed in this case to assess it. SVMs and other approaches of machine learning (AI) Spam detection can benefit greatly from machine (SVM) detection. This project's classification is based on its features. In the email world, spam is a term that refers to unsolicited commercial communications or emails that deceive the recipient. With the use of artificial intelligence and machine learning, spam messages can be identified. Spam filtering is a popular application of machine learning techniques. Machine learning classifiers are used to identify emails as either ham (legitimate messages) or spam (unwanted messages) using these techniques.</em>
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Harjot, Kaur* Er. Prince Verma. "SURVEY ON E-MAIL SPAM DETECTION USING SUPERVISED APPROACH WITH FEATURE SELECTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 120–28. https://doi.org/10.5281/zenodo.496096.
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Electronic Mail (E-mail) has established a significant place in information user’s life. Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of mails have generated various serious issues too. Emails are categorized into ham and spam emails. From past decades spam emails are spreading at tremendous rate. These spam emails are illegitimate and unwanted emails that may contains junk, viruses, malicious codes, advertisements or threat messages to the authenticated account holders. This serious issue has generated a need for efficient and effective anti-spam filters that filter the email into spam or ham email. Spam filters prevent the spam emails from getting into user’s inbox. Email spam filters can filter emails on content base or on header base. Various spam filters are labeled into two categorizes machine learning and non-machine learning techniques. This paper will discuss the process of filtering the mails into spam and ham using various techniques.
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Hnini, Ghizlane, Jamal Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, and Hamid Tairi. "MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection." Applied Sciences 11, no. 24 (2021): 11968. http://dx.doi.org/10.3390/app112411968.
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Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.
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Akalya, Devi C., Renuka D. Karthika, and S. Sarvesh. "Content based Detection and Blocking of Spam/Phishing Emails using Machine Learning." Recent Trends in Information Technology and its Application 5, no. 3 (2022): 1–14. https://doi.org/10.5281/zenodo.7379526.
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Utilising the web has been increasing day by day, as a greater number of people are using it, especially for communication. E-mail remains to be one of the most efficient ways of communication techniques and one of the most effective tools for communication for social to business purposes, due to its cost and minimum time consumption. Through e-mail, one can flood the internet by sending multiple copies of same message to large number of users. One important issue to be addressed in e-mails is that our inboxes are generally affected by attacks which mainly includes spam. Currently, spam e-mails are identified by detecting stop words in it, however if any new spam, fake or irrelevant e-mail is sent without including the stop words, it isn't properly identified. Therefore, a system should learn the words and its meaning to detect spam e-mails efficiently. To overcome this issue of blocking new and unrecognised spam e-mails, Machine Learning based approach on ‘Phishing Websites’ dataset from the UCI repository is proposed. Our proposed methodology is to use Morphological Analysis in Natural Language Processing (NLP) for better spam identification. By utilising the machine learning techniques efficiently, spam and phishing emails are to be detected and blocked in the server side itself.
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Reddy, Anuradha, M. Uma Maheswari, A. Viswanathan, and G. Vikram. "Using Support Vector Machine For Classification And Feature Extraction Of Spam In Email." International Journal of Innovation in Engineering 2, no. 2 (2022): 26–32. http://dx.doi.org/10.59615/ijie.2.2.26.
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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.
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Abd El-Kareem, Mohamed, Ayman Elshenawy, and Fawzi Elrfaey. "MAIL SPAM DETECTION USING STACKING CLASSIFICATION." Journal of Al-Azhar University Engineering Sector 12, no. 45 (2017): 1242–55. http://dx.doi.org/10.21608/auej.2017.19151.
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Anupriya, Koneru, Kurakula Harini, Kethe Balaji, and Karnati Geetha Sudha. "Spam Mail Detection Using Optimization Techniques." Ingénierie des systèmes d information 27, no. 1 (2022): 157–63. http://dx.doi.org/10.18280/isi.270119.
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On account of the widespread availability of internet access, email correspondence is one among the most well-known cost-effective and convenient method for users in the office and in business. Many people abuse this convenient mode of communication by spamming others with conciseness bulk emails. They use emails to collect personal information of the users to benefit financially. A literature review is conducted to investigate the most effective strategies for achieving successful outcomes while working with various spam mail datasets. K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression are all employed in the implementation of machine learning techniques. To make classifiers more efficient, bio-inspired algorithms such as BAT and PSO are used. The accuracy of every classification algorithm along with and without optimization is observed. Factors such as accuracy, f1-score, precision, and recall are used to compare the results. This work is implemented in Python along with GUI interface Tkinter.
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Curran, Kevin, and John Stephen Honan. "Addressing Spam E-Mail Using Hashcast." International Journal of Business Data Communications and Networking 1, no. 2 (2005): 41–65. http://dx.doi.org/10.4018/jbdcn.2005040103.
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&NA;. "E-mail spam sends a message." Nursing 35, no. 9 (2005): 34. http://dx.doi.org/10.1097/00152193-200509000-00032.
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Ma, Zhiqiang, Rui Yan, Donghong Yuan, and Limin Liu. "An Imbalanced Spam Mail Filtering Method." International Journal of Multimedia and Ubiquitous Engineering 10, no. 3 (2015): 119–26. http://dx.doi.org/10.14257/ijmue.2015.10.3.12.
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GORDILLO, J., and E. CONDE. "An HMM for detecting spam mail☆." Expert Systems with Applications 33, no. 3 (2007): 667–82. http://dx.doi.org/10.1016/j.eswa.2006.06.016.
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Liu, Zhiyi, and Rui Chang. "Collaborative Spam Mail Filtering Model Design." International Journal of Education and Management Engineering 3, no. 2 (2013): 66–71. http://dx.doi.org/10.5815/ijeme.2013.02.11.
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Isik, Sahin, Zuhal Kurt, and Yildiray Anagun. "Spam E-mail Classification Recurrent Neural Networks for Spam E-mail Classification on an Agglutinative Language." International Journal of Intelligent Systems and Applications in Engineering 8, no. 4 (2020): 221–27. http://dx.doi.org/10.18201/ijisae.2020466316.
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Florindi, Emanuele. "Spam e tutela della riservatezza." Informatica e diritto XII, no. 1-2 (2003): 173–96. https://doi.org/10.5281/zenodo.10694807.
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Sommario: 1. Che cos'è lo spam - 2. Una definizione di spam - 3. E-mail e pubblici elenchi - 4. I danni causati dallo spam - 5. I mezzi di difesa - 6. La normativa in materia - 7. Liceità delle cd black list.
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Et. al., T. Poonkodi,. "E-Mail Spam Filtering Through Feature Selection Using Enriched Firefly Optimization Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1248–55. http://dx.doi.org/10.17762/turcomat.v12i5.1791.
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E-mail is the most common method of communication because due to its ability to obtain, the rapid modification of messages and low cost of distribution. Spam causes traffic issues and bottlenecks that limit the amount of memory and bandwidth, power and computing speed. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. A methodology based on Sine- Cosine Algorithm (SCA) introduced which address the problem of space and time complexities are increased in E-Mail spam detection. In this method, WordNet optimized semantic ontology applies different methods based on semantics and similarity measures to reduce the large number of extracted textual features. This paper proposed the Enriched Firefly Optimization Algorithm (EFOA) method effectively selecting suitable features from an upper dimensional space using the fitness function. Once the best feature space is identified through EFOA, the spam classification is done using ANN. Intially, E-mail spam dataset is preprocessed, then the extracted textual features are Semantic-based reduction and Features weights updated using optimized semantic WordNet. The results obtained showed that the ANN classifier after selection of features using EFOA was able to classify e-mails as spam and non-spam. This EFOA demonstrates that the proposed method has led to a remarkable improvement compared to the SCA methods.
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Hnini, Ghizlane, Jamal Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, and Hamid Tairi. "Attention Mechanism and Support Vector Machine for Image-Based E-Mail Spam Filtering." Big Data and Cognitive Computing 7, no. 2 (2023): 87. http://dx.doi.org/10.3390/bdcc7020087.
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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.
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Kumar, Arju, Saurav Kumar, Kishan Kumar, and Dr Bharat Bhushan Naib. "E-mail Fraud Detection." International Journal of Emerging Science and Engineering 11, no. 9 (2023): 1–7. http://dx.doi.org/10.35940/ijese.b7797.0811923.
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Spam issues have become worse on social media platforms and apps with the growth of IoT. To solve the problem, researchers have suggested several spam detection techniques. Spam rates are still high despite the use of anti-spam technologies and tactics, especially given the ubiquity of rogue e-mails that lead to dangerous websites. By using up memory or storage space, spam e-mails may cause servers to run slowly. One of the most essential methods for identifying and eliminating spam is filtering e-mails. To this end, various deep learning and machine learning technologies have been used, including Naive Bayes, decision trees, SVM, and random forest. E-mail and Internet of Things spam filters use various machine learning approaches and systems are categorized in this research. Additionally, as more people use mobile devices and SMS services become more affordable, the issue of spam SMS messages is spreading worldwide. This study suggests using a variety of machine learning approaches to detect and get rid of spam as a solution to this problem. According to the trial findings, the TF-IDF with Random Forest classification algorithm outperformed the other examined algorithms in accuracy %. It is only possible to gauge performance on accuracy since the dataset is imbalanced. Therefore, the algorithms must have good precision, recall, and F-measure.