ThreatMesh: Continuous Intelligence Sharing for Insider Risk and Malware Domain

Abstract

Web security systems capable of monitoring user behaviour and preventing access to harmful domains. This research focuses on developing a web-based threat detection and monitoring platform that enhances organizational cybersecurity by identifying potentially malicious websites and tracking employee browsing activities. As organizations increasingly rely on internet resources, protecting users from malware, phishing, and unsafe domains remains a critical challenge. The key problem addressed is the absence of real time systems that classify domains and block harmful content proactively. Traditional monitoring approaches depend on manual inspection and static blacklists, making them reactive, slow, and ineffective against evolving threats. To overcome this, the proposed system uses a Django based application integrated with the Multiverse threat intelligence API for real time domain classification. It enables administrators to manage users, monitor activity, log threats, and visualize data. Domain validation and IP analysis prevent unsafe access while maintaining detailed records. This system improves awareness, supports proactive defence, strengthens monitoring, and enables informed decisions to safeguard organizational networks from emerging cyber risks. It also reduces response time, enhances automation, and minimizes human intervention in threat detection. By leveraging real time intelligence, the platform adapts quickly to new attack patterns and provides scalable protection for modern digital environments.