# Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

arXiv: https://arxiv.org/abs/2109.05701 · HTML: https://mlsanity.com/paper/2109.05701/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks](https://mlsanity.com/paper/2001.10223/) (2020)
- [Network Traffic Anomaly Detection Using Recurrent Neural Networks](https://mlsanity.com/paper/1803.10769/) (2018)
- [RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process](https://mlsanity.com/paper/1709.02232/) (2017)
- [Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration](https://mlsanity.com/paper/1710.03753/) (2017)

## Built on by (most cited first, 15 of 15)

- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning](https://mlsanity.com/paper/2205.08371/) (2022)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraft](https://mlsanity.com/paper/2110.11080/) (2021)
- [A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation](https://mlsanity.com/paper/2201.12705/) (2022)
- [Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM](https://mlsanity.com/paper/2302.10280/) (2023)
- [Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics](https://mlsanity.com/paper/2305.09482/) (2023)
- [PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing](https://mlsanity.com/paper/2412.19284/) (2024)
- [Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication](https://mlsanity.com/paper/2204.09088/) (2022)
- [AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap](https://mlsanity.com/paper/2204.12492/) (2022)
- [Mitigating Presentation Attack using DCGAN and Deep CNN](https://mlsanity.com/paper/2207.00161/) (2022)

## Abstract (arXiv)

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.