Academic literature on the topic 'Unsolicited Bulk e-mail (UBE)'

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.

Internet has become to be an integral part of lifestyles. With multiplied use of internet, numbers of email customers are growing day by day. This growing use of e-mail has created troubles induced. Through unsolicited bulk email messages normally called spam. Electronic mail has now come to be one of the satisfactory methods for commercials due to which junk mail emails are generated. Unsolicited mail emails are the emails that the receiver does not preference to gather. A massive quantity of equal messages is sent to numerous recipients of e-mail. Direct mail usually arises as a result of giving out our email address on an unauthorized or unscrupulous internet website. There are a few of the consequences of junk mail. Fills our Inbox with type of ridiculous emails, that will reduce our internet speed. Steals beneficial records like our info on you contact list. Alters your seek consequences on any laptop software. Junk mail is a huge waste of every body’s time and can quickly turn out to be very frustrating if you get hold of big quantities of it. Figuring out these spammers and the junk mail content is an onerous challenge. Despite the fact that full-size variety of studies were executed, but to this point, the Techniques set forth nevertheless scarcely distinguish spam surveys, and none of them show the Benefits of each eliminated detail compose. Despite increasing network verbal exchange and Losing lot of reminiscence space, spam messages also are used for some attacks.

For most of people, e-mail is the preferable medium for official communication. E-mail service providers face an endless challenge called spamming. Spammingis the exploitation of e-mail systems to send a bulk of unsolicited messages to a large number of recipients. Noisy image spamming is one of the new techniques to evade text analysis based and Optical Character Recognition (OCR) based spams filtering. In the present paper, Convolutional Neural Network (CNN) based on different color models was considered to address image spam problem. The proposed method was evaluated over a public image spam dataset. The results showed that the performance of the proposed CNN was affected by the color model used. The results also showed that XYZ model yields the best accuracy rate among all considered color models.

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.

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.

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.

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.

Функционирование практически любой организации в той или иной степени зависит от того, насколько надёжно защищены её информационные ресурсы от различных угроз безопасности информации, одной из которых является спам. Совершено множество попыток раз и навсегда решить проблему его обнаружения. В данной предметной области постоянно ведутся исследования. По их результатам предлагаются и реализуются на практике различные подходы. Ранее авторами предложена модель электронных писем, учитывающая их содержание, которое зачастую меняется в зависимости от выполняемых пользователями задач и меняющихся их информационных потребностей. В настоящей статье обсуждается вопрос выбора численного значения ключевого параметра модели электронных писем для обнаружения спама, полученной на основе генетического подхода к формированию математических моделей текстов, зарекомендовавшего себя для решения различных задач. Introduction. Currently, one of the most widespread methods of everyday and business communication and management is electronic mail, which has a lot of benefits. In addition to this, e-mail is a mandatory requirement for the use of various digital services, including government one. One of the typical risks associated with its use is spam. It can be reasonably assumed that there has emerged a fairly robust and comprehensive system of methods, models and facilities of spam detection. However, existing approaches to spam detection, typically, do not take into account the changing information needs of a particular user and are not aimed at identifying legitimate messages, which leads to false alarms. Thus, the creation of new models of e-mails that detect the traits of e-mail messages based on their content, taking into account the changing information needs of a particular user (personalization), for the detection of anonymous unsolicited bulk mailings is topical and exhibits a scientific and practical interest. The aim of the research is to choose the value q by carrying out experimental studies. Experiment and evaluation of findings. For each category (class) of mails (legal and spam) of each group of emails, we calculated the sets of terms and determined the coefficient of appertain of each email to legal or spam category, which exhibits the total number of terms contained in the email that were found in the corresponding categories of all groups. The decision on whether an email belongs to spam or legal was made with the use of the simple decision rule - according to the largest total number of terms in the corresponding category. In the case of an equal number of terms for both categories, the email was noted as unclassified. The experiment was carried out on a set of English-language emails, consisting of 6 groups of legal emails with a total amount of 16,100 and 6 groups of spam emails with a total amount of 16,420. There was also used in experiment a set of Russian-language emails, consisting of 3 groups of legal emails with a total amount of 1,242 and 2 groups of spam emails with a total amount of 3,215. Conclusion. The study of the influence of the size of the code chart on the classification of emails showed that the use of the model outputs the best results of the classification of emails with q equal to the number of codes (in the study, q = 256 for the ASCII chart), which are compared to the characters of the text in specific encoding (full code chart, providing the maximum variety of texts), and n = 1. At the same time, depending on the symbolic content of the emails, there are cases when the best result can be observed for n = 2 and other different values ​​of q, which allows to adapt (train) the model to specific texts. This indicates that the model allows to distinguish informative features of emails, taking into account the content of emails of a specific user, which allows to meet personalization of the spam detection process as one of the key properties of spam detection systems, as well as to increase its efficiency. Outcome.There was proposed a model of emails, which allows to specifically detect text segments of emails, which are a reflection of their distinctive features, or terms. The findings indicate the customizability of the model in relation to specific texts of emails, as well as the non-random behavior of the obtained results.

e-mail has become an important means of electronic communication but the viability of its usage is marred by Unsolicited Bulk e-mail (UBE) messages. UBE consists of many types like pornographic, virus infected and 'cry-for-help' messages as well as fake and fraudulent offers for jobs, winnings and medicines. UBE poses technical and socio-economic challenges to usage of e-mails. To meet this challenge and combat this menace, we need to understand UBE. Towards this end, the current paper presents a content-based textual analysis of nearly 3000 winnings-announcing UBE. Technically, this is an application of Text Parsing and Tokenization for an un-structured textual document and we approach it using Bag Of Words (BOW) and Vector Space Document Model techniques. We have attempted to identify the most frequently occurring lexis in the winnings-announcing UBE documents. The analysis of such top 100 lexis is also presented. We exhibit the relationship between occurrence of a word from the identified lexisset in the given UBE and the probability that the given UBE will be the one announcing fake winnings. To the best of our knowledge and survey of related literature, this is the first formal attempt for identification of most frequently occurring lexis in winningsannouncing UBE by its textual analysis. Finally, this is a sincere attempt to bring about alertness against and mitigate the threat of such luring but fake UBE.

e-mail has become an important means of electronic communication but the viability of its usage is marred by Unsolicited Bulk e-mail (UBE) messages. UBE consists of many types like pornographic, virus infected and 'cry-for-help' messages as well as fake and fraudulent offers for jobs, winnings and medicines. UBE poses technical and socio-economic challenges to usage of e-mails. To meet this challenge and combat this menace, we need to understand UBE. Towards this end, the current paper presents a content-based textual analysis of more than 2700 body enhancement medicinal UBE. Technically, this is an application of Text Parsing and Tokenization for an un-structured textual document and we approach it using Bag Of Words (BOW) and Vector Space Document Model techniques. We have attempted to identify the most frequently occurring lexis in the UBE documents that advertise various products for body enhancement. The analysis of such top 100 lexis is also presented. We exhibit the relationship between occurrence of a word from the identified lexis-set in the given UBE and the probability that the given UBE will be the one advertising for fake medicinal product. To the best of our knowledge and survey of related literature, this is the first formal attempt for identification of most frequently occurring lexis in such UBE by its textual analysis. Finally, this is a sincere attempt to bring about alertness against and mitigate the threat of such luring but fake UBE.

In the context of the Internet, spam generally refers to unsolicited and unwanted electronic messages, usually transmitted to a large number of recipients. The problem with spam is that almost all of the related costs are shifted onto the recipients, and many of the messages contain objectionable content. Spam has become a significant problem for network administrators, businesses and individual Internet users that threatens to undermine the usefulness of e-mail. Globally, spam spiralled to account for over 60% of all e-mail near the end of 2004. It is a problem that costs the global economy billions of dollars a year in lost productivity, anti-spam measures and computer resources. It has forced governments to enact legislation against the problem and it has prompted the development of numerous technical countermeasures. Spam can only be defeated by a combination of legal measures, informal measures (including self regulation and social norms), technical measures and consumer education. Because spam is a relatively recent and evolving problem, the application of various common law mechanisms are explored, including the law of privacy and the law of nuisance. Various constitutional concerns may also arise in the context of spam, and the right to freedom of expression must be balanced against other competing rights and values, including the right to privacy. Comparative legislation is examined, because it is important to recognise trends in spam legislation in other jurisdictions so as to ensure a measure of interoperability with those laws. The practical difficulties in identifying spammers, and the lack of jurisdiction over offshore offenders affect the practical implementation of the current protection offered by the ECT Act. In conclusion, this thesis identifies the need for direct anti-spam legislation in South Africa, and suggests various clauses that will need to be catered for in the legislation. It is submitted that "opt-in" legislation should be preferred over "opt-out" legislation. It is further submitted that a definition of spam should be based on the volume and indiscriminate nature of the e-mail, and not only on whether the communication was commercial. Therefore, a definition of bulk unsolicited e-mail is proposed.<br>Criminal & Procedural Law<br>LLD

The practice of spamming (sending unsolicited electronic communications) has been dubbed “the scourge of the 21st century” affecting different stakeholders. This practice is also credited for not only disrupting electronic communications but also, it overloads electronic systems and creates unnecessary costs for those affected than the ones responsible for sending such communications. In trying to address this issue nations have implemented anti-spam laws to combat the scourge. South Africa not lagging behind, has put in place anti-spam provisions to deal with the scourge. The anti-spam provisions are scattered in pieces of legislation dealing with diverse issues including: consumer protection; direct marketing; credit laws; and electronic transactions and communications. In addition to these provisions, an Amendment Bill to one of these laws and two Bills covering cybercrimes and cyber-security issues have been published. In this thesis, a question is asked on whether the current fragmented anti-spam provisions are adequate in protecting consumers. Whether the overlaps between these pieces of legislation are competent to deal with the ever increasing threats on electronic communications at large. Finally, the question as to whether a multi-faceted approach, which includes a Model Law on spam would be a suitable starting point setting out requirements for the sending of unsolicited electronic communications can be sufficient in protecting consumers. And as spam is not only a national but also a global problem, South Africa needs to look at the option of entering into mutual agreements with other countries and organisations in order to combat spam at a global level.<br>Mercantile Law<br>LL. D.