Design and Evaluation of a Bayesian-filter-based Image Spam Filtering Method

This research proposes a three-layer image-spam filtering system. The system filters the image spam by analyzing both the mail header and image. We elaborate the structure of the model and explicate carefully our idea of the design and many technologies related to the model. Experimental results show that this system has satisfactory filtering effect.

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An Expanded Feature Extraction of E-Mail Header for Spam Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1672 ◽

2013 ◽

Vol 846-847 ◽

pp. 1672-1675 ◽

Cited By ~ 2

Author(s):

Yuan Ning Liu ◽

Ye Han ◽

Xiao Dong Zhu ◽

Fei He ◽

Li Yan Wei

Keyword(s):

Machine Learning ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Spam Filtering ◽

Feature Sets ◽

Filtering Method ◽

The Past ◽

Machine Learning Methods ◽

E Mail

Currently a spam filtering method is extracting attributes from e-mail header and using machine learning methods to classify the sample sets. But as time goes on, spammers transform different ways to send spam, which result in a great change of spam's header. So the attributes defined in the past could not deal with this change sufficiently. This paper extracted attributes from all possible forged header fields to expand the feature sets, then used the rough set theory to classify the sample sets. Experiment validated more attributes including in feature sets may lead to greater performance, in terms of higher recall and precision, lower fake recognition than other algorithms.

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Detecting Computer Generated Images for Image Spam Filtering

2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) ◽

10.1109/acsat.2012.38 ◽

2012 ◽

Cited By ~ 1

Author(s):

Zubaidah Muataz Hazza ◽

Normaziah Abdul Aziz

Keyword(s):

Spam Filtering ◽

Image Spam

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A self-adaptable image spam filtering system

Journal of the Chinese Institute of Engineers ◽

10.1080/02533839.2013.815005 ◽

2013 ◽

Vol 37 (4) ◽

pp. 517-528 ◽

Cited By ~ 3

Author(s):

Tzong-Jye Liu ◽

Cheng-Nan Wu ◽

Chia-Lin Lee ◽

Ching-Wen Chen

Keyword(s):

Spam Filtering ◽

Image Spam

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Improved Bayesian-Based Spam Filtering Approach

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.401-403.1885 ◽

2013 ◽

Vol 401-403 ◽

pp. 1885-1891

Author(s):

Xue Jiang ◽

Jun Kai Yi

Keyword(s):

Beneficial Effect ◽

Experimental Result ◽

Bayesian Filtering ◽

Actual Situation ◽

Spam Filtering ◽

Bayesian Filter ◽

Filtering Algorithm ◽

Spam Filter ◽

Filter Approach ◽

Filtering Approach

Bayesian filtering approach is widely used in the field of anti-spam now. However, the two assumptions of this algorithm are significantly different with the actual situation so as to reduce the accuracy of the algorithm. This paper proposes a detailed improvement on researching of Bayesian Filtering Algorithm principle and implement method. It changes the priori probability of spam from constant figure to the actual probability, improves selection and selection rules of the token, and also adds URL and pictures to the detection content. Finally it designs a spam filter based on improved Bayesian filter approach. The experimental result of this improved Bayesian Filter approach indicates that it has a beneficial effect in the spam filter application.

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Image Spam Filtering using Machine Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1035.0782s419 ◽

2019 ◽

Vol 8 (2S4) ◽

pp. 186-190

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Character Recognition ◽

Optical Character Recognition ◽

Spam Filtering ◽

Data Filtering ◽

Visual Data ◽

Optical Character ◽

Image Spam ◽

Artificial Neural

Unsolicited visual data is undesirable in any form. The art of hiding malicious content in images and adding them as attachments to electronic mails has become a popular nuisance. In recent years, attackers have developed various new techniques to evade traditional spam classification systems. Text-based spam classification has been in focus for a long time and, researchers have successfully created a prodigal system for identifying spam text in electronic mails using Optical Character Recognition technology. In the last decade, extensive work has been performed to tackle image spam but with unsatisfactory results. Various algorithms and data augmentation techniques are used today to develop an optimal model for image spam recognition. Many of these proposed systems come close to the ideal system but do not provide 100 percent accuracy. This paper highlights the role of three popular techniques in image spam filtering. We discuss the importance and application of Optical Character Recognition, Support Vector Machines and, Artificial Neural Networks in unsolicited visual data filtering. This paper sheds light on the algorithms of these techniques. We provide a comparison of their accuracy, which helps us draw useful insights for developing a robust unsolicited visual data classification system. This paper aims to bring clarity regarding the feasibility of using these techniques to develop an unsolicited visual data filtering system. This paper records that the most favourable results are obtained using Artificial Neural Networks.

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Graph-Based Spam Image Detection for Mobile Phone Spam Image Filtering

International Journal of Software Innovation ◽

10.4018/ijsi.2015100106 ◽

2015 ◽

Vol 3 (4) ◽

pp. 72-86

Author(s):

So Yeon Kim ◽

Kyung-Ah Sohn

Keyword(s):

Mobile Phone ◽

Mobile Phones ◽

Clustering Algorithms ◽

Image Data ◽

Training Data ◽

Spam Filtering ◽

Visual Words ◽

Image Descriptors ◽

Image Spam ◽

E Mail

Spam images in mobile phones have increasingly appeared these days. As the spam filtering systems become more sophisticated, spams are being more intelligent. Although detection of email-spams has been quite successful, there have not been effective solutions for detecting mobile phone spams yet, especially, spam images. In addition to the expensive image processing time, insufficient spam image data in mobile phones makes it challenging to train a general model. To address this issue, the authors propose a graph-based approach that utilizes graph structure in abundant e-mail spam dataset. The authors employ different clustering algorithms to find a subset of e-mail spam images similar to phone spam images. Furthermore, the performance behavior with respect to different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is extensively investigated. The authors' results highlight that the proposed idea is fairly meaningful in increasing training data size, thus effectively improving image spam detection performance.

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Bayesian Spam Detection Framework on Mobile Device

Recent Patents on Computer Science ◽

10.2174/2213275912666190819121251 ◽

2019 ◽

Vol 12 ◽

Author(s):

Yitao Yang ◽

Guozi Sun ◽

Chengyan Qiu

Keyword(s):

Real Time ◽

Mobile Device ◽

Bayesian Classification ◽

Classification Algorithm ◽

Spam Detection ◽

Spam Filtering ◽

Learning Mechanism ◽

The Real ◽

Filtering Method ◽

Feedback Learning

In recent years, the spam message problem becomes more serious. Similar to spam mail, the spam message in phone brings a big trouble to users. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtering method. A Bayesian spam detection framework is designed in the paper and is deployed on Android device to test. Besides it can filtering coming messages and classify them into normal or spam in real time, it introduces feedback learning mechanism to make its result more accurate. The experiments are conducted under the real environment. The results show that the framework can meet the requirement of spam filtering.

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