Distributed classification for image spam detection

Amir, Amiza; Srinivasan, Bala; Khan, Asad I.

doi:10.1007/s11042-017-4944-y

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…The papers [19][20][21][22][23] present the various approaches which are applied to classify an image as useful or useless. In [24], the author uses low-level features and the OCR technique using SVM classifier to obtain 95 per cent accuracy.…”

Section: Resultsmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

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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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Section: Resultsmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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“…This feature extraction method performs best in comparison with other distributed approaches with a relatively small amount of resources for spam detection. A 98% accuracy has been reached [28]. A Random Forest has the best accuracy, precision, recall, and F-measure than SVM and multilayer perceptron when PCA was used to construct a twitter-dataset image spam model.…”

Section: Spam Classification Techniques Analysis and Reviewmentioning

confidence: 97%

“…Text-based 9 [18], [19], [20], [21], [22], [23], [24], [25], [26] Image-based 16 [27], [28], [29], [9], [30], [31], [32], [33], [34], [35], [17], [5], [36], [37], [38], [39] [20], [30], [34], [5], [37], [38], [39] Dredze 10 5789 spam (spam =3239 & ham = 2550) [30], [31], [33], [34], [40], [17], [5], [37], [28], [21] Enron corpus 1 Not specified [22] SMS spam 1 Not specified [18] Princeton spam corpus [20], [24], [30], [31], [41], [32],…”

Section: No Of Studies Referencementioning

confidence: 99%

“…Convolutional Neural Network has recently been used to create a text-based spam classifier with the introduction of long short time memory neural network (LSTM NN) and an accuracy of more than 92-98% has been achieved [18]. [28], [44] used KNN and Naïve Bayes to implemented his work with the Dredze image dataset. The authors used a distributed associative memory tree to extract features of the image.…”

Section: Spam Classification Techniques Analysis and Reviewmentioning

confidence: 99%

“…Also, the authors revealed that fog-based services are faster than cloud-based services and that it is manageable and easy [20], [31], [32], [33], [34], [35], [17], [27], [9], [44] 4. K-Nearest Neighbor 3 y y [22], [28], [29], [44] 5 to implement a machine learning algorithm on fog nodes than on the cloud. In view of this, an algorithm can be implemented on a fog node to increase the detection speed of the image spam classification.…”

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confidence: 99%

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