Multimodal Spam Classification Using Deep Learning Techniques

Seth, Shikhar; Biswas, Sagar

doi:10.1109/sitis.2017.91

Cited by 25 publications

(9 citation statements)

References 3 publications

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“…SVM, AdaBoost, MLP, DNN, RF models are used in the research. Shikhar Seth [19] has completed a research on Enron Spam Dataset using deep learning technique. Ankit Narendrakumar Soni [20] also has done research on enron dataset using deep learning technique.…”

Section: Related Workmentioning

confidence: 99%

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Islam¹,

Amin²,

Islam³

et al. 2021

Preprint

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Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.<br>

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Section: Related Workmentioning

confidence: 99%

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Islam¹,

Amin²,

Islam³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…New deep network structures continued to be introduced. Using both text and image inputs, a multimodal network was proposed by [16] for spam classification. In this network structure, image and text are processed separately before being combined and further classified with a fully connected layer and finally a softmax layer.…”

Section: B Previous Methodsmentioning

confidence: 99%

“…Content features are the texts extracted from the email's subject and body while social features are calculated by building graphs [11] based on the email's sender and receiver address. The algorithms used in the various studies ranged from traditional machine learning models such as Bayesian classifier [11], support vector machine [12], support vector ordinal regression [13], artificial neural network [1] [9] to deep learning models such as multilayer perceptron (MLP) [17], stacked auto-encoders [14], temporal convolution network [16] and Long Short-Term Memory (LSTM) network [19]. The proposed works of classification or regression of emails into 3 or 5 importance levels has seen certain achievements.…”

Section: Introductionmentioning

confidence: 99%

Combining Content and Social Features in a Deep Learning Approach to Vietnamese Email Prioritization

Nguyen¹,

Dang

Tran

2021

REV J. Electron. Commun.

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The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

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“…Seth and Biswas [122] introduced Deep Learning techniques, such as CNN to tackle spam emails based on images and spam content. To classify e-mails containing both image and text, the authors have proposed two multi-modal architectures.…”

Section: B: Deep Learning Based Frameworkmentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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Multimodal Spam Classification Using Deep Learning Techniques

Cited by 25 publications

References 3 publications

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Combining Content and Social Features in a Deep Learning Approach to Vietnamese Email Prioritization

A Comprehensive Survey for Intelligent Spam Email Detection

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