Abuhamad, M., Abuhmed, T., Mohaisen, D., & Nyang, D. (2020). AUToSen: Deep-learning-based implicit continuous authentication using smartphone sensors. IEEE Internet of Things Journal, 7(6), 5008–5020.
Agrawal, K., & Bhatnagar, C. (2023). F-mim: Feature-based masking iterative method to generate the adversarial images against the face recognition systems. Journal of Information Technology Management, 15(Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-2022)), 80–93.
Alfaleh, K., Alabdultif, A., & Aladhadh, S. (2024). Artificial intelligence-driven cyberbullying detection: A survey of current techniques. Journal of Information Technology Management, 16(4), 38–63.
Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013, April). A public domain dataset for human activity recognition using smartphones. In Esann (Vol. 3, No. 1, pp. 3–4).
Antoniou, A., Storkey, A., & Edwards, H. (2017). Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Ehatisham-ul-Haq, M., Azam, M. A., Loo, J., Shuang, K., Islam, S., Naeem, U., & Amin, Y. (2017). Authentication of smartphone users based on activity recognition and mobile sensing. Sensors, 17(9), 2043.
Giorgi, G., Saracino, A., & Martinelli, F. (2021). Using recurrent neural networks for continuous authentication through gait analysis. Pattern Recognition Letters, 147, 157–163.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
Hajiakhoondi, E., Hashemzadeh Khorasgani, G., Rahmany Youshanlouei, H., & Mirkazemi Mood, M. (2013). Proposing a model to evaluate communication technologies in mobile communication industry. Journal of Information Technology Management, 5(4), 47–66.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kokal, S., Vanamala, M., & Dave, R. (2023). Deep learning and machine learning, better together than apart: A review on biometrics mobile authentication. Journal of Cybersecurity and Privacy, 3(2), 227–258.
Lu, C. X., Du, B., Zhao, P., Wen, H., Shen, Y., Markham, A., & Trigoni, N. (2018, October). Deepauth: In-situ authentication for smartwatches via deeply learned behavioural biometrics. In Proceedings of the 2018 ACM International Symposium on Wearable Computers (pp. 204–207).
Mangal, A., Garg, H., & Bhatnagar, C. (2023). Assessing the performance of Co-Saliency detection method using various deep neural networks. Journal of Information Technology Management, 15(Special Issue: EIntelligent and Security for Communication, Computing Application (ISCCA-2022)), 23–34.
Mekruksavanich, S., & Jitpattanakul, A. (2021). Deep learning approaches for continuous authentication based on activity patterns using mobile sensing. Sensors, 21(22), 7519.
Nayak, A., & Bansode, R. (2016). Analysis of knowledge-based authentication system using persuasive cued click points. Procedia Computer Science, 79, 553–560.
Qin, Z., Yang, S., & Zhong, Y. (2023). Hierarchically gated recurrent neural network for sequence modeling. Advances in Neural Information Processing Systems, 36, 33202–33221.
Sağbaş, E. A., & Ballı, S. (2024). Machine learning-based novel continuous authentication system using soft keyboard typing behavior and motion sensor data. Neural Computing and Applications, 36(10), 5433–5445.
Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., & Havinga, P. J. (2014). Fusion of smartphone motion sensors for physical activity recognition. Sensors, 14(6), 10146–10176.
Shoaib, M., Scholten, H., & Havinga, P. J. (2013, December). Towards physical activity recognition using smartphone sensors. In 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing (pp. 80–87). IEEE.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48.
Siddiqui, N., Dave, R., Vanamala, M., & Seliya, N. (2022). Machine and deep learning applications to mouse dynamics for continuous user authentication. Machine Learning and Knowledge Extraction, 4(2), 502–518.
Tran, L., & Choi, D. (2020). Data augmentation for inertial sensor-based gait deep neural network. IEEE Access, 8, 12364–12378.
Yang, Q., Peng, G., Nguyen, D. T., Qi, X., Zhou, G., Sitová, Z., ... & Balagani, K. S. (2014, November). A multimodal data set for evaluating continuous authentication performance in smartphones. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems (pp. 358–359).
Zhao, C., Gao, F., & Shen, Z. (2024). Multi-motion sensor behavior based continuous authentication on smartphones using gated two-tower transformer fusion networks. Computers & Security, 139, 103698.
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020). Deep learning-based gait recognition using smartphones in the wild. IEEE Transactions on Information Forensics and Security, 15, 3197–3212.