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DARK WEB GUARDIAN: REAL TIME THREAT DETECTION AND ANALYSIS
N. Vinuthna E. Keerthi G. Thanvisree
M. P. Nisha
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Abstract
The dark web represents a significant security threat due to its anonymity and the prevalence of illegal activities, including cybercrime, data breaches, and the sale of illicit goods. In response, real-time threat detection and analysis have become critical components of cybersecurity strategies. This paper introduces "Dark Web Guardian," a system designed to monitor and identify threats in real-time by analyzing dark web activities. The study focuses on the integration of advanced threat detection techniques, such as machine learning algorithms, behavioural analysis, and automated monitoring systems to track emerging risks. It also discusses the importance of real-time data analysis to prevent potential breaches before they escalate. Furthermore, the paper examines the role of collaboration between cybersecurity professionals, law enforcement, and private sector organizations in strengthening defenses against dark web-based threats. By leveraging innovative detection tools, "Dark Web Guardian" aims to provide proactive and dynamic protection against the evolving dangers lurking on the dark web.
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Vinuthna, N., et al.. "Dark Web Guardian: Real Time Threat Detection and Analysis." International Journal of Creative and Open Research in Engineering and Management, vol. , no. , , pp. . doi:https://doi.org/10.55041/ijcope.v2i2.193.
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- •Published on: Feb 20 2026
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