Detecting Image Spam Using Image Texture Features

Al-Duwairi, Basheer; Khater, Ismail M.; Al-Jarrah, Omar

doi:10.20533/ijisr.2042.4639.2013.0040

Cited by 15 publications

(11 citation statements)

References 11 publications

(10 reference statements)

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“…Performance evaluation of the proposed model compared to image spam filter [15] shows that the WRandom Tree and W-Random Forest classifier outperforms all the other classifiers with an average precision, recall and accuracy of 99.83%. For efficient comparison with [15] considering only Dredze and ISH data set, the proposed model shows 99.81% accuracy and precision as in the Table VII. The data mining solution in [14][15] proposes mainly image feature manipulation which requires more of cost and time investment. Hence, ReP-ETD proposes the detection of image spam in a cost effective way considering only text features because the main intention of spammers is Sales, Promotion And Marketing and practically, about only 2% of our inbox is clogged with image spam per week.…”

Section: I) Comparison Of Rep-etd With Work[14]mentioning

confidence: 99%

ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

Manek

Shamini

Bhat³

et al. 2014

2014 IEEE International Advance Computing Conference (IACC)

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Section: I) Comparison Of Rep-etd With Work[14]mentioning

confidence: 99%

ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

Manek

Shamini

Bhat³

et al. 2014

2014 IEEE International Advance Computing Conference (IACC)

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“…Basheer et al [24] compared decision tree, bias network, and random forest with texture fea -1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 [25] employed the texture features based on Wavelet, and applied Active Learning Clustering and feedback-driven semi-supervised support vector machine classification to detect image spams. Table 5 lists the best classification performance of [23] and [24]. It can be observed that, our method achieves better spam detection performance compared to other methods.…”

Section: Evaluation Of Image Spam Recognitionmentioning

confidence: 99%

A hybrid spam detection method based on unstructured datasets

et al. 2015

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Abstract:The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals. In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham subdictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach. Abstract The identification of non-genuine or malicious messages poses a variety of challenges due to the continuous changes in the techniques utilised by cyber-criminals.In this article, we propose a hybrid detection method based on a combination of image and text spam recognition techniques. In particular, the former is based on sparse representation based classification, which focuses on the global and local image features, and a dictionary learning technique to achieve a spam and a ham sub-dictionary. On the other hand, the textual analysis is based on semantic properties of documents to assess the level of maliciousness. More specifically, we are able to distinguish between meta-spam and real spam. Experimental results show the accuracy and potential of our approach.

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“…In order to handle these kinds of Email spam, techniques based on low-level image features [109][110][111][112][113] and a combination of OCR with low-level image features [114][115][116] are used. Another technique called Image Texture AnalysisBased Image Spam Filtering (ITA-ISF) [117] is proposed which can filter image spam based on image texture analysis. But with the passage of time, software developers are trying to swindle anti-spam at a new intensity by switching from image and graphic spam to excel spreadsheets, pdf documents, or even mp3 files and Zip file attachments which are mostly infected with malware [118].…”

Section: Future Trends and Challenges In Detecting Email Spamming Bmentioning

confidence: 99%

A Comprehensive Study of Email Spam Botnet Detection

Khan

Muhaya

et al. 2015

IEEE Commun. Surv. Tutorials

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The problem of Email spam has grown significantly over the past few years. It is not just a nuisance for users but also it is damaging for those who fall for scams and other attacks. This is due to the complexity intensification of Email spamming techniques which are advancing from traditional spamming (direct spamming) techniques to a more scalable, elusive and indirect approach of botnets for distributing Email spam messages. In this paper we first discuss the sources and architectures used by the spamming botnets for sending massive amount of email spam. Then we present detailed chronicles of spamming botnets which systematically describes the timeline of events and notable occurrences in the advancement of these spamming botnets. This paper also aims to represent a comprehensive analysis of particular Email spamming botnet detection techniques proposed in the literature. We attempt to categorize them according to both their nature of defense and method of detection, also revealing and comparing their advantages and disadvantages extensively. We also present a qualitative analysis of these techniques. Finally we summarize the future trends and challenges in detecting email spamming botnets.

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Detecting Image Spam Using Image Texture Features

Abstract: Abstract

Cited by 15 publications

References 11 publications

ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

ReP-ETD: A Repetitive Preprocessing technique for Embedded Text Detection from images in spam emails

A hybrid spam detection method based on unstructured datasets

A Comprehensive Study of Email Spam Botnet Detection

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