Threat Foresight: Web Threat Detection and Forecasting Trends and Insights
DOI:
https://doi.org/10.32628/IJSRST25122209Keywords:
Web Threat Forecasting, AI-driven cybersecurity, Generative AI for Threat Analysis, Cyber Threat Intelligence, Deep Learning for Cyber Threat Detection, Time Series Analysis in Cybersecurity, Anomaly Detection in Web Security, Machine, Learning-Based Threat Mitigation, Real-Time Cyber Threat Monitoring, Benchmarking AI vs. Traditional Security ApproachesAbstract
The increasing sophistication and frequency of web threats necessitate advanced analytics and forecasting techniques to mitigate potential cyber risks. Traditional security measures, while effective to some extent, often struggle to adapt to evolving cyber threats. The advent of Artificial Intelligence (AI) and Generative AI (GenAI) has introduced novel methodologies for detecting, analyzing, and predicting web-based threats. This review paper explores the landscape of web threat analytics, evaluates traditional and modern forecasting techniques, and examines the role of AI and GenAI in enhancing cybersecurity. Furthermore, it highlights the challenges, limitations, and future directions in web threat analytics to guide future research and development.
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