A phishing attack is defined as a type of cybersecurity attack that uses URLs that lead to phishing sites and steals credentials and personal information. Since there is a limitation on traditional deep learning to detect phishing URLs from only the linguistic features of URLs, attempts have been made to detect the misclassified URLs by integrating security expert knowledge with deep learning. In this paper, a genetic algorithm is proposed to find combinatorial optimization of logic programmed constraints and deep learning from given 13 components, which are 12 rule-based symbol components and a neural component. The genetic algorithm explores numerous searching spaces of combinations of 12 rules with deep learning to get an optimal combination of the components. Experiments and 10-fold cross-validation with three different real-world datasets show that the proposed method outperforms the state-of-the-art performance of β -discrepancy integration approach by achieving a 1.47% accuracy and a 2.82% recall improvement. In addition, a post-analysis of the proposed method is performed to justify the feasibility of phishing URL detection via analyzing URLs that are misclassified from either the neural or symbolic networks.
|Title of host publication||Hybrid Artificial Intelligent Systems - 16th International Conference, HAIS 2021, Proceedings|
|Editors||Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián, Emilio Corchado|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||13|
|Publication status||Published - 2021|
|Event||16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021 - Bilbao, Spain|
Duration: 2021 Sept 22 → 2021 Sept 24
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021|
|Period||21/9/22 → 21/9/24|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by an IITP grant funded by the Korean MSIT (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and a grant funded by Air Force Research Laboratory, USA.
© 2021, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)