Web spam classification

2009

Web spamming techniques aim to achieve undeserved rankings in search results. Research has been widely conducted on identifying such spam and neutralizing its influence. However, existing spam detection work only considers current information. We argue that historical web page information may also be important in spam classification. In this paper, we use content features from historical versions of web pages to improve spam classification. We use supervised learning techniques to combine classifiers based on current page content with classifiers based on temporal features. Experiments on the WEBSPAM-UK2007 dataset show that our approach improves spam classification F-measure performance by 30% compared to a baseline classifier which only considers current page content.

ICDS 2012, The Sixth …, 2012

Email spam continues to be a major problem in the Internet. With the spread of malware combined with the power of botnets, spammers are now able to launch large scale spam campaigns causing major traffic increase and leading to enormous economical loss. In this paper, we identify potentially useful email header features for email spam filtering by analyzing publicly available datasets. Then, we use these features as input to several machine learning-based classifiers and compare their performance in filtering email spam. These classifiers are: C4.5 Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perception (MP), Nave Bays (NB), Bayesian Network (BN), and Random Forest (RF). Experimental studies based on publicly available datasets show that RF classifier has the best performance with an average accuracy, precision, recall, F-Measure, ROC area of 98.5%, 98.4%, 98.5%, and 98.5%, respectively.

2008

In the last few years, as Internet usage becomes the main artery of the life's daily activities, the problem of spam becomes very serious for internet community. Spam pages form a real threat for all types of users. This threat proved to evolve continuously without any clue to abate. Different forms of spam witnessed a dramatic increase in both size and negative impact. A large amount of E-mails and web pages are considered spam either in Simple Mail Transfer Protocol (SMTP) or search engines. Many technical methods were proposed to approach the problem of spam. In E-mails spam detection, Bayesian Filters are widely and successfully applied for the sake of detecting and eliminating spam.

Computers & Security, 2014

Web spam is a method of manipulating search engines results by improving ranks of spam pages. It takes various forms and lacks a consistent definition. Web spam detectors use machine learning techniques to detect spam. However, the detectors are mostly verified on data sets coming from the same year as the learning sets. In this paper we compared Support Vector Machine classifiers trained and tested on WEBSPAMeUK data sets from different years. To obtain stable results we proposed new lexical-based features. The HTML document e transformed into a text without HTML tags, a set of visible symbols, and a list of links including the ones from tags e gave information about weird combinations of letters; consonant clusters; statistics on syllables, words, and sentences; and the Gunning Fog Index. Using data collected in 2006 as a learning set, we obtained very stable accuracy among years. This choice of the training set reduced the sensitivity in 2007, but that can be improved by managing the acceptance threshold. Finally, we proved that the balance between the sensitivity and the specificity measured by the Area Under the Curve (AUC) is improved by our selection of features.

2012

The steady growth and popularization of the Web increases the competition between the websites and creates opportunities for profit in several segments. Thus, there is a great interest in keeping the website in a good position in search results. The problem is that many websites use techniques to circumvent the search engines which deteriorates the search results and exposes users to dangerous content. Given this scenario, this paper presents a performance evaluation of different models of artificial neural networks to automatically classify web spam. We have conducted an empirical experiment using a well-known, large and public web spam database. The results indicate that the evaluated approaches outperform the state-of-the-art web spam filters.

International Journal of Communications, Network and System Sciences, 2015

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.

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022

Email is one of the most popular modes of communication we have today. Billions of emails are sent every day in our world but not every one of them is relevant or of importance. The irrelevant and unwanted emails are termed email spam. These spam emails are sent with many different targets that range from advertisement to data theft. Filtering these spam emails is very essential in order to keep the email space fluent in its functioning. Machine Learning algorithms are being extensively used in the classification of spam emails. This paper showcases the performance evaluation of some selected supervised Machine Learning algorithms namely Naive Bayes Classifier, Support Vector Machine, Random Forest, & XG-Boost for spam email classification on a combination of three different datasets. For feature extraction, both Bag of Words & TF-IDF models were used separately and performance with both of these approaches was also compared. The results showed that SVM performed better than all the other algorithms when trained with TF-IDF feature vectors. The performance metrics used were accuracy, precision, recall, and f1-score, along with the ROC curve.

Complex & Intelligent Systems

The increase in people’s use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people’s fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along...

With the increasing growth of Internet and World Wide Web, information retrieval (IR) has attracted much attention in recent years. Quick, accurate and quality information mining is the core concern of successful search companies. Likewise, spammers try to manipulate IR system to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents in search engine result page (SERP). Spammers take advantage of different features of web indexing system for notorious motives. Suitable machine learning approaches can be useful in analysis of spam patterns and automated detection of spam. This paper examines content based features of web documents and discusses the potential of feature selection (FS) in upcoming studies to combat web spam. The objective of feature selection is to select the salient features to improve prediction performance and to understand the underlying data generation techniques. A publically available web data set namely WEBSPAM-UK2007 is used for all evaluations.

In today's digital age, since email is the main form of communication, the identification of email spam is a critical issue. In addition to consuming a lot of time and money, email spam is also a security and privacy risk. In this paper, we provide a means for email spam detection that employes machine learning Algorithms. The required features for training the ML models have been engineered after analysis of the email dataset of contentbased filtering obtained from Kaggle website. We tested a Several types of algorithms for machine learning and analyzed their level of performance using the dataset. Our findings demonstrate how effective is the suggested approach in identifying email spam with highest accuracy of 99.8% and Rmse of 0.2 .Here we applied , the various ML classifier algorithm such as Decision tree , Voting Classifier , Random Forest, Logistic Regression and so on to our dataset ,compared among each other and found which suits best for the dataset with the highest accuracy. This method can be useful in email clients or servers to detect spam emails automatically and enhanced