Biblio
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.
An important ingredient for a successful recipe for solving machine learning problems is the availability of a suitable dataset. However, such a dataset may have to be extracted from a large unstructured and semi-structured data like programming code, scripts, and text. In this work, we propose a plug-in based, extensible feature extraction framework for which we have prototyped as a tool. The proposed framework is demonstrated by extracting features from two different sources of semi-structured and unstructured data. The semi-structured data comprised of web page and script based data whereas the other data was taken from email data for spam filtering. The usefulness of the tool was also assessed on the aspect of ease of programming.
E-mail communication is one of today's indispensable communication ways. The widespread use of email has brought about some problems. The most important one of these problems are spam (unwanted) e-mails, often composed of advertisements or offensive content, sent without the recipient's request. In this study, it is aimed to analyze the content information of e-mails written in Turkish with the help of Naive Bayes Classifier and Vector Space Model from machine learning methods, to determine whether these e-mails are spam e-mails and classify them. Both methods are subjected to different evaluation criteria and their performances are compared.
While email plays a growingly important role on the Internet, we are faced with more severe challenges brought by compromised email accounts, especially for the administrators of institutional email service providers. Inspired by the previous experience on spam filtering and compromised accounts detection, we propose several criteria, like Success Outdegree Proportion, Reverse Pagerank, Recipient Clustering Coefficient and Legitimate Recipient Proportion, for compromised email accounts detection from the perspective of graph topology in this paper. Specifically, several widely used social network analysis metrics are used and adapted according to the characteristics of mail log analysis. We evaluate our methods on a dataset constructed by mining the one month (30 days) mail log from an university with 118,617 local users and 11,460,399 mail log entries. The experimental results demonstrate that our methods achieve very positive performance, and we also prove that these methods can be efficiently applied on even larger datasets.
Spam Filtering is an adversary application in which data can be purposely employed by humans to attenuate their operation. Statistical spam filters are manifest to be vulnerable to adversarial attacks. To evaluate security issues related to spam filtering numerous machine learning systems are used. For adversary applications some Pattern classification systems are ordinarily used, since these systems are based on classical theory and design approaches do not take into account adversarial settings. Pattern classification system display vulnerabilities (i.e. a weakness that grants an attacker to reduce assurance on system's information) to several potential attacks, allowing adversaries to attenuate their effectiveness. In this paper, security evaluation of spam email using pattern classifier during an attack is addressed which degrade the performance of the system. Additionally a model of the adversary is used that allows defining spam attack scenario.