Advancing cybersecurity: a comprehensive review of AI-driven detection techniques

AH Salem, SM Azzam, OE Emam, AA Abohany - Journal of Big Data, 2024 - Springer
As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important
than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly …

[HTML][HTML] Mutual information based logistic regression for phishing URL detection

V Vajrobol, BB Gupta, A Gaurav - Cyber Security and Applications, 2024 - Elsevier
Phishing is a cybersecurity problem that hackers employ to deceive individuals and
organizations. Phishing is dynamic in nature; the hackers change several tricks to deceive …

A novel multiagent collaborative learning architecture for automatic recognition of mudstone rock facies

S Tewari, A Prasad, H Patel, M Uddin… - IEEE …, 2024 - ieeexplore.ieee.org
Recognizing mud rock lithofacies is essential for map** the subsurface depositional
environments and identifying oil and gas-bearing rock formations. Conventional well logs …

An ensemble learning approach for detecting phishing URLs in encrypted TLS traffic

C Kondaiah, AR Pais, RS Rao - Telecommunication Systems, 2024 - Springer
Phishing is a fraudulent method used by hackers to acquire confidential data from victims,
including security passwords, bank account details, debit card data, and other sensitive …

[HTML][HTML] Phishing Webpage Detection via Multi-Modal Integration of HTML DOM Graphs and URL Features Based on Graph Convolutional and Transformer Networks

JH Yoon, SJ Buu, HJ Kim - Electronics, 2024 - mdpi.com
Detecting phishing webpages is a critical task in the field of cybersecurity, with significant
implications for online safety and data protection. Traditional methods have primarily relied …

SENTINEY: Securing ENcrypted mulTI-party computatIoN for Enhanced data privacY and phishing detection

F Hendaoui, S Hendaoui - Expert Systems with Applications, 2024 - Elsevier
Phishing attacks have recently become a real danger that threatens the security of sensitive
data. This research paper presents a new approach based on Secure Multi-Party …

[PDF][PDF] A machine learning algorithms for detecting phishing websites: A comparative study

MA Taha, HDA Jabar - Iraqi Journal for Computer Science and …, 2024 - iasj.net
Phishing website attacks are a type of cyber-attack in which perpetrators create fraudulent
websites that mimic legitimate platforms, such as online banking or social media, with the …

Transfer learning with ResNet50 for malicious domains classification using image visualization

FA Demmese, S Shajarian, S Khorsandroo - Discover Artificial Intelligence, 2024 - Springer
The Internet has become a vital part of our daily lives, serving as a hub for global
connectivity and a facilitator for seamless communication and information exchange …

DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification

AE Mahdaouy, S Lamsiyah, MJ Idrissi, H Alami… - arxiv preprint arxiv …, 2024 - arxiv.org
Detecting and classifying suspicious or malicious domain names and URLs is fundamental
task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and …

LLMs are one-shot URL classifiers and explainers

F Rashid, N Ranaweera, B Doyle, S Seneviratne - Computer Networks, 2025 - Elsevier
Malicious URL classification represents a crucial aspect of cybersecurity. Although existing
work comprises numerous machine learning and deep learning-based URL classification …