Abstract: In cybersecurity, analyzing social network data has become an essential research area due to its property of providing real-time updates about real-world events. Studies have shown that Twitter can contain information about security threats before some specialized sites. Thus, the classification of tweets into security-related and not security-related can help with early warnings for such attacks. In this study, the use of a capsule network (CapsNet), the new deep learning algo-rithm, is investigated for the first time in the field of security attack detection using Twitter. The aim was to increase the accuracy of tweet classification by using CapsNet rather than a convolutional neural network (CNN). To achieve the research objective, the original implementation of CapsNet with dynamic routing is adapted to be suitable for text analysis using tweet data set. A random search technique was used to tune the model’s hyperparameters. The experimental results showed that CapsNet exceeded the baseline CNN on the same data set, with accuracy of 92.21% and a 92.2% F1 score; also, word2vec embedding performed better than a random initialization.

Sahar Altalhi, Maysoon Abulkhair and Entisar Alkayal, “Capsule Network for Cyberthreat Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110673

@article{Altalhi2020,
title = {Capsule Network for Cyberthreat Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110673},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110673},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Sahar Altalhi and Maysoon Abulkhair and Entisar Alkayal}
}