Tightening the net: A review of current and next generation spam filtering tools

Carpinter, James; Hunt, Ray

doi:10.1016/j.cose.2006.06.001

Cited by 71 publications

(53 citation statements)

References 10 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We did not take any measures to counter obfuscated words in the spam messages. Given that there are a large number of possibilities to disguise a word, most content-based spam filters will not be able to deobfuscate the text of a message efficiently (Carpinter & Hunt, 2006). Recently, an efficient complementary filter (Lee & Ng, 2005) has been demonstrated to be able to effectively deobfuscate text with high accuracy.…”

Section: Experimental Datamentioning

confidence: 99%

“…Given that there are millions of e-mail users, profit-driven spammers have great incentives to spam. With as little as 0.001% response rate, a spammer could potentially profit $25,000 on a $50 product (Carpinter and Hunt, 2006). Over the years, spammers have grown in sophistication with cutting-edge technologies and have become more evasive.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Countering Good Word Attacks on Statistical Spam Filters with Instance Differentiation and Multiple Instance Learning

Zhou¹,

Jorgensen²,

Inge³

2008

Tools in Artificial Intelligence

View full text Add to dashboard Cite

Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Countering Good Word Attacks on Statistical Spam Filters with Instance Differentiation and Multiple Instance Learning

Zhou¹,

Jorgensen²,

Inge³

2008

Tools in Artificial Intelligence

View full text Add to dashboard Cite

“…The main content analysis studies are naïve Bayes, support vector machines, artificial neural networks, logistic regression, lazy learning, artificial immune systems, boosting, ensembles, image analysis, and hybrid methods. The authors in [3][4][5] classified and evaluated these methods in their studies.…”

Section: Operations Of Antispammersmentioning

confidence: 99%

“…On the other hand, a great many methods were proposed to block spams in academic and commercial circles and are being used [3][4][5][6]. However, these methods fall short, as statistics report that spam amounts have been increasing each day.…”

Section: Introductionmentioning

confidence: 99%

Stopping spam with sending session verification

Baran¹

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Spam has become one of the most significant problems for Internet communication and users today. The traditional filtering methods and signature-based spam blocking systems that have come into prominence recently fall short, as statistics demonstrate that spam further mounts up day after day. In this study, a new method is recommended to block spam mails. In this recommended method, a mail sending session is verified during mail envelope communication by adding small attachments to the simple mail transfer protocol, so spam mails can be blocked before leaving the sender domain or relay machine. Additionally, hijacked user accounts are able to be detected by statistical filtering software support, and spam mails coming from these users can be blocked and the account owners can be informed. According to the test results, 99.4% of the spam mails were able to be stopped by the proposed method, and the false positive problem was able to be solved and a significant decrease in the false negative percentages was ensured.

show abstract

“…The term spam was used to define the undesirable message, junk-mails sent to web users' inbox. It is most opportune for email spammers to send lots of messages to millions of users simply and without cost [2]. This makes it a public situation for all web users to receive unsolicited email regularly.…”

Section: Introductionmentioning

confidence: 99%

Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

Osman¹,

Aljahdali²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-This research proposed an improved filtering spam technique for suspected emails, messages based on feature weight and the combination of two-step clustering and logistic regression algorithm. Unique, important features are used as the optimum input for a hybrid proposed approach. This study adopted a spam detector model based on distance measure and threshold value. The aim of this model was to study and select distinct features for email filtering using feature weight method as dimension reduction. Two-step clustering algorithm was used to generate a new feature called "Label" to cluster and differentiate the diversity emails and group them based on the inter samples similarity. Thereby the spam filtering process was simplified using the Logistic regression classifier in order to distinguish the hidden patterns of spam and non-spam emails. Experimental design was conducted based on the UCI spam dataset. The outcome of the findings shows that the results of the email filtering are promising compared to other modern spam filtering methods.

show abstract

Tightening the net: A review of current and next generation spam filtering tools

Cited by 71 publications

References 10 publications

Countering Good Word Attacks on Statistical Spam Filters with Instance Differentiation and Multiple Instance Learning

Countering Good Word Attacks on Statistical Spam Filters with Instance Differentiation and Multiple Instance Learning

Stopping spam with sending session verification

Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

Contact Info

Product

Resources

About