AI-Powered Cybersecurity Threat Analysis

Abstract

Cyber threats, such as phishing attacks and unusual user activity, present serious challenges to onlinesecurity, often resulting in data breaches and financial damage. Traditional security methods rely on fixed rules,making it difficult to detect evolving threats effectively. This project introduces an AI-driven cybersecurity system that identifies phishing attempts and detects anomalies in user behavior. The system applies Natural Language Processing (NLP) and machine learning techniques to examine email content, URLs, and network activity for potential phishing risks. Simultaneously, anomaly detection models monitor login patterns by analyzing IP addresses, devices, and timestamps to spot suspicious activity. By incorporatingRecurrent Neural Networks with Gated Recurrent Units (RNN-GRU) for sequence-based threat detection andtransformer-based models for deeper analysis, the system improves prediction accuracy. Additionally, a brute-force protection mechanism prevents unauthorized access by blocking users after multiple failed login attempts. An integrated admin dashboard provides real-time monitoring, enhancing threat response capabilities. This AI-powered approach ensures a dynamic, scalable, and proactive defense against modern cyber risks.