[Paper Review] Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

Joseph M. Ackerson, Rushit Dave+1 more|arXiv (Cornell University)|Sep 13, 2021

Anomaly Detection Techniques and Applications48 references4 citations

TL;DR

This paper investigates the application of Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) and Deep-Residual RNNs, in biometric authentication, facial expression recognition, anomaly detection in IoT and aircraft systems, and aviation safety. It demonstrates that RNNs significantly improve sequential data modeling for temporal pattern recognition, achieving high F1 scores in anomaly detection and enhanced accuracy in biometric and trajectory prediction tasks.

ABSTRACT

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each pro-posed model.

Motivation & Objective

• To analyze current RNN-based methodologies in biometric authentication, expression recognition, anomaly detection, and aviation applications.
• To evaluate the effectiveness of LSTM and Deep-Residual RNN architectures in handling sequential data across diverse domains.
• To identify the benefits and limitations of RNN models in real-world deployment scenarios, particularly in security and safety-critical systems.
• To compare RNN performance against traditional models in terms of accuracy, false positive rates, and real-time processing capability.
• To guide future research by highlighting gaps in model robustness under noisy or variable conditions.

Proposed method

• Utilizes Long Short-Term Memory (LSTM) RNNs to model temporal dependencies in sequential biometric data such as mouse movements, keystrokes, and handwriting.
• Employs stacked RNN frameworks with multiple layers to improve feature representation and anomaly detection in time-series sensor data.
• Integrates Deep-Residual RNNs into aircraft trajectory simulations to model complex dynamics and improve prediction accuracy over numerical methods.
• Applies RNNs in conjunction with Convolutional Neural Networks (CNNs) for facial feature extraction before emotion classification using LSTM layers.
• Implements RNN-based Intrusion Detection Systems (IDS) for IoT devices to detect anomalous network behavior from sequential data streams.
• Evaluates model performance using metrics such as F1 score, precision, recall, and real-time inference latency across diverse datasets.

Experimental results

Research questions

• RQ1 How do LSTM and Deep-Residual RNNs improve biometric authentication accuracy compared to traditional models?
• RQ2 To what extent can RNNs enhance expression recognition in varying lighting and pose conditions?
• RQ3 What are the key challenges in deploying RNNs for real-time anomaly detection in IoT and aircraft systems?
• RQ4 How does integrating aircraft dynamics into RNN-based trajectory models affect prediction accuracy and robustness?
• RQ5 What are the trade-offs between model complexity, false positive rates, and real-time performance in RNN-based anomaly detection?

Key findings

• LSTM-based models achieved high F1 scores in anomaly detection tasks, indicating strong balance between precision and recall in identifying deviations from normal behavior.
• Deep-Residual RNNs outperformed traditional numerical simulation methods in aircraft trajectory prediction by effectively modeling complex dynamics and reducing prediction errors.
• Stacked RNN frameworks showed improved anomaly detection capability but faced challenges in defining clear anomaly thresholds due to high-dimensional, multi-variable inputs.
• RNNs integrated with CNNs significantly enhanced facial expression recognition by capturing temporal dynamics in sequential video frames.
• Real-time anomaly detection using LSTM RNNs was feasible but challenged by high data throughput and latency constraints in UAV and IoT environments.
• Noise in engine sensor data and environmental variability led to false positives and missed detections, highlighting the need for robust preprocessing and noise filtering in RNN applications.