@inproceedings{castillo-etal-2020-email,
title = "Email Threat Detection Using Distinct Neural Network Approaches",
author = "Castillo, Esteban and
Dhaduvai, Sreekar and
Liu, Peng and
Thakur, Kartik-Singh and
Dalton, Adam and
Strzalkowski, Tomek",
editor = "Bhatia, Archna and
Shaikh, Samira",
booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.stoc-1.8",
pages = "48--55",
abstract = "This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.",
language = "English",
ISBN = "979-10-95546-39-9",
}
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<abstract>This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.</abstract>
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%0 Conference Proceedings
%T Email Threat Detection Using Distinct Neural Network Approaches
%A Castillo, Esteban
%A Dhaduvai, Sreekar
%A Liu, Peng
%A Thakur, Kartik-Singh
%A Dalton, Adam
%A Strzalkowski, Tomek
%Y Bhatia, Archna
%Y Shaikh, Samira
%S Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-39-9
%G English
%F castillo-etal-2020-email
%X This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.
%U https://aclanthology.org/2020.stoc-1.8
%P 48-55
Markdown (Informal)
[Email Threat Detection Using Distinct Neural Network Approaches](https://aclanthology.org/2020.stoc-1.8) (Castillo et al., STOC 2020)
ACL
- Esteban Castillo, Sreekar Dhaduvai, Peng Liu, Kartik-Singh Thakur, Adam Dalton, and Tomek Strzalkowski. 2020. Email Threat Detection Using Distinct Neural Network Approaches. In Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management, pages 48–55, Marseille, France. European Language Resources Association.