PIMSI: A Partial Image Spam Inspector

Klangpraphant, Pattaraporn; Bhattarakosol, Pattarasinee

doi:10.1109/futuretech.2010.5482767

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Since image spam emails appeared in 2004, several studies were conducted to formally define image spam emails and construct models to detect image spam emails in academia. Klangpraphan et al [13] observed that image spam emails contain an image-based link to a website, which looks like a text. Soranamageswari et al [21] introduced the definition of image spam email as spam email having at least one image containing spam content.…”

Section: Image Spam Emailmentioning

confidence: 99%

DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

Kim

Abuadbba

Kim

2020

Preprint

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Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, but there is a significant performance degrade against entirely new and unseen image spam emails due to overfitting during the training phase. To address this challenging issue, we mainly focus on developing a more robust model to address the overfitting problem. Our key idea is to build a CNN-XGBoost framework consisting of eight layers only with a large number of training samples using data augmentation techniques tailored towards the image spam detection task. To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6,000 spam and 2,313 non-spam image samples. The experimental results show that DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM [19] with an F1-score of 82%. Moreover, DeepCapture outperformed existing image spam detection solutions against new and unseen image datasets.

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Section: Image Spam Emailmentioning

confidence: 99%

DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation

Kim

Abuadbba

Kim

2020

Preprint

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“…According to [107], the new inclination in Email spam consists of such Botnet attachments which include attached files like images and even more sophisticated types of graphical images in which like background noise is created in pictures, letters are replaced by images, the elements are rotated at different angles unique fonts or fonts of different sizes, animated spam and GIF images are used. 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.…”

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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“…OCR errors is considered as one of the drawbacks of this kind of filters, especially when spammers obscure the content of the image by adding noise, dots, changing the background colors and rotating images, which affects the efficiency of OCR text extraction. This fact has led to other techniques based on low-level image features [9] and a combination of OCR with low-level image features [10], [11], [12]). …”

Section: Ocr-based Techniquesmentioning

confidence: 99%