This document discusses web spam detection using machine learning techniques. Specifically, it proposes an improved Naive Bayes classifier that incorporates user feedback and domain-specific features to better detect spam pages. The key points are:
1) Web spam has become a serious problem as internet usage has increased, threatening search engines and users. Spam pages aim to deceive search engines' ranking algorithms.
2) Existing spam detection techniques like content analysis are still lacking and Naive Bayes classifiers are commonly used but have limitations like treating all terms equally.
3) The paper proposes an improved Naive Bayes classifier that assigns different weights to terms based on user feedback and considers domain-specific features to reduce false positives and negatives and improve accuracy
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. 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. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
This document discusses email spam detection. It begins with the objectives of classifying emails as spam or ham (legitimate emails) and providing users knowledge about fake vs real emails. It introduces spam as unsolicited commercial email involving mass mailing, explains how spammers obtain emails and the spam lifecycle. The document also outlines different types of spam filters including header, Bayesian, permission and content filters. It provides a flowchart of the email processing and preprocessing steps like removing stop words and lemmatization (grouping word forms). Finally, it discusses the scope of the project, including increased security and reduced costs.
The document presents a presentation on spam email detection. It introduces the topic and defines spam emails. It then discusses using a naïve Bayes classifier to classify emails as spam or not spam based on feature vectors. It identifies problems caused by spam emails and the objectives of detection. It reviews literature on spam prevention. The document outlines the project scope, requirements, feasibility study, testing approach using datasets, and output. It concludes that the method can effectively classify emails but may have limitations when dealing with large volumes of emails.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. 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. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
This document discusses email spam detection. It begins with the objectives of classifying emails as spam or ham (legitimate emails) and providing users knowledge about fake vs real emails. It introduces spam as unsolicited commercial email involving mass mailing, explains how spammers obtain emails and the spam lifecycle. The document also outlines different types of spam filters including header, Bayesian, permission and content filters. It provides a flowchart of the email processing and preprocessing steps like removing stop words and lemmatization (grouping word forms). Finally, it discusses the scope of the project, including increased security and reduced costs.
The document presents a presentation on spam email detection. It introduces the topic and defines spam emails. It then discusses using a naïve Bayes classifier to classify emails as spam or not spam based on feature vectors. It identifies problems caused by spam emails and the objectives of detection. It reviews literature on spam prevention. The document outlines the project scope, requirements, feasibility study, testing approach using datasets, and output. It concludes that the method can effectively classify emails but may have limitations when dealing with large volumes of emails.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
An Approach for Malicious Spam Detection in Email with Comparison of Differen...IRJET Journal
This document summarizes a research paper that proposes a model to improve detection of malicious spam emails through feature selection. The model employs a novel dataset for feature selection to optimize classification parameters, prediction accuracy, and computation time. Feature selection is expected to improve training time and classification accuracy. The paper also compares various classifiers, including Naive Bayes and Support Vector Machine, on the selected feature subset. The goal is to automatically learn to detect malicious spam emails, which threaten privacy and security by spreading malware, phishing links, and sensitive data theft.
SPAM refers to sending unwanted messages in large quantities to many recipients. There are several types of spam, including email spam, social networking spam, blog spam, SMS spam, forum spam, video sharing site spam, and online game messaging spam. Spam is commonly used to advertise dubious products, schemes, or services. To protect from spam, maintain separate public and private email addresses, do not respond to or unsubscribe from spam messages, use strong anti-spam software, update browsers and security patches, and avoid posting email addresses on suspicious websites.
The document discusses spam, including its definition as unsolicited commercial email, statistics on its prevalence, and various types like email spam, web search engine spam, image spam, and blank spam. It also covers how spammers earn money, techniques for sending spam like using botnets and open relays, and anti-spam techniques like using filters and reporting spam messages. The conclusion emphasizes staying informed about spam characteristics to protect systems and data from potential dangers.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
Spam emails are a huge problem, with around 130 billion spam emails sent daily. The United States and China account for about 40% of all spam. Spammers obtain email addresses in many ways, like harvesting them from public websites or infecting computers to access address books. To reduce spam, the document recommends using a disposable email address, carefully reviewing privacy policies before sharing your email, reporting spam emails, and not posting your email publicly or opening suspicious attachments. Some anti-spam tools and services mentioned are Mailinator, Spamihilator, MailWasher, and SPAMfighter.
Analysis of an image spam in email based on content analysisijnlc
Researchers initially have addressed the problem of spam detection as a text classification or
categorization problem. However, as spammers’ continue to develop new techniques and the type of email
content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in
preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently
adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind
this project is to design a spam detection system. The system will be enabled to analyze the content of
emails, in particular the artificially generated image sent as attachment in an email. The system will
analyze the image content and classify the embedded image as spam or legitimate hence classify the email
accordingly.
This document provides an introduction and overview of text analytics for SMS spam filtering classification. It discusses the classification of spam and ham SMS, describes the company Sky Bits Technology which focuses on analytics solutions, and performs Porter's Five Forces and SWOT analyses of the analytics industry and company. It also covers basic concepts in text mining such as preprocessing, transformation, feature selection, and classification methods. The objective is to develop a text classification model using R Studio to automatically categorize SMS as spam or ham.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
This document discusses techniques for detecting and filtering email spam. It begins with an introduction to the growing problem of spam emails and outlines some common characteristics of spam messages like sender information, word usage, and content. It then describes several techniques used for spam filtering, including traditional classification approaches like Naive Bayes and Support Vector Machines, ontology-based methods, graph mining approaches, and neural network methods. Specific algorithms discussed in more detail include Naive Bayes, k-means clustering, and neural networks. The document concludes that machine learning methods show promise for improving spam filtering but that spam remains a significant challenge for internet users and systems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
Naïve Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks.
Typical applications include filtering spam, classifying documents, sentiment prediction etc.
Naïve Bayes classifiers are a popular statistical technique of email-filtering.
Bayes Rule P (H | E) = (P (E | H))/(P (E)) × P (H).
AutoRE is a software developed by Microsoft to detect spam emails generated by botnets. It combines content-based and non-content-based detection methods. It first pre-processes URLs from emails, groups similar URLs into domains, and generates domain-agnostic regular expressions to identify patterns. This allows it to detect botnets even if they change domains. AutoRE's analysis of botnet characteristics informed future related work on real-time reputation systems and large-scale botnet detection using behavior analysis and IP address distribution. However, AutoRE itself was not fully implemented in real-time.
The document discusses spam filtering techniques. It defines spam as unsolicited bulk electronic messages, especially advertising. It describes different types of spam like email, comment, instant messaging, junk fax, and text messages. It then discusses current spam filtering works like Bayesian filtering models and other machine learning approaches. It proposes a collaborative intelligence approach to warn users of potential spam messages. Finally, it provides references on spam statistics and filtering techniques.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% sent using networks of virus-infected computers. The legal status of spam varies by jurisdiction, though in the US it is legal if it meets certain specifications under the CAN-SPAM Act of 2003. Spam now averages 78% of all email sent and costs businesses billions each year.
GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAMieijjournal
Google has released many algorithm updates to combat web spam over the years:
- Updates like Panda and Penguin specifically target low-quality sites, thin content sites, and sites engaging in unethical SEO practices like link spamming.
- Other updates like Caffeine and Social Signals aim to incorporate more authoritative signals like social media mentions and site freshness to improve results.
- Google's goal is to balance economic incentives for spammers by keeping costs high for manipulation, while continually adapting their algorithms to mitigate new spam tactics.
The document presents a taxonomy of web spamming techniques used to mislead search engines into ranking certain pages higher in search results. It discusses techniques like term spamming, which involves optimizing text fields like titles, meta tags, and anchor text to increase relevance for targeted queries. It also covers link spamming techniques, where spammers create link structures to influence importance scores. The taxonomy is intended to help researchers understand spamming methods in order to develop effective countermeasures against degraded search quality.
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
An Approach for Malicious Spam Detection in Email with Comparison of Differen...IRJET Journal
This document summarizes a research paper that proposes a model to improve detection of malicious spam emails through feature selection. The model employs a novel dataset for feature selection to optimize classification parameters, prediction accuracy, and computation time. Feature selection is expected to improve training time and classification accuracy. The paper also compares various classifiers, including Naive Bayes and Support Vector Machine, on the selected feature subset. The goal is to automatically learn to detect malicious spam emails, which threaten privacy and security by spreading malware, phishing links, and sensitive data theft.
SPAM refers to sending unwanted messages in large quantities to many recipients. There are several types of spam, including email spam, social networking spam, blog spam, SMS spam, forum spam, video sharing site spam, and online game messaging spam. Spam is commonly used to advertise dubious products, schemes, or services. To protect from spam, maintain separate public and private email addresses, do not respond to or unsubscribe from spam messages, use strong anti-spam software, update browsers and security patches, and avoid posting email addresses on suspicious websites.
The document discusses spam, including its definition as unsolicited commercial email, statistics on its prevalence, and various types like email spam, web search engine spam, image spam, and blank spam. It also covers how spammers earn money, techniques for sending spam like using botnets and open relays, and anti-spam techniques like using filters and reporting spam messages. The conclusion emphasizes staying informed about spam characteristics to protect systems and data from potential dangers.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
Spam emails are a huge problem, with around 130 billion spam emails sent daily. The United States and China account for about 40% of all spam. Spammers obtain email addresses in many ways, like harvesting them from public websites or infecting computers to access address books. To reduce spam, the document recommends using a disposable email address, carefully reviewing privacy policies before sharing your email, reporting spam emails, and not posting your email publicly or opening suspicious attachments. Some anti-spam tools and services mentioned are Mailinator, Spamihilator, MailWasher, and SPAMfighter.
Analysis of an image spam in email based on content analysisijnlc
Researchers initially have addressed the problem of spam detection as a text classification or
categorization problem. However, as spammers’ continue to develop new techniques and the type of email
content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in
preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently
adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind
this project is to design a spam detection system. The system will be enabled to analyze the content of
emails, in particular the artificially generated image sent as attachment in an email. The system will
analyze the image content and classify the embedded image as spam or legitimate hence classify the email
accordingly.
This document provides an introduction and overview of text analytics for SMS spam filtering classification. It discusses the classification of spam and ham SMS, describes the company Sky Bits Technology which focuses on analytics solutions, and performs Porter's Five Forces and SWOT analyses of the analytics industry and company. It also covers basic concepts in text mining such as preprocessing, transformation, feature selection, and classification methods. The objective is to develop a text classification model using R Studio to automatically categorize SMS as spam or ham.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
This document discusses techniques for detecting and filtering email spam. It begins with an introduction to the growing problem of spam emails and outlines some common characteristics of spam messages like sender information, word usage, and content. It then describes several techniques used for spam filtering, including traditional classification approaches like Naive Bayes and Support Vector Machines, ontology-based methods, graph mining approaches, and neural network methods. Specific algorithms discussed in more detail include Naive Bayes, k-means clustering, and neural networks. The document concludes that machine learning methods show promise for improving spam filtering but that spam remains a significant challenge for internet users and systems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
Naïve Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks.
Typical applications include filtering spam, classifying documents, sentiment prediction etc.
Naïve Bayes classifiers are a popular statistical technique of email-filtering.
Bayes Rule P (H | E) = (P (E | H))/(P (E)) × P (H).
AutoRE is a software developed by Microsoft to detect spam emails generated by botnets. It combines content-based and non-content-based detection methods. It first pre-processes URLs from emails, groups similar URLs into domains, and generates domain-agnostic regular expressions to identify patterns. This allows it to detect botnets even if they change domains. AutoRE's analysis of botnet characteristics informed future related work on real-time reputation systems and large-scale botnet detection using behavior analysis and IP address distribution. However, AutoRE itself was not fully implemented in real-time.
The document discusses spam filtering techniques. It defines spam as unsolicited bulk electronic messages, especially advertising. It describes different types of spam like email, comment, instant messaging, junk fax, and text messages. It then discusses current spam filtering works like Bayesian filtering models and other machine learning approaches. It proposes a collaborative intelligence approach to warn users of potential spam messages. Finally, it provides references on spam statistics and filtering techniques.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% sent using networks of virus-infected computers. The legal status of spam varies by jurisdiction, though in the US it is legal if it meets certain specifications under the CAN-SPAM Act of 2003. Spam now averages 78% of all email sent and costs businesses billions each year.
GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAMieijjournal
Google has released many algorithm updates to combat web spam over the years:
- Updates like Panda and Penguin specifically target low-quality sites, thin content sites, and sites engaging in unethical SEO practices like link spamming.
- Other updates like Caffeine and Social Signals aim to incorporate more authoritative signals like social media mentions and site freshness to improve results.
- Google's goal is to balance economic incentives for spammers by keeping costs high for manipulation, while continually adapting their algorithms to mitigate new spam tactics.
The document presents a taxonomy of web spamming techniques used to mislead search engines into ranking certain pages higher in search results. It discusses techniques like term spamming, which involves optimizing text fields like titles, meta tags, and anchor text to increase relevance for targeted queries. It also covers link spamming techniques, where spammers create link structures to influence importance scores. The taxonomy is intended to help researchers understand spamming methods in order to develop effective countermeasures against degraded search quality.
This document discusses web spam and techniques used to mislead search engines. It defines web spam as actions intended to boost a page's ranking without improving its true value. Two main categories of techniques are described: boosting techniques like term spamming and link spamming; and hiding techniques like content hiding and cloaking to conceal spamming from users and search engines. Specific spamming methods like term repetition, link exchanges, and cloaking behavior are outlined. The goal of web spammers and the algorithms search engines use for ranking are also summarized.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
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.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM cscpconf
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.
Low Cost Page Quality Factors To Detect Web Spamieijjournal
This document presents 32 low-cost page quality factors for detecting web spam pages that can be classified into three categories: URL features, content features, and link features. An experiment was conducted using these factors as inputs to a neural network classifier based on resilient backpropagation learning. The classifier achieved 92% accuracy, 91% efficiency, 93% precision, and 90% F1 score when using all 32 factors, demonstrating their effectiveness at detecting web spam with low computational requirements.
Low Cost Page Quality Factors To Detect Web Spamieijjournal
Web spam is a big challenge for quality of search engine results. It is very important for search engines to detect web spam accurately. In this paper we present 32 low cost quality factors to classify spam and ham pages on real time basis. These features can be divided in to three categories: (i) URL features, (ii) Content features, and (iii) Link features. We developed a classifier using Resilient Back-propagation learning algorithm of neural network and obtained good accuracy. This classifier can be applied to search engine results on real time because calculation of these features require very little CPU resources.
Low Cost Page Quality Factors To Detect Web Spam ieijjournal
Web spam is a big challenge for quality of search engine results. It is very important for search engines to detect web spam accurately. In this paper we present 32 low cost quality factors to classify spam and ham pages on real time basis. These features can be divided in to three categories: (i) URL features, (ii) Content features, and (iii) Link features. We developed a classifier using Resilient Back-propagation learning
algorithm of neural network and obtained good accuracy. This classifier can be applied to search engine results on real time because calculation of these features require very little CPU resources.
A SURVEY ON WEB SPAM DETECTION METHODS: TAXONOMYIJNSA Journal
Web spam refers to some techniques, which try to manipulate search engine ranking algorithms in order to raise web page position in search engine results. In the best case, spammers encourage viewers to visit their sites, and provide undeserved advertisement gains to the page owner. In the worst case, they use malicious contents in their pages and try to install malware on the victim’s machine. Spammers use three kinds of spamming techniques to get higher score in ranking. These techniques are Link based techniques, hiding techniques and Content-based techniques. Existing spam pages cause distrust to search engine results. This not only wastes the time of visitors, but also wastes lots of search engine resources. Hence spam detection methods have been proposed as a solution for web spam in order to reduce negative effects of spam pages. Experimental results show that some of these techniques are
working well and can find spam pages more accurate than the others. This paper classifies web spam techniques and the related detection methods.
State of the Art Analysis Approach for Identification of the Malignant URLsIOSRjournaljce
Malicious URLs have been universally used to ascend various cyber attacks including spamming, phishing and malware. Malware, short term for malicious software, is software which is developed to penetrate computers in a network without the user’s permission or notification. Existing methods typically detect malicious URLs of a single attack type. Hence such detection systems are failed to protect the users from various attacks. Malware spreading widely throughout the area of network as consequence of this it becomes predicament in distributed computer and network systems. Malicious links are the place of origin of all attacks which circulated all over the web. Hence malicious URLs should be detected for the prevention of users from these malware attacks. In this paper we described a novel approach which analyze all types of attacks by identifying malicious URLs and secure the web users from them. This technique prevents the users from malignant URLs before visiting them. Therefore efficiency of web security gets maintained. For such anatomization we developed an analyzer which identifies URLs and examine as malicious or benign. We also developed five processes which crawl for suspicious URLs. This approach will prevent the users from all types of attacks and increase efficiency of web crawling phase.
A Deep Learning Technique for Web Phishing Detection Combined URL Features an...IJCNCJournal
The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING H...ijnlc
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on ecommerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
DEVELOPING PRODUCTS UPDATE-ALERT SYSTEM FOR E-COMMERCE WEBSITES USERS USING ...kevig
Websites are regarded as domains of limitless information which anyone and everyone can access. The
new trend of technology has shaped the way we do and manage our businesses. Today, advancements in
Internet technology has given rise to the proliferation of e-commerce websites. This, in turn made the
activities and lifestyles of marketers/vendors, retailers and consumers (collectively regarded as users in
this paper) easier as it provides convenient platforms to sale/order items through the internet.
Unfortunately, these desirable benefits are not without drawbacks as these platforms require that the users
spend a lot of time and efforts searching for best product deals, products updates and offers on e-
commerce websites. Furthermore, they need to filter and compare search results by themselves which takes
a lot of time and there are chances of ambiguous results. In this paper, we applied web crawling and
scraping methods on an e-commerce website to obtain HTML data for identifying products updates based
on the current time. These HTML data are preprocessed to extract details of the products such as name,
price, post date and time, etc. to serve as useful information for users.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
Significant factors for Detecting Redirection Spamijtsrd
This document discusses significant factors for detecting redirection spam. Redirection spam refers to techniques where users are redirected through multiple domains and pages until reaching a compromised page. The document reviews related work on redirection spam detection and identifies new factors that could help detect malicious redirections more accurately. These factors include the number of domains and script-generated redirections visited, the number of redirection hops, the delay in page refresh times, the HTTP status codes which indicate redirection, and the use of iframes. The identified factors provide criteria to design more robust approaches for detecting redirection spam.
Malicious-URL Detection using Logistic Regression TechniqueDr. Amarjeet Singh
Over the last few years, the Web has seen a
massive growth in the number and kinds of web services.
Web facilities such as online banking, gaming, and social
networking have promptly evolved as has the faith upon them
by people to perform daily tasks. As a result, a large amount
of information is uploaded on a daily to the Web. As these
web services drive new opportunities for people to interact,
they also create new opportunities for criminals. URLs are
launch pads for any web attacks such that any malicious
intention user can steal the identity of the legal person by
sending the malicious URL. Malicious URLs are a keystone
of Internet illegitimate activities. The dangers of these sites
have created a mandates for defences that protect end-users
from visiting them. The proposed approach is that classifies
URLs automatically by using Machine-Learning algorithm
called logistic regression that is used to binary classification.
The classifiers achieves 97% accuracy by learning phishing
URLs
Detecting Phishing Websites Using Machine LearningIRJET Journal
1. The document proposes a system to detect phishing websites in real-time using machine learning. It trains a random forest classifier on a dataset of URLs and associated features to classify websites as legitimate or phishing.
2. The system collects URL and page content features from a user's browser and sends them to a cloud-based random forest model. The model was trained on over 40,000 URLs and achieved 97.36% accuracy at detecting phishing sites.
3. The proposed system provides advantages like real-time detection, using a large dataset for training, detecting new phishing sites, and independence from third-party services. It aims to protect users by identifying phishing sites before sensitive information is entered.
This document summarizes a research paper that proposes an enhancement to the Multi-Level Link Structure Analysis (MLSA) algorithm to reduce false positives in detecting link spam. The MLSA algorithm analyzes the inlinks and outlinks of a web page across multiple levels but suffers from falsely detecting some legitimate pages as spam. The proposed improvement tracks duplicated links, only increments crosslink counts between different domains, and compares the main domains of pages to reduce falsely detected spam pages by 90-100%. An experimental analysis shows the improved MLSA correctly identifies pages that the original MLSA incorrectly flagged as spam.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
There have been reports such as ‘there is high rate of web application vulnerability’ as well as a range of ways in which web hackers attack web applications. Since the discovery that web applications convey the best content to users, there have been attempts to determine ways in which these systems can be hacked into through defacing, damage and defrauding. As the culture of conveying information across the internet continues to gain ground, there are increasing cases of vulnerabilities of these sites to cyber criminals.
Similar to Web Spam Detection Using Machine Learning (20)
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
2. Web Spam Detection Using Machine Learning in Specific Domain Features 221
in second place with 35% spam pages. Moreover, pages pages become enticing target for all different kinds of
written in some particular languages are more likely to be traders and marketers to advertise their products for sale,
spam than those written in other languages, for instance get-rich-on-the-fly schemes [13, 17], and to get information
pages written in French are the most likely to be spam, with about pornographic web sites. Moreover, they become an
the percentage of 25% of being spam. enticing target for spammers to embed their spam content.
Spammers proved their excellence to adapt to the SpamCon Inc. [1] estimated the cost induced by resources
different formats available for Web pages, several spamming loss and spam filtering associated with only one unsolicited
message is 1$ up to 2$ multiplied by the number of spam
techniques used by spammers to influence the ranking page
sent and received every day, the one dollar becomes million.
algorithms of search engines. All these techniques are
Because of the serious problems associated with the
considered challenging for web page spam detection
unsolicited spam contents of either a single E-mail or a
algorithm, especially for Contents-Based approach. The two large web page, a number of automated filtering approaches
main categories of spammer techniques are: Term were proposed in the literature to overcome such problems
Spamming, and Link Spamming [11, 3].In term spamming, [16, 10]. These filters are used mainly for E-mail spam and
many techniques that modify the content of the page are then transformed to be used in the context of Web Spam.
applied. The content includes: the document body, the title, Early proposed approaches for spam filtering relied
Meta tags in HTML header, anchor texts associated with mostly on manually constructed pattern-matching rules that
URLs and page URLs. The spammers can attach their need to be tuned to each user’s message [9]. That is, they
unsolicited content (i.e. spam) to one or more of these allow users to hand-build a rule set that consists of a set of
contents resulting in a new page that can pass the spam logical rules to detect spam emails and Web pages.
filter without any doubt of being legal. Among all term However, these approaches are seemed to be tedious and
spamming techniques, the most popular one is body problematic, since users need to pay a full attention just to
spamming [11], in which terms are included in the build the desired set of rules, which by the way not all users
document body, an example is to include specific terms as can build such a set. In addition, it is a time consuming
"Free grant money", "free installation", "Promise you ...!", process, since the generated set of rules should be changed
"free preview", etc. or refined periodically as the nature of spam changes too.
Another way of grouping term spamming techniques is Because of the problems associated with the manual
based on the type of terms that are added to the text fields, construction of rules, another approach was proposed in [7]
either by repeating one or a few specific terms, including a to automatically adapt to the changing nature of spam over
large number of unrelated terms, or stitching phrase time and to provide a system that can learn directly from
wherein, sentences or phrases, possibly from different data already stored in the web server databases. These
sources are glued together [11]. approaches proved as successful when applied for general
In link spamming technique, spammers tend to insert classification tasks, that is, the classification of E-mail to
links between pages that are present for reasons other than either spam or non-spam based on their text, with no
merit [13]. Link spam takes advantage of link-based ranking regards to the existence of some domain specific features.
algorithms, such as Google’s Page Rank algorithm, which Several machine learning algorithms have been proposed
gives a higher ranking to a website that is cited by other for text categorization (classification) [14, 15]. These
high ranked websites. approaches were investigated to be used for spam filtering
In correspondence to the aforementioned spamming since it is viewed as a text categorization problem. In [13],
techniques, many content-based spam filtering techniques they applied a machine learning algorithm for the purpose
were proposed. The importance of analyzing the content of a of spam filtering. In this algorithm, the filter learns to
particular web page is that spammers tend to boost their web classify documents into fixed classes (i.e. spam and
pages rank by applying spamming techniques on these nonspam), based on their content, after being trained on
contents [11]. During content analysis, the number of words manually classified documents.
in the page’s body and title, the average length of words, the As a variation of the rule-based approaches discussed
amount of text anchor and keywords in metatags are above, a great deal of work was witnessed in the literature to
analyzed to detect the abnormalities in these contents that automatically perform content-based classification. Naïve
are interpreted as spamming attempts. Bayes classifiers [16], was proposed as a good example of
The rest of this paper is organized as follows. Section 2 those approaches that showed satisfactory results in the
gives an overview of the works related to spam detection. context of E-mail spam Filtering. [13] trained a Naïve Bayes
The Naïve Bayes classifier is illustrated in Section 3. classifier on manually classified spam and nonspam
Section 4 proposed our approach, and our experimental messages reporting surprisingly good results in terms of
results are shown in Section 5. Section 6 concludes the precision and recall. Our work utilizes Naïve Bayes
paper and provides future directions. classifiers based on the context of web pages to detect the
spam pages automatically.
2. Related Work
Due to the important role that web pages occupy as means 3. The Naïve Bayes Classifier
for supporting electronic commerce (E-Commerce), web A Naïve Bayesian classifier is a simple probabilistic
221
3. 222 Najadat and Hmeidi
classifier based on applying Bayes' theorem with a strong spam, divided by the probability of finding those words in
(naive) independence assumption that all variables A1, any document [13]:
A2,…,An in a given category C are conditionally
independent with each others given C [16]. Depending on p(words | spam)
P(spam | words) = p(spam) (3)
the precise nature of the probability model, Naive Bayes P(words)
classifiers can be trained very efficiently in a supervised
learning setting [6,12]. 4. Web Spam Detection Classifier
Beside Naïve Bayes classifiers, a variety of supervised
machine learning algorithms such as Support Vector Finding a spam web page is viewed as supervised text
Machine (SVM) and memory-based learning [4, 8] have classification problem. In the supervised classification
been successfully applied and showed satisfactory results in application, the web spam classifier needs to be trained with
the context of spam filtering. a set of web pages that are previously classified into two
categories, spam and non-spam.
Although these techniques discussed above proved to
Since spam is a relative concept, that is, what is
perform well in some cases, they still have problems in other
considered spam for one user may not be the same for other
cases: for instance, all types of Content-based spam filters users. Moreover, what might be spam for a specific user at a
have false positives; generally, it is more sever to misclassify particular time might not be so for the same user at different
a legitimate message as spam than to let a spam message time, then, depending only on the capabilities of the trained
pass the filter [4]. In addition what is classified as spam by classifier as the case of many traditional Naïve Bayes
these filters may not truly be so because spam is a relative Classifiers seems to be of limited benefits.
concept, that is, what might be considered as a spam for one In training phase, a user-oriented preparation is
person may not be so for another one. performed, wherein, the web pages reside in the web server
These limitations are driving factors for us to develop a are classified into spam or nonspam based on user's
novel technique for spam filtering by using a user-oriented feedback and the automated classification by the filter. The
feedback mechanism that works in combination with Naïve attention is given for the general spam that the majority of
Bayes classifier to reduce the false positives and false users agree upon, then all the terms contained in the pages
negative encountered by traditional classifiers, in addition, a classified as general spam are extracted to form the General
special concern is given to some specific domain features Spam Dictionary, this phase is illustrated in Figure 1.
(terms and patterns) that maybe considered as spam
discriminators.
The classifiers can be applied on spam filtering being
viewed as text classification problem as follows: Given a set
of training documents, D= {t1, t2… tn } of tuples and a set of
classes C= {C1, C2… Cn}. The classification problem is to
define a mapping f: D→C where each ti is assigned to one General
class with their associated class labels, each document, ti, is
spam,
represented by a vector of words {w1, w2,…wn}. The
independent probability of wi of a given document associated
specific
Filter
with class C can be written as in [6] p (Wi | C ) . spam,
and
Since each document consists of a large number of words,
the Naïve Bayes classifier makes the simplifying assumption non-
that w1, w2,…wi are conditionally independent given the spam
category C, pages
P(D | C ) = ∏ i
p (W i | C ) (1) Feedback
The probability of a given document D belongs to a given
class C is represented as P(C|D), which can be computed
Figure 1. User Oriented Training
p (C ) (2)
P (C | D ) = p(D | C )
P(D )
Therefore, a novel user-oriented training mechanism is
needed to support the classifier with periodic users' feedback
To estimate the probability of a particular document is to determine whether the page is considered as spam or
spam, given that it contains certain words, Bayes' theorem nonspam. In this training scenario, there are two outcomes:
states that the probability of finding those certain words in those web pages that are judged to be spam by the majority
spam documents, times the probability that any document is of users, we call such pages: the General Spam, and those
4. Web Spam Detection Using Machine Learning in Specific Domain Features 223
web pages that viewed to be spam by one or few users, we lead to inadequate results. In other words, there is no
call them Specific Spam. Our attention will be focused on particular feature in the text of the web page that provides
the general spam, because they can contribute efficiently in evidence as to whether the page is spam or nonspam.
the process of classification. However, this assumption does not hold in many real
This novel training mechanism is proposed mainly to situations. For example, it is proved by experience (and
function in the server-side rather than in the client-side. It is from users' feedback) that there are many specific features
an effective and promising mechanism to overcome the whose existence provides a strong indication on the
previously mentioned problems associated with web server suspicious message (spam), such as "free money",
being affected with spam, especially the problem of wasting "congratulation you are winner number…", "$$$$ you win
resources on spam pages (the resources include server's xxx$$$$", and the over used punctuations "??????".
bandwidth, CPU cycles and storage space). In addition to these discriminating textual features
The spam detection system consists of three phases which (patterns), web pages contain many non-textual features that
include training phase, preprocessing phase, and indicate whether it is spam or not such as the domain type
classification phase. .edu, .org, .com, .biz, etc [4]. It is shown by Microsoft
As shown in Figure 2, the preprocessing phase is the researchers [11] that 70% of those pages with the domain
cleaning process, which is applied to each web document to .biz are spam, and that .edu pages are rarely (or never)
extract its body. In stemming and stop words removal, the contain spam.
frequent words that do not contribute efficiently in To this end, we consider the employment of such (textual
classification process are removed from the body of the and non-textual) features as good discriminators of spam
page. that insist in a correct classification. To achieve this, we
Stemming operation reduces distinct words to their maintain a table (or named as dictionary) called specific
common stem, which is achieved by removing prefixes and feature dictionary consisting of all these specific features,
suffixes from words. We choose the affix stemmers each entry in this table corresponds to: <feature,
algorithm in the stemming work. This assists in reducing probability>. Then, it becomes straightforward to
the time required for classification since the words length is incorporate such additional features to our Naïve Bayes
lessened, which yields to reduce the accuracy of Model.
classification. As a new web page needs to be classified, a list of all its
The vital step in this phase is the generating dictionaries, words is generated and checked against the pre-established
wherein two different lists of words are generated, such lists features dictionary to make sure whether it contains one or
are called dictionaries, and they include: the frequent terms more of its words that are determined to be spam
dictionary, extracted from those web pages that are discriminators. In addition to the comparison with the
classified as non-spam, and the special features dictionary. specific features dictionary, the new webpage is compared
Each entry in the frequent terms dictionary consists of against other dictionaries (i.e. the general spam and the
<term, probability of being spam>. frequent terms dictionaries). After each comparison, the
In classification phase, the preprocessed documents are probability of spam is computed, as a result, we come up
represented by Term Frequency Matrix (TFM) structure [5] with a probability value that takes into account the domain
to perform the statistical analysis (i.e. Bayesian rule). TFM specific features existence, the words that are judged (by
simplifies the calculation of the probability of wordi belongs users) to be spam, and the ordinary terms that might be
to classj, P(classj | wordi), and also improves the efficiency of spam in some cases (according to their probability).
Naïve Bayes classifier which requires only one scan through Figure 4 depicts the Naïve Bayes classifier with user
the entire training dataset. As provided in Figure 3, each feedback and domain specific features.
intersects of word and class row in Term Frequency Matrix
represents the number of times (or frequency) of the word
appear in class j.
Assuming that each feature contributes in the process of
spam filtering in an equal manner to each other feature may
223
5. 224 Najadat and Hmeidi
Figure 2. Web Spam detection system structure with Naïve Bayes Classifier
6. Web Spam Detection Using Machine Learning in Specific Domain Features 225
Figure 3. Term Frequency Matrix associated with indiviual file terms
predicted classifications done by a classification system
5. Experimental Results [6]. Figure 5 shows confusion matrix for the two classes
spam and non-spam. As in [6],
Our experiments were all performed on the webspam-
TP represents actually positive and classify as positive,
UK2006 data set [4]. The training dataset consists of
FN represents actually positive and classify as
8,1415 web pages. A detailed description of their data set
negative,
and the criteria in assigning a web to be spam or non-
FP represents actually negative and classify as positive,
spam can be found in [4]. In our work, a sample of these
TN represents actually negative and classify as
web pages is taken to evaluate the classification accuracy
negative.
of our spam detector.
To estimate the accuracy of our proposed algorithm, we
use a popular accuracy measure in the context of
Information Retrieval, namely: the Confusion Matrix
(CM). CM contains information about actual and
225
7. 226 Najadat and Hmeidi
1) Build Vocabulary Table for the entire documents.
Sensitivity = truePositive (4)
2) Calculate P (Spam) and P (Non-spam). truePositive + falseNegative
3) For each wordi in test documentj do
If (wordi exists in Feature dictionary) then calculate trueNegative
Specificity = (5)
probability of document D being spam trueNegative + falsePositive
P1( D | spam) = ∏ P( wi | spam)
i
We use the above measurements to compute the accuracy
If (wordi exists in Spam dictionary) then calculate which is defined as follows:
probability of document D being spam:
P 2 ( D | spam ) = ∏ P ( wi | spam ) TP + TN
i
Accuracy = (6)
TP + FN + FP + TN
If (wordi exists in Term dictionary) then calculate
probability of document D being spam:
P 3 ( D | spam ) = ∏ P (w
i
i | spam ) To evaluate the accuracy, holdout technique is utilized to
produce a true estimate of the classifier. The data are
4) End for loop partitioned into two separated dataset, training set and
5) Ptotal ( D | spam ) = P1 + P 2 + P3 testing set. The training set used to learn the classifier
algorithm, and the testing set in used to evaluate the
6) Calculate posterior probability of document D : accuracy. We run our classifier on different five samples and
then calculate the classification accuracy for each run. For
P ( spam | D ) = Ptotal ( D | spam).P( spam) ,
instance, we calculate the accuracy of a sample consisting of
P(non− spam| D) = ∏P(wi | non- spam.P(non− spam
) ) 238 testing documents (with 69 of documents belong to
i Nonspam class, and 169 of documents belong to Spam
class). For this test, the Sensitivity and Specificity are 97%
7) If P( spam | D ) > P( non − spam | D) then
and 66% respectively. And the total accuracy is 88%, where
Classify document D as spam else total accuracy equal ((TP + TN) / (TP+FP+TN+FN)). Other
experiments are made on different samples consisting of
Classify document D as not spam.
324, 400, 519 and 618 documents.
The accuracy results are shown in figures 6 and 7, these
Figure 4. Spam Detection Procedure
figures show also the effect of stemming the document
before classification on the accuracy results. Figure 7
indicates that using stemming documents gains 80% in
average, while Figure 6 shows the accuracy is 78%.
Spam Nonspa
m 100
Actual Spam TP FN 90
Class Nonspam FP TN 80
70
Accuracy (%)
Figure 5. Confusion Matrix 60
50
40
We use the confusion matrix to calculate sensitivity and 30
Specificity measures. Sensitivity refers to true positive ratio, 20
that is, the proportion of positive documents that are 10
correctly identified. Specificity is the true negative ratio, 0
that is, the proportion of negative documents that are 238 324 400 519 618
correctly identified. Where truePositive is the number of true Data Size
positive documents are correctly classify. TrueNegative is
the number of true negative documents that are correctly Figure 6. Accuracy versus Dataset size (Stemming text)
classified. FalsePositive is the number of false positive
documents that are incorrectly classified [6].
8. Web Spam Detection Using Machine Learning in Specific Domain Features 227
Detecting spam web pages is one of the major challenges
that face search engines in their queries results. Search
engines should return high quality results in response to the
90
user's queries. Many search engines necessitate an
85 integration of a healthy detection spam to eliminate all web
pages that effect in page ranking algorithm. Several content-
80
based and machine learning techniques were proposed to
75 detect spam pages.
This paper proposed a Naïve Bayes approach that gives
70 Accuracy (%) weight for user's feedback to improve the training process of
65
the classifier and that consider the existence of some domain
238 324 400 519 618 specific features that contribute strongly to the spam
Data Size discrimination, that is, there existence provides evidence
that the webpage is spam. This approach proposed mainly to
Figure 7. Accuracy versus Dataset size (Unstemming text) function in the server-side, to reduce the overhead
associated with spam pages in the web server.
Our work involved Naïve Bayes classifier in discovering
We also evaluate the classifier performance by non required pages. The experimental results showed that
concentrating on the number of nonspam pages that are Naïve Bayes classifier provides on average accuracy equal to
wrongly classified as spam, and the number of spam pages 80.2%. For future works we will develop an application as
that are wrongly classified as nonspam. The first parameter plug-in in one of the open source browser to work as
is called the Nonspam Misclassification Rate (HMR) while detector in online website pages to notify the users for spam
the second parameter is called Spam Misclassification Rate pages currently working on.
(SMR). Our future work will be to optimize the performance of
In the context of spam filtering, it is proved that the effect our Naïve Bayes Classifier by taking into consideration the
if misclassifying a legitimate message as spam is more sever word-position of the domain specific features, which will
than misclassifying a spam message as legitimate. The contribute to a better accuracy. In addition, the improvement
accuracy is compared for both Naïve Bayes Classifier with will include the user-oriented feedback model to satisfy the
Domain Specific Features (NBCDSF) and the Naïve Bayes needs of much more users.
Classifier without User Feedback (NBCUF), the results
show that the improved Naïve Bayes classifier with user
feedback outperforms the NBCUF in terms of increased References
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Hassan M. Najadat is an Assistant Professor in the
ACM symposium on Document engineering, ACM,
Department of Computer
2006.
[11] A.Ntoulas, M.Najork, M.Manasse, and D.Fetterly, Information Systems in Jordan
"Detecting Spam Web Pages Through Content University of Science and
Analysis", In the Proceedings of the 15th Technology in Irbid, Jordan. His
international conference on World Wide Web, ACM, research interests are centered on
2006. data mining, machine learning,
[12] G.Paul, "Better Bayesian Filtering". In the database systems and he has
Proceedings of the 2003 spam conference, Jan 2003. authored over nine refereed
[13] M.Sahami, S.Dunmais, D.Heckerman, and publications in the areas of
E.Horvitz, "A Bayesian Approach to Filtering Junk clustering, classification, association
E-mail", AAAI Workshop on Learning for Text rules, and text mining. He received
Categorization, July 1998, Madison, Wisconsin, his PhD in Computer Science from
AAAI Technical Report WS-1998. North Dakota State University in Fargo, USA, MS in
[14] J.Su and H.Zhang, "Full Baysian Network Classifiers", Computer Science from University of Jordan in Amman,
In the Proceedings of 23 rd International Conference Jordan, and BS in Computer Science from Mut’ah
on Machine Learning, Pittsburgh, PA, 2006.
University in Alkarak, Jordan.
[15] B.Yu and Z.Xu, "A Comparative Study for Content-
Based Dynamic Spam Classification Using Four
Machine Learning Algorithms", Knowledge-Based
Systems, 4(21), pp. 355-362, 2008. Ismail Hmeidi is an Assistant
[16] H.Zhang and D.Li, "Naïve Bayes Text Classifier", In Professor in the Department of
the Proceedings of the IEEE International Conference Computer Information Systems in
on Granular Computing, 2007. Jordan University of Science and
[17] L.Zhang, J.Zhu, and T.Yao, "An Evaluation of Technology in Irbid, Jordan. His
Statistical Spam Filtering Techniques” ACM research interests are centered on
Transactions on Asian Language Information information retrieval, natural
Processing, 4(l.3), pp. 243-269, 2004. language processing, e-learning, and
database systems. He has authored
over 13 refereed publications in the
areas of query expansion, automatic indexing and text
categorization. He received his PhD in Computer Science
from Illinois Institute of Technology, USA, MS and BS in
Computer Science from Eastern Michigan University,
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U.S.A.