Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes. Recent researches have investigated the role of machine learning (ML) models detect URLs. By using ML algorithms, first, features URLs are extracted, and then different trained. The limitation this approach that it requires manual feature engineering does not consider se...

Malicious Web content poses a serious threat to the Internet, organizations and users. Current approaches to detecting malicious Web content employ high-powered honey clients to scan the Web for potentially malicious pages. These approaches, while effective at detecting malicious content, have the drawbacks of being few and far between, presenting a single snapshot in time of very dynamic pheno...

Malware authors have been using websites to distribute their products as a way to evade spam filters and classic anti-virus engines. Yet there has been relatively little work in modeling the behaviors and temporal properties of websites, as most research focuses on detecting whether a website distributes malware. In this paper we ask: How does web-based malware spread? We conduct an extensive s...

Compromised websites that redirect users to malicious websites are often used by attackers to distribute malware. These attackers compromise popular websites and integrate them into a drive-by download attack scheme to lure unsuspecting users to malicious websites. An incident response organization such as a CSIRT contributes to preventing the spread of malware infection by analyzing compromise...

Malicious Uniform Resource Locators (URLs) are the major issue posed by cybersecurity threats. Cyberattackers spread malicious URLs to carry out attacks such as phishing and malware, which lead unsuspecting visitors into scams, resulting in monetary loss information theft. The adoption of Quick Response (QR) codes with is a growing concern an open security issue. existing QR link detection scan...