Tiago A. Almeida

Contributions to the study of SMS spam filtering: new collection and results

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.

Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers

E-mail spam has become an increasingly important problem with a big economic impact in society. Fortunately, there are different approaches allowing to automatically detect and remove most of those messages, and the best-known techniques are based on Bayesian decision theory. However, such probabilistic approaches often suffer from a well-known difficulty: the high dimensionality of the feature space. Many term-selection methods have been proposed for avoiding the curse of dimensionality. Nevertheless, it is still unclear how the performance of Naive Bayes spam filters depends on the scheme applied for reducing the dimensionality of the feature space. In this paper, we study the performance of many term-selection techniques with several different models of Naive Bayes spam filters. Our experiments were diligently designed to ensure statistically sound results. Moreover, we perform an analysis concerning the measurements usually employed to evaluate the quality of spam filters. Finally, we also investigate the benefits of using the Matthews correlation coefficient as a measure of performance.

Content-based spam filtering

The growth of email users has resulted in the dramatic increasing of the spam emails. Helpfully, there are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. However, there are several forms of Naive Bayes filters, something the anti-spam literature does not always acknowledge. In this paper, we discuss seven different versions of Naive Bayes classifiers, and compare them with the well-known Linear Support Vector Machine on six non-encoded datasets. Moreover, we propose a new measurement in order to evaluate the quality of anti-spam classifiers. In this way, we investigate the benefits of using Matthews correlation coefficient as the measure of performance.

Evaluation of Approaches for Dimensionality Reduction Applied with Naive Bayes Anti-Spam Filters

There are different approaches able to automatically detect e-mail spam messages, and the best-known ones are based on Bayesian decision theory. However, the most of these approaches have the same difficulty: the high dimensionality of the feature space. Many term selection methods have been proposed in the literature. Nevertheless, it is still unclear how the performance of naive Bayes anti-spam filters depends on the methods applied for reducing the dimensionality of the feature space. In this paper, we compare the performance of most popular methods used as term selection techniques, such as document frequency, information gain, mutual information, X 2 statistic, and odds ratio used for reducing the dimensionality of the term space with four well-known different versions of naive Bayes spam filter.

Facing the spammers: A very effective approach to avoid junk e-mails

Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, in which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle and confidence factors. The proposed model is fast to construct and incrementally updateable. Furthermore, we have conducted an empirical experiment using three well-known, large and public e-mail databases. The results indicate that the proposed classifier outperforms the state-of-the-art spam filters.

Filtering spams using the minimum description length principle

Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle. The proposed model is fast to construct and incrementally updateable. Additionally, we offer an analysis concerning the measurements usually employed to evaluate the quality of the anti-spam classifiers. In this sense, we present a new measure in order to provide a fairer comparison. Furthermore, we conducted an empirical experiment using six well-known, large and public databases. Finally, the results indicate that our approach outperforms the state-of-the-art spam filters.

Probabilistic anti-spam filtering with dimensionality reduction

One of the biggest problems of e-mail communication is the massive spam message delivery. Everyday, billion of unwanted messages are sent by spammers and this number does not stop growing. Helpfully, there are different approaches able to automatically detect and remove most of these messages, and a well-known ones are based on Bayesian decision theory. However, many machine learning techniques applied to text categorization have the same difficulty: the high dimensionality of the feature space. Many term selection methods have been proposed in the literature. Nevertheless, it is still unclear how the performance of naive Bayes anti-spam filters depends on the methods applied for reducing the dimensionality of the feature space. In this paper, we compare the performance of most popular methods used as term selection techniques with some variations of the original naive Bayes anti-spam filter.

On the Validity of a New SMS Spam Collection

Mobile phones are becoming the latest target of electronic junk mail. Recent reports clearly indicate that the volume of SMS spam messages are dramatically increasing year by year. Probably, one of the major concerns in academic settings was the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. To address this issue, we have recently proposed a new SMS Spam Collection that, to the best of our knowledge, is the largest, public and real SMS dataset available for academic studies. However, as it has been created by augmenting a previously existing database built using roughly the same sources, it is sensible to certify that there are no duplicates coming from them. So, in this paper we offer a comprehensive analysis of the new SMS Spam Collection in order to ensure that this does not happen, since it may ease the task of learning SMS spam classifiers and, hence, it could compromise the evaluation of methods. The analysis of results indicate that the procedure followed does not lead to near-duplicates and, consequently, the proposed dataset is reliable to use for evaluating and comparing the performance achieved by different classifiers.

Advances in Spam Filtering Techniques

Nowadays e-mail spam is not a novelty, but it is still an important rising problem with a big economic impact in society. Fortunately, there are different approaches able to automatically detect and remove most of those messages, and the best-known ones are based on machine learning techniques, such as Naive Bayes classifiers and Support Vector Machines. However, there are several different models of Naive Bayes filters, something the spam literature does not always acknowledge. In this chapter, we present and compare seven different versions of Naive Bayes classifiers, the well-known linear Support Vector Machine and a new method based on the Minimum Description Length principle. Furthermore, we have conducted an empirical experiment on six public and real non-encoded datasets. The results indicate that the proposed filter is easy to implement, incrementally updateable and clearly outperforms the state-of-the-art spam filters.

An Analysis of Machine Learning Methods for Spam Host Detection

The web is becoming an increasingly important source of entertainment, communication, research, news and trade. In this way, the web sites compete to attract the attention of users and many of them achieve visibility through malicious strategies that try to circumvent the search engines. Such sites are known as web spam and they are generally responsible for personal injury and economic losses. Given this scenario, this paper presents a comprehensive performance evaluation of several established machine learning techniques used to automatically detect and filter hosts that disseminate web spam. Our experiments were diligently designed to ensure statistically sounds results and they indicate that bagging of decision trees, multilayer perceptron neural networks, random forest and adaptive boosting of decision trees are promising in the task of web spam classification and, hence, they can be used as a good baseline for further comparison.