[HTML][HTML] A systematic literature review on phishing website detection techniques
A Safi, S Singh - Journal of King Saud University-Computer and …, 2023 - Elsevier
Phishing is a fraud attempt in which an attacker acts as a trusted person or entity to obtain
sensitive information from an internet user. In this Systematic Literature Survey (SLR) …
sensitive information from an internet user. In this Systematic Literature Survey (SLR) …
[HTML][HTML] Mitigation strategies against the phishing attacks: A systematic literature review
Phishing attacks are among the most prevalent attack mechanisms employed by attackers.
The consequences of successful phishing include (and are not limited to) financial losses …
The consequences of successful phishing include (and are not limited to) financial losses …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
Business email compromise phishing detection based on machine learning: a systematic literature review
HF Atlam, O Oluwatimilehin - Electronics, 2022 - mdpi.com
The risk of cyberattacks against businesses has risen considerably, with Business Email
Compromise (BEC) schemes taking the lead as one of the most common phishing attack …
Compromise (BEC) schemes taking the lead as one of the most common phishing attack …
Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions
Steganography and steganalysis are two interrelated aspects of the field of information
security. Steganography seeks to conceal communications, whereas steganalysis aims to …
security. Steganography seeks to conceal communications, whereas steganalysis aims to …
Prompt engineering or fine-tuning? a case study on phishing detection with large language models
Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML)
application development. The emergence of versatile LLMs capable of undertaking a wide …
application development. The emergence of versatile LLMs capable of undertaking a wide …
Machine-learning techniques for predicting phishing attacks in blockchain networks: A comparative study
Security in the blockchain has become a topic of concern because of the recent
developments in the field. One of the most common cyberattacks is the so-called phishing …
developments in the field. One of the most common cyberattacks is the so-called phishing …
Deep learning for diverse data types steganalysis: A review
Steganography and steganalysis are two interrelated aspects of the field of information
security. Steganography seeks to conceal communications, whereas steganalysis is aimed …
security. Steganography seeks to conceal communications, whereas steganalysis is aimed …
Badlabel: A robust perspective on evaluating and enhancing label-noise learning
Label-noise learning (LNL) aims to increase the model's generalization given training data
with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different …
with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different …
Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review
In the continuously developing field of cyber security, user authentication and authorization
play a vital role in protecting personal information and digital assets from unauthorized use …
play a vital role in protecting personal information and digital assets from unauthorized use …