Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques

Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks aga...

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Main Authors: Jorge E. Coyac-Torres, Grigori Sidorov, Eleazar Aguirre-Anaya, Gerardo Hernández-Oregón
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Machine Learning and Knowledge Extraction
Subjects:
Online Access:https://www.mdpi.com/2504-4990/5/3/58
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author Jorge E. Coyac-Torres
Grigori Sidorov
Eleazar Aguirre-Anaya
Gerardo Hernández-Oregón
author_facet Jorge E. Coyac-Torres
Grigori Sidorov
Eleazar Aguirre-Anaya
Gerardo Hernández-Oregón
author_sort Jorge E. Coyac-Torres
collection DOAJ
description Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks against them. This work presents an approach based on natural language processing tools and a convolutional neural network architecture to detect and classify four types of cyberattacks in social network messages, including malware, phishing, spam, and even one whose aim is to deceive a user into spreading malicious messages to other users, which, in this work, is identified as a bot attack. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent of particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first stage detected the existence of any of the four types of cyberattacks within the message, achieving an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage was to classify it as one of the four types of cyberattack, achieving an accuracy value of 0.82.
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spelling doaj.art-04d014110e6f49b885eed91e4441c82a2023-11-19T11:41:49ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902023-09-01531132114810.3390/make5030058Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP TechniquesJorge E. Coyac-Torres0Grigori Sidorov1Eleazar Aguirre-Anaya2Gerardo Hernández-Oregón3Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoCentro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoCentro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoCentro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoSocial networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks against them. This work presents an approach based on natural language processing tools and a convolutional neural network architecture to detect and classify four types of cyberattacks in social network messages, including malware, phishing, spam, and even one whose aim is to deceive a user into spreading malicious messages to other users, which, in this work, is identified as a bot attack. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent of particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first stage detected the existence of any of the four types of cyberattacks within the message, achieving an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage was to classify it as one of the four types of cyberattack, achieving an accuracy value of 0.82.https://www.mdpi.com/2504-4990/5/3/58botCNNcyberattackdeep learningmalwareNLP
spellingShingle Jorge E. Coyac-Torres
Grigori Sidorov
Eleazar Aguirre-Anaya
Gerardo Hernández-Oregón
Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
Machine Learning and Knowledge Extraction
bot
CNN
cyberattack
deep learning
malware
NLP
title Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
title_full Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
title_fullStr Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
title_full_unstemmed Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
title_short Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
title_sort cyberattack detection in social network messages based on convolutional neural networks and nlp techniques
topic bot
CNN
cyberattack
deep learning
malware
NLP
url https://www.mdpi.com/2504-4990/5/3/58
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AT eleazaraguirreanaya cyberattackdetectioninsocialnetworkmessagesbasedonconvolutionalneuralnetworksandnlptechniques
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