Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
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.
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
Identification of Spam Emails from Valid Emails by Using VotingEditor IJCATR
In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted
messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem
and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of
a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The
advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and
Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam
detection indicate improved accuracy compared to the previous methods.
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 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
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.
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.
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
Identification of Spam Emails from Valid Emails by Using VotingEditor IJCATR
In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted
messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem
and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of
a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The
advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and
Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam
detection indicate improved accuracy compared to the previous methods.
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 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
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.
This document presents research on using machine learning algorithms to detect SMS spam messages. It introduces the problem of SMS spam, describes the dataset used containing over 5,000 SMS messages, and explains the preprocessing and feature extraction steps. It then evaluates the performance of various classification algorithms - Naive Bayes, SVM, k-NN, Random Forests, and AdaBoost with Decision Trees - on the SMS spam detection task, reporting the accuracy, spam caught rate, and ham blocked rate for each. It finds that Naive Bayes and SVM performed best with over 98% accuracy.
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.
Tracking Spam Mails Using SPRT Algorithm With AAAIRJET Journal
This document proposes a system to detect and block spam emails using AAA (authentication, authorization, and accounting) and SPRT (sequential probability ratio test) algorithms. The system would authenticate users, authorize their ability to send emails, detect spam emails using SPRT, and maintain logs of email activity. Spam emails detected would be blocked without notifying the sender. The system aims to identify "spam zombies" - compromised machines used to send spam emails. It would generate graphs of IP addresses versus number of spam emails to analyze spamming behavior and help administrators take appropriate action against spammers. The proposed system has four modules - staff machine, authentication server, mail server, and admin module for monitoring logs and reports.
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.
WORKLOAD CHARACTERIZATION OF SPAM EMAIL FILTERING SYSTEMSIJNSA Journal
Email systems have suffered from degraded quality of service due to rampant spam, phishing and fraudulent emails. This is partly because the classification speed of email filtering systems falls far behind the requirements of email service providers. We are motivated to address this issue from the perspective of computer architecture support. In this paper, as the first step towards novel architecture designs, we present extensive performance data collected from measurement and profiling experiments using representative email filtering systems including CRM114, DSPAM, SpamAssassin and TREC Bogofilter. We provide detailed analysis of the time consuming functions in the systems under study. We also show how the processor architecture parameters affect the performance of these email filters through simulation experiments.
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.
A New Method to Stop Spam Emails in Sender SideIDES Editor
This summarizes a research paper that proposes a new method to stop spam emails at the sender side rather than the receiver side. The key steps of the proposed method are:
1) The sender connects and authenticates with their mail server.
2) The sender uploads the email to be checked by their mail server.
3) The mail server applies filtering techniques like checking the subject, links, and content to determine if the email is spam. It also considers the sender's user license and whitelist.
4) If deemed not spam, the mail server distributes the email. If spam, it is blocked at the sender side to conserve network resources compared to receiver-side filtering.
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 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
24 Hours Of Exchange Server 2007 ( Part 13 Of 24)Harold Wong
This document discusses maintaining anti-spam systems in Exchange Server 2007. It covers understanding anti-spam functionality through deploying a defense-in-depth approach and configuring the various anti-spam components. These components include connection filtering, sender filtering, sender ID filtering, content filtering, and sender reputation filtering. It also discusses deploying the Edge Transport server and configuring internet message delivery.
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
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.
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.
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
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.
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.
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.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of commun
ication so are the attempts to misuse it to
take undue advantage of its low cost and high reach
ability. 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 existin
g 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 t
he effectiveness of the architecture
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
This document presents research on using machine learning algorithms to detect SMS spam messages. It introduces the problem of SMS spam, describes the dataset used containing over 5,000 SMS messages, and explains the preprocessing and feature extraction steps. It then evaluates the performance of various classification algorithms - Naive Bayes, SVM, k-NN, Random Forests, and AdaBoost with Decision Trees - on the SMS spam detection task, reporting the accuracy, spam caught rate, and ham blocked rate for each. It finds that Naive Bayes and SVM performed best with over 98% accuracy.
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.
Tracking Spam Mails Using SPRT Algorithm With AAAIRJET Journal
This document proposes a system to detect and block spam emails using AAA (authentication, authorization, and accounting) and SPRT (sequential probability ratio test) algorithms. The system would authenticate users, authorize their ability to send emails, detect spam emails using SPRT, and maintain logs of email activity. Spam emails detected would be blocked without notifying the sender. The system aims to identify "spam zombies" - compromised machines used to send spam emails. It would generate graphs of IP addresses versus number of spam emails to analyze spamming behavior and help administrators take appropriate action against spammers. The proposed system has four modules - staff machine, authentication server, mail server, and admin module for monitoring logs and reports.
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.
WORKLOAD CHARACTERIZATION OF SPAM EMAIL FILTERING SYSTEMSIJNSA Journal
Email systems have suffered from degraded quality of service due to rampant spam, phishing and fraudulent emails. This is partly because the classification speed of email filtering systems falls far behind the requirements of email service providers. We are motivated to address this issue from the perspective of computer architecture support. In this paper, as the first step towards novel architecture designs, we present extensive performance data collected from measurement and profiling experiments using representative email filtering systems including CRM114, DSPAM, SpamAssassin and TREC Bogofilter. We provide detailed analysis of the time consuming functions in the systems under study. We also show how the processor architecture parameters affect the performance of these email filters through simulation experiments.
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.
A New Method to Stop Spam Emails in Sender SideIDES Editor
This summarizes a research paper that proposes a new method to stop spam emails at the sender side rather than the receiver side. The key steps of the proposed method are:
1) The sender connects and authenticates with their mail server.
2) The sender uploads the email to be checked by their mail server.
3) The mail server applies filtering techniques like checking the subject, links, and content to determine if the email is spam. It also considers the sender's user license and whitelist.
4) If deemed not spam, the mail server distributes the email. If spam, it is blocked at the sender side to conserve network resources compared to receiver-side filtering.
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 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
24 Hours Of Exchange Server 2007 ( Part 13 Of 24)Harold Wong
This document discusses maintaining anti-spam systems in Exchange Server 2007. It covers understanding anti-spam functionality through deploying a defense-in-depth approach and configuring the various anti-spam components. These components include connection filtering, sender filtering, sender ID filtering, content filtering, and sender reputation filtering. It also discusses deploying the Edge Transport server and configuring internet message delivery.
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
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.
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.
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
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.
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.
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.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of commun
ication so are the attempts to misuse it to
take undue advantage of its low cost and high reach
ability. 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 existin
g 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 t
he effectiveness of the architecture
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
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.
This document summarizes a research paper that proposes a new email spam detection framework using feature selection and similarity coefficients. The framework first applies feature selection to identify the most important features for distinguishing spam and non-spam emails. It then calculates similarity coefficients between email pairs to quantify their similarity based on the selected features. The researchers claim this approach improves spam detection accuracy and rate by removing irrelevant and redundant features before classification. They test their framework on a standard email dataset and find that detection performance is better after applying feature selection compared to using all original features.
Detecting spam mail using machine learning algorithmIRJET Journal
This document discusses a study that aims to detect spam emails using machine learning algorithms. The researchers developed a model using natural language processing and machine learning techniques. They preprocessed email data by removing stop words and stemming words. They then used a correlation-based feature selection method to extract the most important features. A bagged hybrid classifier combining Naive Bayes and Decision Tree (J48) algorithms was used for classification. The study aims to more accurately classify emails as spam or ham (non-spam) compared to existing methods, which rely on rules-based approaches or single algorithms. It evaluates the performance of different machine learning classifiers like logistic regression, Naive Bayes, and support vector machines.
Detection of Spam in Emails using Machine LearningIRJET Journal
The document discusses detecting spam emails using machine learning techniques. It evaluates various machine learning algorithms for classifying emails as spam or ham (not spam) and finds that the Multinomial Naive Bayes algorithm provides the highest accuracy. The study uses an email dataset to train classifiers including Naive Bayes, Decision Trees, Random Forest, KNN, SVM and evaluates their performance based on precision, accuracy and other metrics. It finds that while Naive Bayes performs best, it has some limitations and ensemble methods like random forests generally provide more robust performance. The paper contributes to improving email spam detection through comparative analysis of machine learning algorithms.
The document summarizes a research paper about AutoRE, a system that combines content-based and non-content-based approaches to detect spam emails generated by botnets in real-time. AutoRE first pre-processes URLs in emails to group related domains and then generates regular expressions to identify patterns. It verifies spam classifications using blacklists and behavioral analysis of email properties, sending times, and patterns. The document also discusses how AutoRE helped characterize botnets and their traffic, informing future research like systems that calculate sender reputations based on global email behavior analysis.
Identifying Valid Email Spam Emails Using Decision TreeEditor IJCATR
The increasing use of e-mail and the growing trend of Internet users sending unsolicited bulk e-mail, the need for an antispam
filtering or have created, Filter large poster have been produced in this area, each with its own method and some parameters are
to recognize spam. The advantage of this method is the simultaneous use of two algorithms decision tree ID3 - Mamdani and Naive
Bayesian is fuzzy. The first two algorithms are then used to detect spam Bagging approach is to identify spam. In the evaluation of this
dataset contains a thousand letters have been analyzed by the software Weka charts provided in spam detection accuracy than previous
methods of improvement
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 discusses email spam filtering. It begins by introducing email and the problem of spam. It then describes the two main types of spam filtering: server-side filtering that occurs before emails reach the user, and client-side filtering using user-created rules. Some techniques used by both types are discussed such as block lists and virus definitions. The document then focuses on Gmail spam filtering, how messages are marked as spam, and reasons a message may be placed in the spam folder like unconfirmed senders or messages previously marked as spam. Advantages and disadvantages of spam filtering are provided.
Email spam, also known as junk email or unsolicited
bulk email(UBE), is a subset of electronic spam involving nearly
identical messages sent to numerous recipients by email. Clicking
on links in spam email may send users to phishing web sites or sites
that are hosting malware. Spam email may also include malware as
scripts or other executable file attachments. Definitions of spam
usually include the aspects that email is unsolicited and sent in bulk
In order to overcome spam problem many researchers have
been conducted and various method of anti-spam filtering have
been implemented. A spam filter is a set of instruction for
determining the status of the received email. Spam filters are used
to prevent spam email passing through the recipient. The main
challenge is how to design an effective spam filter that allows
desired email to pass through while blocking the unwanted email.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
This document summarizes a research paper on using correlation-based feature subset selection to improve spam detection accuracy when using a Bayesian classifier. The researchers introduce using feature subset selection to identify the most relevant features of spam emails while removing redundant features. This improves the accuracy of a naïve Bayesian classifier for spam detection from 65-74% to over 80%. It discusses how correlation-based feature subset selection works by selecting features highly correlated with the class (spam or not spam) but uncorrelated with each other. The researchers apply this method to a spam email dataset and achieve over 92% accuracy in spam detection using a Bayesian network classifier after feature subset selection, an improvement over using the classifier alone.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
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incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
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This document proposes an approach to using SMTP connect time blocking as a reliable method for email filtering. It involves performing checks on the SMTP header before receiving the email contents, including verifying the HELO/EHLO name, sender and recipient addresses, and checking sending IPs against blacklists. Checks are ordered from simple to complex to filter emails efficiently while avoiding false positives. Techniques like temporary reject codes and greylisting can block many spam emails without delaying legitimate emails. When used with traditional content analysis, this approach effectively filters over 97% of spam.
1) The document proposes a Bayesian algorithm approach to accurately detect spam emails. It aims to improve on existing near duplicate matching schemes that rely on user feedback databases.
2) A novel email abstraction scheme called SAG is introduced to represent emails for near duplicate matching in a way that captures similarities between spam emails while avoiding accidental deletion of legitimate emails.
3) The Bayesian filter is trained on the email abstractions generated by SAG to automatically classify subsequent emails as spam or legitimate. The approach aims to more effectively detect spam emails that evolve over time.
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MINIMIZING THE TIME OF SPAM MAIL DETECTION BY RELOCATING FILTERING SYSTEM TO THE SENDER MAIL SERVER
1. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
DOI : 10.5121/ijnsa.2012.4204 53
MINIMIZING THE TIME OF SPAM
MAIL DETECTION BY RELOCATING FILTERING
SYSTEM TO THE SENDER MAIL SERVER
Alireza Nemaney Pour1
, Raheleh Kholghi2
and Soheil Behnam Roudsari2
1
Dept. of Software Technology Engineering, Islamic Azad University of Abhar, Iran
pour@abhariau.ac.ir
2
Dept. of IT Engineering, Sharif University of Technology, Kish Island, Iran
{r.kholghi,Soheil.bh}@gmail.com
ABSTRACT
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of
users. Spam emails consume the network resources and cause lots of security uncertainties. As we
studied, the location where the spam filter operates in is an important parameter to preserve network
resources. Although there are many different methods to block spam emails, most of program developers
only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a
new and efficient approach to prevent spam emails from being transferred. The result shows that if we
focus on developing a filtering method for spams emails in the sender mail server rather than the receiver
mail server, we can detect the spam emails in the shortest time consequently to avoid wasting
network resources.
KEYWORDS
Anti-spams, Receiver mail server, Sender mail server, Spam Email
1. INTRODUCTION
An e-mail is considered “spam” when a massive number of them are sent to multiple recipients.
Spam email is usually used for advertisement or marketing. These unwanted emails cause
drawbacks to the recipient, and consume the users’ network resources. The disadvantages of
spam emails have been addressed in many occasions. In some cases for a single user 9 out of 10
emails are spams that fill his/her inbox. The United States Federal Trade Commission described
that 66% of spams have false information somewhere in the message and 18% of spams
advertise “Adult” material. According to another report [1] 12% of users spend half hour or
more per day dealing with spam emails.
There are several major problems with spam mails. First of all, they are high in volume
and fill in mailbox of users. Secondly, there is no correlation between receivers’ area of
interests and the contents of spam mails. Thirdly, they cost money for ISPs because the
bandwidth and the memory of system are wasted. Finally, Spam e-mails cause a lot of
security problems because most of them include Trojan, Malwares, and viruses [2].
Many filtering techniques have been developed to control the flow of spam emails.
Unfortunately, even with these available techniques, the number of spam emails is growing and
the flow has not been controlled completely. The setback is that there is no actual solution
because a spammer; an unidentified user with enough knowledge is able to be familiar with the
logic of the filtering mechanisms. As a result, bypassing the filter and sending the spam emails
2. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
54
seems not to be a difficult task for such spammers. In such cases, the spam emails are not
detected and are considered as legitimate ones.
There are studies regarding spam email filtering [3-11]. The common issue with the usage of all
of these techniques is that the filtering systems are set up in the receiver mail server,
consequently, causing network load and wasting network resources. To preserve network
resources such as bandwidth and memory, and to reduce network load, this paper proposes to
locate spam email filtering in the sender mail server rather than the receiver mail server.
Moreover, this paper by experimental results shows that this novel approach works more
efficiently compared with the previously proposed approaches.
This paper is organized as follows. In section 2, the related work to the subject will be
highlighted. The Overview of email system and its operation are described in section 3. Our
proposal and the experiment results are presented in section 4 and 5 respectively. Finally, the
conclusion is shown in section 6.
2. RELATED WORK
As stated before, there are many filtering techniques to stop the flow of spam emails to mail
boxes [3-12]. Figure 1 simply illustrates the classification of spam email filtering techniques.
The classification includes list-based filtering [3-7], static algorithm [8-10], and IP-based
filtering [11]. The list-based filtering is classified into three categories; Blacklist [3], Whitelist
[4, 5], and Greylist [6, 7]. Static algorithm is classified into content-based [8, 9], and the rule-
based [10] filtering. Finally, IP-based filtering consists of revers-lookup [11].
In the Blacklist filtering [3], the IP address and the domain name of the sender server is stored
in a list called Blacklist and the emails from that IP address and domain are blocked. Then,
based on the policy of the receiver side, the emails from the Blacklisted IP addresses are deleted
or sent to spam folder. Conversely, there are some limitations for the Blacklist filtering. First,
since the spammer uses several IP addresses with a variety of domain names, updating these
lists is a difficult task for the client. Consequently, updating the Blacklist regularly is costly.
Second, Blacklist filtering may result in identification of an email as false negative because of
minimal control in this methodology.
On the other side of the Blacklist, is the Whitelist filtering [4, 5]. In this technique, any user
stores his/her email contacts in a list called the Whitelist. Therefore, any received email with the
correspondent address from this list is accepted, and all other addresses out of this list are
considered uncertain. In this technique, also there are certain obstacles. The obvious one is that,
since the sender is unidentified and unpredictable, it is difficult to insert all possible sender
addresses in this list. Similar to the Blacklist, the Whitelist filtering needs to be updated
regularly; which is a costly task for the user. Another major issue is that if the email address of a
spammer is added in the Whitelist of an email client once, this will provide access to all of the
addresses in the Whitelist of that specific client without any boundaries or limits. As a result,
this will ensure the spammer more reachable email addresses.
In Greylist filtering [6], a different approach is practiced. This technique can be set either on the
mail server of the receiver or/and on the personal receiver anti-spam application. At first step,
all received emails are rejected. Because of this policy, spammers do not try to resend the
rejected email since it is time consuming for them. Instead, the spammers prefer to search for
another email address without Greylist filtering. Moreover, from the behavior of spammer
viewpoint, the Greylist by itself provides a usable contact list of the mail servers using this
filtering technique, subsequently; the spammer avoids sending more messages to those servers
after the first rejection because spammer can recognize what the receiver mail server filtering
structure is, and how it operates. Consequently, the spammer will update his techniques in order
to bypass Greylist filtering. Finally, a major weakness observed in this technique is that there is
3. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
55
a possibility that legitimate messages may be lost [6, 7].
Content-based filtering [8, 9] is another filtering technique that uses machine learning criteria.
In order to have the satisfactory results, the administrator of the mail server needs to train the
filters to perform their functions. This filtering starts to work based on some predefined words
after the email is received entirely. These particular words are collected by statistical reports
based on the words and phrases gathered from the spam emails. Rule based filtering [10] is
similar to content-based one with some differences. This technique works through some certain
rules and regulations. By these rules the filter decides to pass or to block the received email.
The major problem with the content and rule based filtering is that, the rules and the words are
verified by the programmer. This leads to variable restrictions. First, the databases and the
policies need to be updated at regular basis. Second, as all spammers are aware of these filters,
and their functionality, they will try to deliver their messages using additional characters to
legitimize their emails. Finally, these techniques work after the body of the email is completely
received by the mail server which increases the time for checking whether the email message is
spam or not.
In reverse lookup, also known as a reverse DNS (Domain Name System) lookup, the host is
associated with a given IP (Internet Protocol) address. By using this routine, the receiver can
confirm the identity of the domain name of the sender. This technique is not effective for the
mobile users and the users with invalid IP address [11].
Authors in [12] introduce a new procedure based on the spammer behavior. Commonly, a
spammer sends an email(s) to huge number of users. In this filtering, the administrator sets a
counter on mail server to limit the number of the emails which its clients wish to send. This
counter-based filtering provides time saving because the mail server can decide whether a mail
is spam or not before the message is completely received. But its restriction is that the legitimate
emails may not pass the counter filter.
As spammers become more dominant, the number of anti-spam methodologies and software are
growing correspondingly. The problem is that even with the most accurate anti-spam
techniques, we lose lots of network resources such as time and bandwidth because these
techniques are set on the receiver server side. In most of web based email services such as
Hotmail, AOL, and Yahoo, filtering emails start after they are fully received by the receiver
mail servers.
In this paper we propose shifting the location of the filtering system from the receive mail
server to the sender mail server to achieve efficient results. For this purpose, we define four
scenarios and evaluate the results with two anti-spam software, DSPAM1
, and TREC2
. These
software systems are open source programs, and include all filtering techniques stated above.
1
DSPAM: http://dspam.nuclearelephant.com/
2
TREC: Text REtrieval Conference. http://trec.nist.gov/
Figure. 1 Classification of Spam Detection Methods
4. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
56
3. OVERVIEW OF EMAIL SYSTEM
In this section, a brief explanation of email protocol and the process of filtering will be
elaborated. Simple Mail Transfer Protocol (SMTP) is the first protocol which transfers the
emails by some commands. Figure 2 illustrates SMTP commands. First, TCP/IP (Transmission
Control protocol and Internet Protocol) connection starts between sender and the associated
mail server. Following that, the SMTP commands begin with a Hello message and
announcing the acceptance of the session between the client and the server. This process
ends when the message is accepted by the mail server. TCP connection disconnects if
there is no more message from the client to the mail server.
3. OVERVIEW OF EMAIL SYSTEM
In this section, a brief explanation of email protocol and the process of filtering will be
elaborated. Simple Mail Transfer Protocol (SMTP) is the first protocol which transfers the
emails by some commands. Figure 2 illustrates SMTP commands. First, TCP/IP (Transmission
Control protocol and Internet Protocol) connection starts between sender and the associated
mail server. Following that, the SMTP commands begin with a Hello message and announcing
the acceptance of the session between the client and the server. This process ends when the
message is accepted by the mail server. TCP connection disconnects if there is no more message
from the client to the mail server.
When the email is delivered by the server, the filtering phase is started. Based on the server
filtering policy, Blacklist and Whitelist filtering is stared to examine if the email is a spam or a
valid one. If the email is recognized as a valid one, it is sent to receiver’s inbox otherwise the
email is blocked or transferred to the spam folder. When a Greylist filtering is used in relevant
Figure. 2 SMTP Transaction Commands
5. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
57
mail server, the email is rejected for the first time. Afterward the body of the email is tested with
content-based and rule-based filters according to the standards of the administrator.
4. STRUCTURE OF OUR PROPOSAL
This section describes our proposal. The main purpose of this approach is to detect spam emails
in the shortest time and consequently to avoid wasting network resources by shifting the
location of the filtering system from the receiver mail server to sender mail server (Figure 3). In
this way, all emails are screened and checked before being permitted to proceed to the receiver
mail server. Figure 4 illustrates the structure of sending an email with necessary filtering steps.
The procedure consists of four steps, IP validity check for sender and receiver, list-based
filtering, and statistical algorithm.
① First, the validity of IP address of the sender is checked by its mail server. When the client
is invalid, the connection is terminated. Otherwise, the mail is preceded to next step.
② Second, the validity of the email address of the receiver is checked by the DNS server.
When the email address of the receiver is not available, or wrong, the mail server sends
back a failure message to the sender, and the connection is terminated. Otherwise, the mail
is preceded to list-based filtering.
③ Third, the email should be checked by the list-based filtering such as Whitelist, Blacklist,
and Greylist. By passing these filtering the email is preceded to next step. When an email
cannot pass one of these filtering, the connection is terminated.
④ Forth, the statistical filtering such as, content-base and rule-based filtering is started.
Emails that cannot be verified as valid ones are not sent.
⑤ Finally, the valid emails are sent to the receiver mail server.
Figure. 3 Shifting the location of filtering system to sender mail server
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Our proposal is a new approach to prevent spam emails. Its novelty is that we introduce the
filtering methods in the sender mail server rather than the receiver mail server. By this approach,
invalid emails are not transferred to the receiver mail server because of filtering in the sender
mail server. Consequently, the network resources, such as bandwidth, time, and memory is
preserved. In the next section, we will illustrate the efficiency of this approach by fortifying it
with experimental results.
5. EXPERIMENT RESULTS
This section describes experiment results. We analyse our proposed model and compare the
performance of filtering system when is set up on different locations. Figure 5 illustrates the
observation model assuming that the spammer is going to send 1000 emails through the mail
server A. Four scenarios are defined based on changing the location of filtering system as
follows:
(1) The spammer is going to send 1000 emails through the mail server A to the mail server B,
and B checks the emails (Figure 5 scenario 1).
(2) The spammer is going to send 1000 emails through the mail server A to different mail
servers such as B, C, D, and they check the received emails respectively (Figure 5 scenario
2)
(3) The spammer is going to send 1000 emails through the mail server A to mail server B, and
A checks the emails (Figure 5 scenario 3).
(4) The spammer is going to send 1000 emails through the mail server A to different mail
servers such as B, C, D, and the mail server A checks the emails (Figure 5 scenario 4).
Figure. 4 An algorithm of filtering in sender mail server
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For our experiment, we used the results that concluded from various sources [13]. Figure 6
illustrates the result of the performance of anti-spam software running on different ISPs
compared with two representative open source anti-spams software. This Figure shows the time
required to process 1000 emails in the receiver mail server. The required time to process emails
at ISPs such as Hotmail, AOL and Microsoft are, 0.1, 0.09, and 0.1 second respectively. On the
other hand, the results of our experiment with two representative anti-spam software such as
TREC and DSPAM shows that the required time to process 1000 emails are 200 and 250
seconds. This result shows that the performance of email filtering in ISPs is better compared
with open source software. The factors that directly affect the performance of filtering
discrepancies are based on the size of email, processor power, and several others. Later, we will
use the outcomes of this experimentation for the four scenarios explained above.
As experimental results illustrated in Figure 7, we fulfilled our four scenarios. Starting from
scenario 1, the mail server B checks all 1000 emails one by one. For this purpose, the mail
server B puts each single email in its specific memory based on each IP address in the email. As
a result, the server consumes more time for the same email. The result of this scenario for each
anti-spam system is shown with the rightmost bar in Figure 7.
Scenario 1: Mail server B checks 1000 emails
Scenario2. Mail servers B, C, D, checks 1000 emails respectively.
Scenario3. Mail server A checks 1000 emails being sent to mail server B
Scenario4. Mail server A checks 1000 emails being sent to the mail servers B, C, D
Figure. 5 Observation model assuming that the spammer sends 1000 emails through the mail server A
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In scenario 2 the receiver mail servers, B, C and D check all the received emails. In order to
send each email, the mail server A is required to establish a session with associate mail servers.
For this purpose, the receiver mail servers, B, C, and D should accept the sessions for each IP
address. The result of this scenario is similar to the first scenario because we consider the total
process time for scenario 2. Results of this scenario are shown with blue bar in Figure 7.
In the third scenario the spam filtering is performed in the sender mail server. When the
spammer attempts to send 1000 emails to different clients, the anti-spam software starts to
process filtering on the email just once. If the email is recognized as trustable email, it is sent to
B. Otherwise this email will be deleted. The advantage of filtering in the sender mail server is
that the network resources between the servers are preserved. The results are displayed with
green bars in Figure 7.
In the last scenario, the sender sends 1000 emails to several recipients on different mail servers.
Figure. 6 The performance comparison of filtering between ISPs and
open source software in receiver mail server
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
Time(sec)
scenario 4 scenario 3 scenario 2 scenario 1
TREC DSPAM
Hotmail
Microsoft
AOL
Figure. 7 The performance comparison of spam mail filtering between sender and receiver mail servers.
9. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
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This time the mail server A should check the emails. Before any action for filtering, the mail
server A needs to establish sessions with the other mail servers. For this reason, the process time
is longer than the time required in scenario 3. The anti-spam processing in the sender mail
server uses less time when compared the same procedure performed on the receiver mail server.
Results are demonstrated with red bars.
The outcome of these assessments, as shown in Figure. 7, imply that scenarios 3 and 4 have
better performance compared with scenarios 1 and 2 under all conditions. It is because the
filtering process is performed in the sender mail server. Moreover, the performance of scenario
4 is not satisfactory compared with scenario 3 because the sender mail server needs to check the
domain of each receiver mail server. In this case, the performance level falls, however still it is
considerably better than the cases where the filtering is checked in the receiver mail server
(scenarios 1 and 2). Although the results for TREC and DSPAM are based on our experiments,
the results for ISPs such as Hotmail, AOL and Microsoft have been calculated logically based
on our prediction extracted from Figure. 6. On the other hand, scenario 3 indicates that when the
filtering system is located in the sender mail server, the processed time becomes n times less
than the time when the filtering system is in the receiver mail server when n indicates the
number of processed emails.
Spam mail detection is a challenging work against human mind because spammers try to find
new ways to bypass filtering systems. Therefore, it seems that it is a difficult task to read the
spammers’ mind and to find all the possible tricks that they might develop. To overcome this
problem, we suggest developing methods to provide high performance in the shortest time.
Spam mail filtering in the sender mail server (our proposal) is one of those methods compared
with the filtering in the receiver mail server.
6. CONCLUSION
In this paper, we have proposed an efficient approach for spam email detection. Our approach
proposes to shift the location of spam email filtering system from receiver mail server to sender
mail server. The purpose of this novel idea is to detect spam emails in the shortest time and
consequently to prevent wasting the network resources from misusage of spammers. In addition,
by experimental results we proved that our idea is efficient because just the resources in the
sender side are accessed. This implies that if an email is identified as spam one, the receiver’s
bandwidth and memory is preserved which will assure a better performance. Finally, by locating
the filtering system in the sender mail server; the processed time becomes n times less than the
time when the filtering system is in the receiver mail server when n indicates the number of
processed emails.
REFERENCES
[1] C. MacFarlane, (2003), “FTC Measures False Claims Inherent in Random Spam,” Federal Trade
Commission, http://www.ftc.gov/opa/2003/04/spamrpt.shtm, Accessed Jul. 20, 2011.
[2] L. Nosrati & A. Nemaney Pour, “Dynamic Concept Drift Detection for Spam Email Filtering,”
Proceedings of ACEEE 2nd International Conference on Advances Information and Communication
Technologies (ICT 2011), Amsterdam, Netherlands, pp. 124-126, Dec. 2011.
[3] A. Ramachandran, D. Dagon & N. Feamster, “Can DNS-Based Blacklists Keep Up with Bots?,” The
Third Conference on Email and Anti-Spam (CEAS 2006), California, USA, pp.1-2, Jul. 2006.
[4] J. Goodman, “Spam: Technologies and Policies,” White Paper, Microsoft research, pp.1-19, Feb.
2004.
10. International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012
62
[5] A.Ramachandran & N. Feamster, “Understanding the Network-Level Behavior of Spammers,”
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for
computer communications (SIGCOMM 2006), Pisa, Italy, pp. 291-302, Sep. 2006.
[6] E. Harris, (2003), “Greylisting: The Next Step in the Spam Control War,” White Paper,
http://projects.puremagic.com/greylisting/whitepaper.html, Accessed Dec. 20, 2011.
[7] J.R. Levine, “Experience with Greylisting,” Proceedings of Second Conference on Email and Anti-
Spam (CEAS 2005), CA, USA, pp. 1-2, Jul. 2005.
[8] P. Graham, “Better Bayesian filtering,” MIT Spam Conference, Jun. 2003..
[9] H. Yin & Z. Chaoyang, “An Improved Bayesian Algorithm for Filtering Spam E-Mail,” IEEE
2nd International Symposium on Intelligence Information Processing and Trusted Computing
(IPTC 2011), Huangzhou, China, pp. 87-90, Oct. 2011.
[10] A. Ciltik & T. Gungor, (2008), “Time-efficient spam e-mail filtering using n-gram models,” Elsevier,
Pattern Recognition Letters, Vol. 29, No. 1, pp. 19-33.
[11] B. Agrawal, N. Kumar & M. Molle, “Controlling Spam E-mail at the Routers,” Proceedings of the
IEEE International Conference on Communications (ICC 2005), Seoul, Korea, pp. 1588-1592, May
2005.
[12] R. Kholghi, S. Behnam Roudsari & A. Nemaney Pour, “An Efficient Spam Mail Detection by
Counter Technique,” Proceedings of International Conference on Computer Science and
Information Technology (ICCSIT 2011), Penang, Malaysia, pp. 22-24, Feb. 2011.
[13] Y. Luo, (2010), “Workload characterization of spam email filtering systems,” International Journal
of Network Security & Its Application (IJNSA), Vol. 2, No. 1, pp. 22-41.
Authors
Alireza Nemaney Pour1
has obtained his B.S degree in Computer Science
from Sanno University, Japan, M.S in Computer Science from Japan
Advanced Institute of Science And Technology, Japan, and Ph.D. degree in
Information Network Science from Graduate School of Information Systems,
the University of Electro-Communications, Japan.
He is currently a faculty member of Islamic Azad University of Abhar in Iran.
In addition, He is a technical advisor of J-Tech Corporation in Japan. His
research interests include Network Security, Group Communication Security,
Protocol Security, Information Leakage, Spam Mail Prevention, Web Spam
Detection, and Cryptography.
Raheleh Kholghi2
has received her B.Sc. from Azad University in software
engineering and M.Sc. degree in Information Technology Engineering from
Sharif University of Technology, International Campus, Iran in 2011. She
works currently in Telecommunication Kish Company (TKC) as an IT engineer.
Her research area is about network and its application security.
Soheil Behnam Roudsari2
has received his B.Sc. in Information Technology
Engineering from Sharif University of Technology, International Campus, Iran.
He is currently a M.Sc. student in Engineering of Computer Systems,
Politecnico Di Milano University, Italy. His area of research interest is Network
Security.