Biotrap: Biometric Honeytrap Security System

Traditional authentication and access control sys-tems in contemporary cybersecurity environments mainly follow a binary allow-or-deny model, which restricts their capacity to comprehend attacker intent or obtain useful intelligence. Even though these systems might be effective in preventing unwanted access, they frequently fall short in identifying, evaluating, and correlating malicious activity across multiple intrusion attempts. As a result, chances for intelligence-driven defense and long-term threat profiling are lost. This paper introduces BioTrap, a Biometric Honeytrap Security System that combines behavioral biometrics, deep learning-based anomaly detection, and deception technology. A completely isolated honeytrap environment that mimics authentic application interfaces is where suspicious login attempts are dynamically redirected. This decoy system continu-ously records and analyzes fine-grained behavioral signals, such as mouse movements, keystroke dynamics, navigation patterns, and session timing. These time-series interactions are processed by a Long Short-Term Memory (LSTM) model to produce anomaly scores and compact behavioral embeddings, which are then saved in a vector database for attacker correlation and crosssession similarity analysis. BioTrap converts intrusion attempts into opportunities for intelligence gathering through the combination of deception-based engagement and real-time behavioral analysis. The proposed system is designed for scal-ability in the cloud, maintains privacy through anonymization and data retention policies, and enables adaptive multi-factor authentication to reduce false positives. The effectiveness of BioTrap in distinguishing between malicious and legitimate users and enabling long-term behavioral fingerprinting and threat attribution is validated through experimental evaluation.