Abstract:
Phishing websites are the ones with same name and appearance as an offi cial website. Nowadays, URL phishing is increasing and are causing great
threats, they pretend to be the same as the official website and steal user
information. Earlier blacklists were used to identify these URLs; however,
they cannot identify the one-time Uniform Resource Locators (URL). Deep
learning methods are employed to avoid such risks and also to improve the
detection accuracy and decrease the misjudgment ratio. In URL detection
using residual pipelining, the common URL features along with some senti mental values are extracted and fed onto a residual pipeline. The residual
pipeline consists of convolutional blocks and inverted residual blocks. The
result obtained from this block is finally passed onto the MLP output layer
where the actual detection and classification of an URL takes place. The
dataset is obtained from Kaggle. The accuracy, precision, F1 score, and re call are being measured and it is observed that it is much higher than those
of several other traditional algorithms
