Malicious Web Links Detection Based on Image Processing and Deep Learning Models (2024)

2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) Authors C. Coste Abstract The latest improvements regarding the online world have come with great benefits, but, as well as dangerous drawbacks (i.e., web-malware). This article proposes to investigate the reliability and accuracy of a novel web-malware detection method by using images…

Using Chat GPT for Malicious Web Links Detection (2024)

International Conference on Web Information Systems and Technologies Authors Thomas Kaisser, C. Coste Abstract Over the last years, the Internet has monopolized most businesses and industries. These outstanding advancements lead to the dangerous development of specialized threats employed to outsmart everyday users, collect personal data and financial benefits. One of the most relevant attacks…

Malicious Web Links Detection Using Ensemble Models (2023)

International Conference on Web Information Systems and Technologies Authors C. Coste, A. Andreica, C. Chira Abstract Malicious links are becoming the main propagating vector for web-malware. They may lead to serious security issues, such as phishing, distribution of fake news and low-quality content, drive-by-downloads, and malicious code running. Malware link detection is a challenging…

Malicious Web Links Detection – A Comparative Analysis of Machine Learning Algorithms (2023)

Studia Universitatis BabeČ™-Bolyai Informatica Authors C. Coste Abstract One of the most challenging categories of threats circulating into the online world is social engineering, with malicious web links, fake news, clickbait, and other tactics. Malware URLs are extremely dangerous because they represent the main propagating vector for web malware. Malicious web links detection is…