Email Image Spam Classification based on ResNet Convolutional Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00339961" target="_blank" >RIV/68407700:21230/20:00339961 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/20:00339961

Výsledek na webu

<a href="https://doi.org/10.5220/0008956704570464" target="_blank" >https://doi.org/10.5220/0008956704570464</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0008956704570464" target="_blank" >10.5220/0008956704570464</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Email Image Spam Classification based on ResNet Convolutional Neural Network

Popis výsledku v původním jazyce

The problem with email image spam classification is known from the year 2005. There are several approaches to this task. Lately, those approaches use convolutional neural networks (CNN). We propose a novel approach to the image spam classification task. Our approach is based on CNN and transfer learning, namely Resnet v1 used for semantic feature extraction and one layer Feedforward Neural Network for classification. We have shown that this approach can achieve state-of-the-art performance on publicly available datasets. 99% F1- score on two datasets (Dredze et al., 2007), Princeton and 96% F1-score on the combination of these datasets. Due to the availability of GPUs, this approach may be used for just-in-time classification in anti-spam systems handling huge amounts of emails. We have observed also that mentioned publicly available datasets are no longer representative. We overcame this limitation by using a much richer dataset from a one-week long real traffic of the freemail provider Email.cz. The training data annotation was created by user labeling of the emails. The image spam (and image ham even more) tackles privacy issues. We overcame it by publishing extracted feature vectors with associated classes (instead of images itself). This data does not violate privacy issues. We have published Email.cz image spam dataset v1 via the AcademicTorrents platform and propose a system, which achieves up to 96% F1-score with presented model architecture on this novel dataset. Providing our dataset to the community may help others with solving similar tasks.

Název v anglickém jazyce

Email Image Spam Classification based on ResNet Convolutional Neural Network

Popis výsledku anglicky

The problem with email image spam classification is known from the year 2005. There are several approaches to this task. Lately, those approaches use convolutional neural networks (CNN). We propose a novel approach to the image spam classification task. Our approach is based on CNN and transfer learning, namely Resnet v1 used for semantic feature extraction and one layer Feedforward Neural Network for classification. We have shown that this approach can achieve state-of-the-art performance on publicly available datasets. 99% F1- score on two datasets (Dredze et al., 2007), Princeton and 96% F1-score on the combination of these datasets. Due to the availability of GPUs, this approach may be used for just-in-time classification in anti-spam systems handling huge amounts of emails. We have observed also that mentioned publicly available datasets are no longer representative. We overcame this limitation by using a much richer dataset from a one-week long real traffic of the freemail provider Email.cz. The training data annotation was created by user labeling of the emails. The image spam (and image ham even more) tackles privacy issues. We overcame it by publishing extracted feature vectors with associated classes (instead of images itself). This data does not violate privacy issues. We have published Email.cz image spam dataset v1 via the AcademicTorrents platform and propose a system, which achieves up to 96% F1-score with presented model architecture on this novel dataset. Providing our dataset to the community may help others with solving similar tasks.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 6th International Conference on Information Systems Security and Privacy

ISBN

978-989-758-399-5

ISSN

2184-4356

e-ISSN

—

Počet stran výsledku

8

Strana od-do

457-464

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Valletta

Datum konání akce

25. 2. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000570766300047