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Most published Xplore authors for Unsolicited E-mail

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Xplore Articles related to Unsolicited E-mail

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Filtering spam messages and mails using fuzzy C means algorithm

2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 2019

Advancement in computer technology has changed the world in many different ways. Communication is just a click away with the power of internet. For effective, low cost and fast communication between people email plays a very important role and thus there is a great need of email services in daily life of users. From all the transactions to business or ...


The Document Similarity Index based on the Jaccard Distance for Mail Filtering

2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2019

We propose a new index of similarity for classification of emails into ham and spam ones with the Jaccard index. It takes advantage of co-occurrence value of all pairs of two words in emails. The co-occurrence of words represents a sort of context in documents because a word is often in use with another word in the same context. Our ...


BGP hijacking classification

2019 Network Traffic Measurement and Analysis Conference (TMA), 2019

Recent reports show that BGP hijacking has increased substantially. BGP hijacking allows malicious ASes to obtain IP prefixes for spamming as well as intercepting or blackholing traffic. While systems to prevent hijacks are hard to deploy and require the cooperation of many other organizations, techniques to detect hijacks have been a popular area of study. In this paper, we classify ...


SmartTech: An Email Analytics Application

2018 International Conference on System Modeling & Advancement in Research Trends (SMART), 2018

In the time of social media, email and SMS marketing might sound old school but these two are the most credible and profitable tool from digital marketing. Promoting a business by sending emails and newsletters is what we call email marketing. Similarly SMS marketing is sending SMS for promoting business. In addition to earning income, email is highly valued for ...


Data Mining Techniques for Fraud Detection in Banking Sector

2018 4th International Conference on Computing Communication and Automation (ICCCA), 2018

Banking sector is having a great significance or value in our everyday life. Each and every person makes the use of banking sector in two ways, (i) physical and (ii) online. Physical fraud can take place like stealing the credit cards, sharing bank account details with corrupt bank employees, etc. Online fraud takes place by sharing the card details on ...



Educational Resources on Unsolicited E-mail

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IEEE-USA E-Books

  • Filtering spam messages and mails using fuzzy C means algorithm

    Advancement in computer technology has changed the world in many different ways. Communication is just a click away with the power of internet. For effective, low cost and fast communication between people email plays a very important role and thus there is a great need of email services in daily life of users. From all the transactions to business or general communication these are done through the help of emails. But often the communication is effected by the attacks on the email system which include spam mails. Spamming is the use of messaging or electronic messaging system that send huge amount of data. Spam often fills the internet with multiple copies of a message and are sent to different recipients repeatedly without their request and urges to open them. In this paper we analyze different machine learning techniques with feature selection and without feature selection algorithms and their performance to detect the best classifier for spam mail classification. First, we apply each classifier without selecting any features in order to experiment on the dataset and examine the outcome. Next, to select the desired features we apply best first feature selection algorithm and apply various algorithms for classification. We found that the accuracy has improved when we applied feature selection process in the experimentation.

  • The Document Similarity Index based on the Jaccard Distance for Mail Filtering

    We propose a new index of similarity for classification of emails into ham and spam ones with the Jaccard index. It takes advantage of co-occurrence value of all pairs of two words in emails. The co-occurrence of words represents a sort of context in documents because a word is often in use with another word in the same context. Our proposed method classified emails into hams or spams with high accuracy rate than the present filtering system using appearance frequency of word. Our method could extract patterns of word usage reflecting the context of emails.

  • BGP hijacking classification

    Recent reports show that BGP hijacking has increased substantially. BGP hijacking allows malicious ASes to obtain IP prefixes for spamming as well as intercepting or blackholing traffic. While systems to prevent hijacks are hard to deploy and require the cooperation of many other organizations, techniques to detect hijacks have been a popular area of study. In this paper, we classify detected hijack events in order to document BGP detectors output and understand the nature of reported events. We introduce four categories of BGP hijack: typos, prepending mistakes, origin changes, and forged AS paths. We leverage AS hegemony - a measure of dependency in AS relationship - to identify forged AS paths in a fast and efficient way. Besides, we utilize heuristic approaches to find common operators' mistakes such as typos and AS prepending mistakes. The proposed approach classifies our collected ground truth into four categories with 95.71% accuracy. We characterize publicly reported alarms (e.g. BGPMon) with our trained classifier and find 4%, 1%, and 2% of typos, prepend mistakes, and BGP hijacking with a forged AS path, respectively.

  • SmartTech: An Email Analytics Application

    In the time of social media, email and SMS marketing might sound old school but these two are the most credible and profitable tool from digital marketing. Promoting a business by sending emails and newsletters is what we call email marketing. Similarly SMS marketing is sending SMS for promoting business. In addition to earning income, email is highly valued for its ability to quickly, and measure results in improving and personalizing customers. More advantages are the ease of use and the fact that emails are on the list of the most common forms of communication used by almost everyone. Many marketing trends come and go, but email and SMS still exist. Three times as many e-mail accounts, such as Facebook and Twitter accounts. Many leading brands among the most successful companies around the world believe that email is a channel for expanding your business, and SMS offers excellent support. Email and SMS marketing are the most effective ways to attract, participate, and interact with participants to increase sales and profits.

  • Data Mining Techniques for Fraud Detection in Banking Sector

    Banking sector is having a great significance or value in our everyday life. Each and every person makes the use of banking sector in two ways, (i) physical and (ii) online. Physical fraud can take place like stealing the credit cards, sharing bank account details with corrupt bank employees, etc. Online fraud takes place by sharing the card details on the Internet or over the phone with a wrong person. It may also include spamming and phishing. While carrying out the transactions and all the relations with the bank policies, customers and the banks may face many problems due to fraudsters and criminals, and the chances of getting trapped are very higher. These kinds of frauds can be credit card fraud, insurance fraud, accounting fraud, etc. which may lead to the financial loss to the bank or the customers. Thus, detection of these kinds of frauds are very important. Fraud detection in banking sector is based on the data mining techniques and their collective analysis from the past experiences and the probability of how the fraudsters can steal from customers and banks. Therefore this paper addresses the analysis of data mining techniques of how to detect frauds and overcoming it in banking sector.

  • Automatic Email Spam Detection using Genetic Programming with SMOTE

    Being one of the major communication ways on the Internet, the emailing systems need to be protected from spam which represents unsolicited messages with serious threats to both individual users and organizations. Realizing this issue, it is an imperious necessity to develop more accurate and effective spam detection models for the emailing platforms. In this paper, an efficient email spam detection model based on Genetic Programming (GP) combined with Synthetic Minority Over-sampling Technique (SMOTE) is proposed to detect spam emails. The model is applied and tested on two benchmark email corpora and tested against four other well-recognized classifiers using four measures; accuracy, recall, precision and G-mean. Experimental results show that GP combined with SMOTE can effectively classify spam emails outperforming the usual classification methods.

  • The Characteristics of the Information Protection Systems Design for Corporate Information Systems

    The article is devoted to the aspects of smaller information systems, their modern features and the possibility of working with limited resources allocated for information protection. The article analyzes the security of the system prior to the implementation of the proposed security policy based on the methodology for analyzing threats and vulnerabilities. The authors propose an approach to reduce the risks associated with the probability of loss, distortion, and data compromise. On the basis of the research, the benefits associated with the implementation of the proposed measures are shown.

  • Neural Network Spam Filtering Technology

    In this paper we solve the problem of neural network technology development for e-mail messages classification. We analyze basic methods of spam filtering such as a sender IP-address analysis, spam messages repeats detection and the Bayesian filtering according to words. We offer the neural network technology for solving this problem because the neural networks are universal approximators and effective in addressing the problems of classification. Also, we offer the scheme of this technology for e-mail messages “spam”/“not spam” classification. The creation of effective neural network model of spam filtering is performed within the databases knowledge discovery technology. For this training set is formed, the neural network model is trained, its value and classifying ability are estimated. The experimental studies have shown that a developed artificial neural network model is adequate and it can be effectively used for the e-mail messages classification. Thus, in this paper we have shown the possibility of the effective neural network model use for the e-mail messages filtration and have shown a scheme of artificial neural network model use as a part of the e-mail spam filtering intellectual system.

  • E-Mail Classification Using Natural Language Processing

    Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.

  • Spam Domain Detection Method Using Active DNS Data and E-Mail Reception Log

    E-mail is widespread and an essential communication technology in modern times. Since e-mail has problems with spam mails and spoofed e-mails, countermeasures are required. Although SPF, DKIM and DMARC have been proposed as sender domain authentication, these mechanisms cannot detect non-spoofing spam mails. To overcome this issue, this paper proposes a method to detect spam domains by supervised learning with features extracted from e-mail reception log and active DNS data, such as the result of Sender Authentication, the Sender IP address, the number of each DNS record, and so on. As a result of the experiment, our method can detect spam domains with 88.09% accuracy and 97.11% precision. We confirmed that our method can detect spam domains with detection accuracy 19.40% higher than the previous study by utilizing not only active DNS data but also e-mail reception log in combination.



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