Malicious URL classification using artificial fish swarm optimization and deep learning
Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their syst...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Tech Science Press
2023
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/101886/7/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm.pdf http://irep.iium.edu.my/101886/13/101886_Malicious%20URL%20classification%20using%20artificial%20fish%20swarm_SCOPUS.pdf http://irep.iium.edu.my/101886/ http://doi.org/10.32604/cmc.2023.031371 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Cybersecurity-related solutions have become familiar since it
ensures security and privacy against cyberattacks in this digital era. Malicious
Uniform Resource Locators (URLs) can be embedded in email or Twitter
and used to lure vulnerable internet users to implement malicious data
in their systems. This may result in compromised security of the systems,
scams, and other such cyberattacks. These attacks hijack huge quantities
of the available data, incurring heavy financial loss. At the same time,
Machine Learning (ML) and Deep Learning (DL) models paved the way
for designing models that can detect malicious URLs accurately and classify
them. With this motivation, the current article develops an Artificial Fish
Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL
Detection and Classification (AFSADL-MURLC) model. The presented
AFSADL-MURLC model intends to differentiate the malicious URLs from
genuine URLs. To attain this, AFSADL-MURLC model initially carries out
data preprocessing and makes use of glove-based word embedding technique.
In addition, the created vector model is then passed onto Gated Recurrent
Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is
applied to the proposed model to enhance the efficiency of GRU model. The
proposed AFSADL-MURLC technique was experimentally validated using
benchmark dataset sourced from Kaggle repository. The simulation results
confirmed the supremacy of the proposed AFSADL-MURLC model over
recent approaches under distinct measures |
|---|