Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measure...Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The Bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved Bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.展开更多
Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic ...Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory algorithms.However,they do not consider the document when analyzing spam mail content.These approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency.Because there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents together.Thus,spam mail is not classified fully,and these approaches remain with loopholes.Thus,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s topic.Moreover,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model.Our proposed approach also reduces the dimensionality,which ultimately increases the strength of document classification.Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation.Comparative evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.展开更多
Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not o...Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes much of the bandwidth of the Internet. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen, while spam reorganization has different properties comparing with normal text reorganization. Presented are three different classifiers are combined with detailed analysis on various training data set of the given spam database. These classifiers are combined into a mixture of experts (MOE) system which yields overall better performance than any of the individual contributors. The instructions for further improvements on classifiers as well as its requirement on spam databases are also given.展开更多
Spam is no longer just commercial unsolicited email messages that waste our time, it consumes network traffic and mail servers’ storage. Furthermore, spam has become a major component of several attack vectors includ...Spam is no longer just commercial unsolicited email messages that waste our time, it consumes network traffic and mail servers’ storage. Furthermore, spam has become a major component of several attack vectors including attacks such as phishing, cross-site scripting, cross-site request forgery and malware infection. Statistics show that the amount of spam containing malicious contents increased compared to the one advertising legitimate products and services. In this paper, the issue of spam detection is investigated with the aim to develop an efficient method to identify spam email based on the analysis of the content of email messages. We identify a set of features that have a considerable number of malicious related features. Our goal is to study the effect of these features in helping the classical classifiers in identifying spam emails. To make the problem more challenging, we developed spam classification models based on imbalanced data where spam emails form the rare class with only 16.5% of the total emails. Different metrics were utilized in the evaluation of the developed models. Results show noticeable improvement of spam classification models when trained by dataset that includes malicious related features.展开更多
In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is giv...In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.展开更多
Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is success...Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Reasoning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not applied to this area yet.展开更多
文摘Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The Bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved Bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.
文摘Spam mail classification considered complex and error-prone task in the distributed computing environment.There are various available spam mail classification approaches such as the naive Bayesian classifier,logistic regression and support vector machine and decision tree,recursive neural network,and long short-term memory algorithms.However,they do not consider the document when analyzing spam mail content.These approaches use the bagof-words method,which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency.Because there are many words in a document,these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents together.Thus,spam mail is not classified fully,and these approaches remain with loopholes.Thus,we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s topic.Moreover,the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model.Our proposed approach also reduces the dimensionality,which ultimately increases the strength of document classification.Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation.Comparative evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment,with higher document word frequencies of 97.05%,99.17%and 96.59%.
文摘Spam, the electronic equivalent of junk mail, affects over 600 million users worldwide. The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes much of the bandwidth of the Internet. Even as anti-spam solutions change to limit the amount of spam sent to users, the senders adapt to make sure their messages are seen, while spam reorganization has different properties comparing with normal text reorganization. Presented are three different classifiers are combined with detailed analysis on various training data set of the given spam database. These classifiers are combined into a mixture of experts (MOE) system which yields overall better performance than any of the individual contributors. The instructions for further improvements on classifiers as well as its requirement on spam databases are also given.
文摘Spam is no longer just commercial unsolicited email messages that waste our time, it consumes network traffic and mail servers’ storage. Furthermore, spam has become a major component of several attack vectors including attacks such as phishing, cross-site scripting, cross-site request forgery and malware infection. Statistics show that the amount of spam containing malicious contents increased compared to the one advertising legitimate products and services. In this paper, the issue of spam detection is investigated with the aim to develop an efficient method to identify spam email based on the analysis of the content of email messages. We identify a set of features that have a considerable number of malicious related features. Our goal is to study the effect of these features in helping the classical classifiers in identifying spam emails. To make the problem more challenging, we developed spam classification models based on imbalanced data where spam emails form the rare class with only 16.5% of the total emails. Different metrics were utilized in the evaluation of the developed models. Results show noticeable improvement of spam classification models when trained by dataset that includes malicious related features.
文摘In the recent years spam became as a big problem of Internet and electronic communication. There developed a lot of techniques to fight them. In this paper the overview of existing e-mail spam filtering methods is given. The classification, evaluation, and comparison of traditional and learning-based methods are provided. Some personal anti-spam products are tested and compared. The statement for new approach in spam filtering technique is considered.
文摘Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Reasoning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not applied to this area yet.
基金山东省自然科学基金(the Natural Science Foundation of Shandong Province of China under Grant No.Y2006G20)山东省科技公关计划(the Key Technologies R&D Program of Shandong Province of China under Grant No.053120126)
