Coyac-Torres, J.E.; Sidorov, G.; Aguirre-Anaya, E.; Hernández-Oregón, G. Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques. Mach. Learn. Knowl. Extr.2023, 5, 1132-1148.
Coyac-Torres, J.E.; Sidorov, G.; Aguirre-Anaya, E.; Hernández-Oregón, G. Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques. Mach. Learn. Knowl. Extr. 2023, 5, 1132-1148.
Coyac-Torres, J.E.; Sidorov, G.; Aguirre-Anaya, E.; Hernández-Oregón, G. Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques. Mach. Learn. Knowl. Extr.2023, 5, 1132-1148.
Coyac-Torres, J.E.; Sidorov, G.; Aguirre-Anaya, E.; Hernández-Oregón, G. Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques. Mach. Learn. Knowl. Extr. 2023, 5, 1132-1148.
Abstract
Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose purpose is to compromise digital assets of other members by using messages as an attack vector to execute different variants of cyberattacks against them. Therefore, this work presents an approach based on Natural Language Processing tools and a Convolutional Neural Network architecture to detect and classify, on social network messages, four types of cyberattacks, such as malware, phishing, spam, and even one whose purpose is deceiving the user into spreading malicious messages to other users, which in this work is identified as bot attacks. 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 from particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first detects the existence of any of the four cyberattacks within the message, obtaining an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage is to classify it into one of the four types of cyberattack, achieving an accuracy value of 0.82.
Keywords
bot; CNN; cyberattack; deep-learning; malware; NLP; phishing; social networks; spam
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
