mlsanityData to 7 Oct 2026

Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

arXiv 2109.05701 ↗First posted 13 Sep 2021cs.LGcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 6 of its 51 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

6 of 51 references.

References · 6

  1. 2017Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networkscited by 40
  2. 2017Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Networkcited by 28
  3. 2020BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networkscited by 19
  4. 2018Network Traffic Anomaly Detection Using Recurrent Neural Networkscited by 11
  5. 2017RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Processcited by 6
  6. 2017Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibrationcited by 2

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Cited by year

Citing papers per year: 2021: 3; 2022: 9; 2023: 2; 2024: 1.

15 papers.

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  1. 2022Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learningcited by 12
  2. 2021Machine Learning Algorithms In User Authentication Schemescited by 11
  3. 2021GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learningcited by 8
  4. 2022Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learningcited by 8
  5. 2022A Close Look into Human Activity Recognition Models using Deep Learningcited by 8
  6. 2022Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learningcited by 6
  7. 2022Using Deep Learning to Detecting Deepfakescited by 6
  8. 2021Continuous Authentication Using Mouse Movements, Machine Learning, and Minecraftcited by 4
  9. 2022A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluationcited by 3
  10. 2023Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVMcited by 3
  11. 2023Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamicscited by 1
  12. 2024PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealingcited by 1
3 more
  1. 2022Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authenticationcited by 0
  2. 2022AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmapcited by 0
  3. 2022Mitigating Presentation Attack using DCGAN and Deep CNNcited by 0