Capsule Network for Cyberthreat Detection

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.

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