Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments
Vishnukumar Ravula, Mangayarkarasi Ramaiah
TL;DR
This paper introduces a new model for detecting phishing URLs in cloud environments using deep learning and dynamic optimization, achieving high accuracy in identifying threats.
Contribution
The novel DAOA-DLPC model combines dynamic optimization with character-level deep learning for improved phishing URL detection in cloud environments.
Findings
The DAOA-DLPC model achieved 98.85% accuracy in phishing URL detection.
The model outperformed conventional methods with a recall of 98.49% and F1-score of 98.38%.
The model effectively reduces false positives and adapts to new phishing attack patterns in real time.
Abstract
As cloud computing becomes increasingly prevalent, the detection and prevention of phishing URL attacks are essential, particularly in the Internet of Vehicles (IoV) environment, to maintain service reliability. In such a scenario, an attacker could send misleading phishing links, potentially compromising the system’s functionality or, at worst, leading to a complete shutdown. To address these emerging threats, this study introduces a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model for cloud-enabled IoV infrastructure. The candidate’s research utilizes character-level embeddings instead of word embeddings, as the former can capture intricate URL patterns more effectively. These embeddings are integrated with a deep learning model, the Multi-Head Attention and Bidirectional Gated Recurrent Units (MHA-BiGRU). To…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsSpam and Phishing Detection · Text and Document Classification Technologies · Sentiment Analysis and Opinion Mining