Image analysis for efficient categorization of image-based spam e-mail

Aradhye, Hrishikesh B.; Myers, Gregory; Herson, James A.

doi:10.1109/icdar.2005.135

Cited by 77 publications

(35 citation statements)

References 6 publications

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“…Second, e-mails can contain multiple images and it is not clear how to combine them towards a single prediction. One approach has been done by Aradhye et al [17]. They treated each image separately, avoiding this difficulty.…”

Section: Related Workmentioning

confidence: 99%

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Improved Spam Filter via Handling of Text Embedded Image E-mail

Youn¹,

Cho²

2015

Journal of Electrical Engineering and Technology

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-The increase of image spam, a kind of spam in which the text message is embedded into attached image to defeat spam filtering technique, is a major problem of the current e-mail system. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of anti-spam filtering system. Recently, spammers try to defeat anti-spam filter by many techniques. Text embedding into attached image is one of them. We proposed an ontology spam filters. However, the proposed system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add image e-mail handling capability into the anti-spam filtering system keeping the advantages of the previous text based spam e-mail filtering system. Also, the proposed system gives a low false negative value, which means that user's valuable e-mail is rarely regarded as a spam e-mail.

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Section: Related Workmentioning

confidence: 99%

“…The classification can then be fed into existing content filters as a feature. Others have followed this approach [17] and it had several advantages. First, it separates image classification from spam e-mail classification, which is a difficult and well-studied problem.…”

Section: Related Workmentioning

confidence: 99%

Improved Spam Filter via Handling of Text Embedded Image E-mail

Youn¹,

Cho²

2015

Journal of Electrical Engineering and Technology

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“…Recently, spammers have used distorted and noisy images to convey text messages, much attention has been paid to the specific problem of extracting features from these images [8,14,22,52,62,186], however, the overall impact of image-specific techniques has yet to be established [74].…”

Section: Tokenizationmentioning

confidence: 99%

Email Spam Filtering: A Systematic Review

Cormack

2008

FNT in Information Retrieval

199

141

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Spam is information crafted to be delivered to a large number of recipients, in spite of their wishes. A spam filter is an automated tool to recognize spam so as to prevent its delivery. The purposes of spam and spam filters are diametrically opposed: spam is effective if it evades filters, while a filter is effective if it recognizes spam. The circular nature of these definitions, along with their appeal to the intent of sender and recipient make them difficult to formalize. A typical email user has a working definition no more formal than "I know it when I see it." Yet, current spam filters are remarkably effective, more effective than might be expected given the level of uncertainty and debate over a formal definition of spam, more effective than might be expected given the state-of-the-art information retrieval and machine learning methods for seemingly similar problems. But are they effective enough? Which are better? How might they be improved? Will their effectiveness be compromised by more cleverly crafted spam?We survey current and proposed spam filtering techniques with particular emphasis on how well they work. Our primary focus is spam filtering in email; Similarities and differences with spam filtering in other communication and storage media -such as instant messaging and the Web -are addressed peripherally. In doing so we examine the definition of spam, the user's information requirements and the role of the spam filter as one component of a large and complex information universe. Well-known methods are detailed sufficiently to make the exposition self-contained, however, the focus is on considerations unique to spam. Comparisons, wherever possible, use common evaluation measures, and control for differences in experimental setup. Such comparisons are not easy, as benchmarks, measures, and methods for evaluating spam filters are still evolving. We survey these efforts, their results and their limitations. In spite of recent advances in evaluation methodology, many uncertainties (including widely held but unsubstantiated beliefs) remain as to the effectiveness of spam filtering techniques and as to the validity of spam filter evaluation methods. We outline several uncertainties and propose experimental methods to address them.

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“…filters that learn and identify textual patterns of spam messages, are used by most ESPs [7,8]. Unfortunately, spammers have developed their own techniques to circumvent content based filters, such as image-spam mails [9] and hashbusters [10].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Outgoing Spammers in Large-Scale Service Provider Networks

Cohen

Gordon

Hendler

2013

Detection of Intrusions and Malware, and Vulnerability Assessment

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Abstract. We present ErDOS, an Early Detection scheme for Outgoing Spam. The detection approach implemented by ErDOS combines content-based detection and features based on inter-account communication patterns. We define new account features, based on the ratio between the numbers of sent and received emails and on the distribution of emails received from different accounts. Our empirical evaluation of ErDOS is based on a real-life data-set collected by an email service provider, much larger than data-sets previously used for outgoing-spam detection research. It establishes that ErDOS is able to provide early detection for a significant fraction of the spammers population, that is, it identifies these accounts as spammers before they are detected as such by a content-based detector. Moreover, ErDOS only requires a single day of training data for providing a high-quality list of suspect accounts.

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Image analysis for efficient categorization of image-based spam e-mail

Cited by 77 publications

References 6 publications

Improved Spam Filter via Handling of Text Embedded Image E-mail

Improved Spam Filter via Handling of Text Embedded Image E-mail

Email Spam Filtering: A Systematic Review

Early Detection of Outgoing Spammers in Large-Scale Service Provider Networks

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