Android Malware Detection Against String Encryption Based Obfuscation

Bhakta, Dip; Yousuf, Mohammad Abu; Rana, Md. Sohel

doi:10.1007/978-981-19-9379-4_39

Cited by 2 publications

(1 citation statement)

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“…The classification of spam mail is a major challenge faced, as the adversaries are constantly updating the tools and techniques for the creation and dissemination of spam mails. Recent advancement in the area of machine learning [2,3] have proven to be useful in the area of spam classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Application of error level analysis in image spam classification using deep learning model

Singh,

Singh

2023

PLoS ONE

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Image spam is a type of spam that contains text information inserted in an image file. Traditional classification systems based on feature engineering require manual extraction of certain quantitative and qualitative image features for classification. However, these systems are often not robust to adversarial attacks. In contrast, classification pipelines that use convolutional neural network (CNN) models automatically extract features from images. This approach has been shown to achieve high accuracies even on challenge datasets that are designed to defeat the purpose of classification. We propose a method for improving the performance of CNN models for image spam classification. Our method uses the concept of error level analysis (ELA) as a pre-processing step. ELA is a technique for detecting image tampering by analyzing the error levels of the image pixels. We show that ELA can be used to improve the accuracy of CNN models for image spam classification, even on challenge datasets. Our results demonstrate that the application of ELA as a pre-processing technique in our proposed model can significantly improve the results of the classification tasks on image spam datasets.

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Section: Introductionmentioning

confidence: 99%