[HTML][HTML] Applications of recurrent neural network for biometric authentication & anomaly detection

JM Ackerson, R Dave, N Seliya - Information, 2021 - mdpi.com
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 …

User authentication schemes using machine learning methods—a review

N Siddiqui, L Pryor, R Dave - Proceedings of International Conference on …, 2021 - Springer
With the recent advancements in technology, more and more people rely on their personal
devices to store their sensitive information. Concurrently, the environment in which these …

A modern analysis of aging machine learning based IOT cybersecurity methods

S Strecker, R Dave, N Siddiqui, N Seliya - arXiv preprint arXiv:2110.07832, 2021 - arxiv.org
Modern scientific advancements often contribute to the introduction and refinement of never-
before-seen technologies. This can be quite the task for humans to maintain and monitor …

Touch-based continuous mobile device authentication: State-of-the-art, challenges and opportunities

AZ Zaidi, CY Chong, Z Jin, R Parthiban… - Journal of Network and …, 2021 - Elsevier
The advancement in the computational capability and storage size of a modern mobile
device has evolved it into a multi-purpose smart device for individual and business needs …

An analysis of IoT cyber security driven by machine learning

S Strecker, W Van Haaften, R Dave - Proceedings of International …, 2021 - Springer
Since the beginning of the Internet of Things (IoT), the number of IoT devices connected to
the Internet has grown rapidly. However, many IoT devices lack the security standards that …

Hold on and swipe: a touch-movement based continuous authentication schema based on machine learning

J Mallet, L Pryor, R Dave, N Seliya… - … Asia Conference on …, 2022 - ieeexplore.ieee.org
In recent years, the amount of secure information being stored on mobile devices has grown
exponentially. However, current security schemas for mobile devices such as physiological …

Using deep learning to detecting deepfakes

J Mallet, R Dave, N Seliya… - 2022 9th International …, 2022 - ieeexplore.ieee.org
In the recent years, social media has grown to become a major source of information for
many online users. This has given rise to the spread of misinformation through deepfakes …

An Examination on Implementation of Deep Fake in Images Through Deep Learning

AK Saxena, RP KN - 2022 Fourth International Conference on …, 2022 - ieeexplore.ieee.org
Deep learning has been successfully used to address a variety of challenging problems,
including large-scale information analysis, PC vision, and human-level control. However …

Leveraging deep learning approaches for deepfake detection: A review

A Tiwari, R Dave, M Vanamala - … of the 2023 7th International Conference …, 2023 - dl.acm.org
Conspicuous progression in the field of machine learning (ML) and deep learning (DL) have
led the jump of highly realistic fake media, these media oftentimes referred as deepfakes …

Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning

WZ Tee, R Dave, N Seliya… - 2022 Asia Conference on …, 2022 - ieeexplore.ieee.org
Human activity recognition has grown in popularity with its increase of applications within
daily lifestyles and medical environments. The goal of having efficient and reliable human …