Protect Users With Advanced Malicious URL Detection
Cyberattacks using malicious URLs are a critical threat to users and organizations alike. They’re used to launch virus attacks, download malware to computers and trick users into visiting malicious websites, which can lead to phishing scams, credential harvesting, identity theft and unauthorized system access. This is why most virus protection technology and advanced malware protection solutions include email scanning with URL analysis that identifies bad links to prevent users from clicking them.
Protect Users with Advanced Malicious URL Detection Solutions
Protect users with advanced malicious URL detection is often used as a way for attackers to exploit trust in the victim, steal login credentials or gain access to private systems. As such, identifying and blocking these URLs is a crucial step in cybersecurity that can reduce the risk of data compromise, financial harm and sustain economic stability.
However, it’s becoming increasingly difficult to differentiate safe from malicious URLs because threat actors are constantly finding new ways to evade antivirus scanners and security policies. For example, registering new domains, hijacking existing “trusted” domains and redirecting to unknown sites are common methods for evading traditional security measures.
Consequently, developing effective models to identify malicious URLs is a challenging task for the security industry. To improve security, it’s necessary to develop a more adaptive and dynamic defense mechanism that can effectively adapt to evolving threats. The current study aims to develop and evaluate several machine-learning models and instance selection methods for identifying malicious URLs. The evaluation of model performance was carried out using three assessment metrics: precision, recall, and F1 score. The results showed that model performances varied depending on the instance selection method employed, which significantly impacted the overall classification accuracy of the models.