* [141] S. Bazrafkan and P. Corcoran, “Enhancing iris authentication on handheld devices using deep learning derived segmentation techniques,” in _Proceedings of IEEE International Conference on Consumer Electronics (ICCE)_ , Las Vegas, NV, USA, January 2018.
  * [142] M. P. Centeno, A. van Moorsel, and S. Castruccio, “Smartphone continuous authentication using deep learning autoencoders,” in _Proceedings of 15th Annual Conference on Privacy, Security and Trust (PST)_ , Calgary, AB, Canada, August 2017.
  * [143] P. Chong, Y. X. M. Tan, J. Guarnizo, Y. Elovici, and A. Binder, “Mouse authentication without the temporal aspect – what does a 2d-cnn learn?” in _Proceedings of IEEE Security and Privacy Workshops (SPW)_ , San Francisco, CA, USA, May 2018.
  * [144] M. S. Zareen, S. Tahir, M. Akhlaq, and B. Aslam, “Artificial intelligence/ machine learning in iot for authentication and authorization of edge devices,” in _Proceedings of International Conference on Applied and Engineering Mathematics (ICAEM)_ , Taxila, Pakistan, August 2019.
  * [145] M. F. Bari, B. Chatteijee, and S. Sen, “Dirac: Dynamic-irregular clustering algorithm with incremental learning for rf-based trust augmentation in iot device authentication,” in _Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS)_ , Daegu, Korea, May 2021.
  * [146] X.-X. Shi, S.-L. Wang, and J.-Y. Lai, “Visual speaker authentication by ensemble learning over static and dynamic lip details,” in _Proceedings of IEEE International Conference on Image Processing (ICIP)_ , Phoenix, AZ, USA, September 2016.
  * [147] J. Liao, S. Wang, X. Zhang, and G. Liu, “3d convolutional neural networks based speaker identification and authentication,” in _Proceedings of 25th IEEE International Conference on Image Processing (ICIP)_ , Athens, Greece, October 2018.
  * [148] A. Ferdowsi and W. Saad, “Deep learning for signal authentication and security in massive internet-of-things systems,” _IEEE Transactions on Communications_ , vol. 67, no. 2, pp. 1371–1387, 2019.
  * [149] C. Qin, E. Liu, G. Feng, and X. Zhang, “Perceptual image hashing for content authentication based on convolutional neural network with multiple constraints,” _IEEE Transactions on Circuits and Systems for Video Technology (Early Access Article)_ , pp. 1–1, 2020.
  * [150] G. Gao and G. Jiang, “A zero-watermarking image authentication scheme using zernike moment and extreme learning machine,” in _Proceedings of International Conference on Wireless Communications and Signal Processing (WCSP)_ , Nanjing, China, November 2011.
  * [151] C.-Y. Chang and S.-J. Su, “Apply the counterpropagation neural network to digital image copyright authentication,” in _Proceedings of 9th International Workshop on Cellular Neural Networks and Their Applications_ , Hsinchu, Taiwan, May 2005.
  * [152] S. Upadhyay and S. K. Singh, “Learning based video authentication using statistical local information,” in _Proceedings of International Conference on Image Information Processing_ , Shimla, India, November 2011.
  * [153] A. Maier, B. Lorch, and C. Riess, “Toward reliable models for authenticating multimedia content: Detecting resampling artifacts with bayesian neural networks,” in _Proceedings of IEEE International Conference on Image Processing (ICIP)_ , Abu Dhabi, United Arab Emirates, October 2020.
  * [154] A. Weinand, R. Sattiraju, M. Karrenbauer, and H. D. Schotten, “Supervised learning for physical layer based message authentication in urllc scenarios,” in _Proceedings of IEEE 90th Vehicular Technology Conference (VTC)_ , Honolulu, HI, USA, September 2019.
  * [155] A. Weinand, M. Karrenbauer, R. Sattiraju, and H. Schotten, “Application of machine learning for channel based message authentication in mission critical machine type communication,” in _Proceedings of 23th European Wireless Conference_ , Dresden, Germany, May 2017.
  * [156] C. Kumar and A. K. Dudyala, “Bank note authentication using decision tree rules and machine learning techniques,” in _Proceedings of International Conference on Advances in Computer Engineering and Applications_ , Ghaziabad, India, March 2015.
  * [157] Haryono, K. Anam, and A. Saleh, “A novel herbal leaf identification and authentication using deep learning neural network,” in _Proceedings of International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)_ , Surabaya, Indonesia, November 2020.
  * [158] A. Ali, M. Testa, L. Markhasin, T. Bianchi, and E. Magli, “Adversarial learning of mappings onto regularized spaces for biometric authentication,” _IEEE Access_ , vol. 8, pp. 149 316–149 331, 2020.
  * [159] A. Ali, M. Testa, T. Bianchi, and E. Magli, “Authnet: Biometric authentication through adversarial learning,” in _Proceedings of IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)_ , Pittsburgh, PA, USA, October 2019.
  * [160] W. Zhou, X. Yuan, W. Chai, and H. Ma, “Deep learning based attack on social authentication system,” in _Proceedings of IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)_ , Chengdu, China, March 2019.
  * [161] S. Takemoto, K. Shibagaki, Y. Nozaki, and M. Yoshikawa, “Deep learning based attack for ai oriented authentication module,” in _Proceedings of 35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)_ , Nagoya, Japan, July 2020.
  * [162] S. Nakhodchi, B. Zolfaghari, A. Yazdinejad, and A. Dehghantanha, “Steeleye: An application-layer attack detection and attribution model in industrial control systems using semi-deep learning,” in _2021 18th International Conference on Privacy, Security and Trust (PST)_. IEEE, 2021, pp. 1–8.
  * [163] M.-D. Yu, M. Hiller, J. Delvaux, R. Sowell, S. Devadas, and I. Verbauwhede, “A lockdown technique to prevent machine learning on pufs for lightweight authentication,” _IEEE Transactions on Multi-Scale Computing Systems_ , vol. 2, no. 3, pp. 146–159, 2016.
  * [164] N. Chakraborty, J.-Q. Li, S. Mondal, C. Luo, H. Wang, M. Alazab, F. Chen, and Y. Pan, “On designing a lesser obtrusive authentication protocol to prevent machine-learning-based threats in internet of things,” _IEEE Internet of Things Journal_ , vol. 8, no. 5, pp. 3255–3267, 2021.
  * [165] J. D. Ndibwile, A. Govardhan, K. Okada, and Y. Kadobayashi, “Web server protection against application layer ddos attacks using machine learning and traffic authentication,” in _Proceedings of IEEE 39th Annual Computer Software and Applications Conference_ , Taichung, Taiwan, July 2015.
  * [166] M. Agarwal, S. Biswas, and S. Nandi, “Detection of de-authentication dos attacks in wi-fi networks: A machine learning approach,” in _Proceedings of IEEE International Conference on Systems, Man, and Cybernetics_ , Hong Kong, China, October 2015.
  * [167] S. Chen, Z. Pang, H. Wen, K. Yu, T. Zhang, and Y. Lu, “Automated labeling and learning for physical layer authentication against clone node and sybil attacks in industrial wireless edge networks,” _IEEE Transactions on Industrial Informatics_ , vol. 17, no. 3, pp. 2041–2051, 2021.
  * [168] C.-H. Chen and C.-Y. Chen, “Optimal fusion of multimodal biometric authentication using wavelet probabilistic neural network,” in _Proceedings of IEEE International Symposium on Consumer Electronics (ISCE)_ , Hsinchu, Taiwan, June 2013.
  * [169] L. Senigagliesi, M. Baldi, and E. Gambi, “Statistical and machine learning-based decision techniques for physical layer authentication,” in _Proceedings of IEEE Global Communications Conference (GLOBECOM)_ , Waikoloa, HI, USA, December 2019.
  * [170] J. Phiri and T. J. Zhao, “Using shannon’s information theory and artificial neural networks to implement multimode authentication,” in _Proceedings of International Conference on Communications and Intelligence Information Security_ , Xian, China, October 2010.
  * [171] M. Abuhamad, T. Abuhmed, D. Mohaisen, and D. Nyang, “Autosen: Deep-learning-based implicit continuous authentication using smartphone sensors,” _IEEE Internet of Things Journal_ , vol. 7, no. 6, pp. 5008–5020, 2020.
  * [172] A. Page, A. Kulkarni, and T. Mohsenin, “Utilizing deep neural nets for an embedded ecg-based biometric authentication system,” in _Proceedings of IEEE Biomedical Circuits and Systems Conference (BioCAS)_ , Atlanta, GA, USA, October 2015.
  * [173] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, and K.-K. R. Choo, “P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking,” _Computers & Security_, vol. 88, p. 101629, 2020.
  * [174] W.-H. Lee and R. B. Lee, “Implicit smartphone user authentication with sensors and contextual machine learning,” in _Proceedings of 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)_ , Denver, CO, USA, June 2017.
  * [175] J. Andrews, A. Vakil, and J. Li, “Biometric authentication and stationary detection of human subjects by deep learning of passive infrared (pir) sensor data,” in _Proceedings of IEEE Signal Processing in Medicine and Biology Symposium (SPMB)_ , Philadelphia, PA, USA, December 2020.
  * [176] L. Senigagliesi, M. Baldi, and E. Gambi, “Physical layer authentication techniques based on machine learning with data compression,” in _Proceedings of IEEE Conference on Communications and Network Security (CNS)_ , Avignon, France, June-July 2020.
  * [177] M. Kazemi and A. Yazdinejad, “Towards automated benchmark support for multi-blockchain interoperability-facilitating platforms,” _arXiv preprint arXiv:2103.03866_ , 2021.
  * [178] A. Yazdinejad, R. M. Parizi, G. Srivastava, A. Dehghantanha, and K.-K. R. Choo, “Energy efficient decentralized authentication in internet of underwater things using blockchain,” in _2019 IEEE Globecom Workshops (GC Wkshps)_. IEEE, 2019, pp. 1–6.
  * [179] A. M. Allam, H. M. Abbas, and M. W. El-Kharashi, “Authenticated key exchange protocol using neural cryptography with secret boundaries,” in _Proceedings of International Joint Conference on Neural Networks (IJCNN)_ , Dallas, TX, USA, August 2013.
  * [180] M. Firmino, G. B. Brandão, A. M. G. Guerreiro, and R. A. de M. Valentim, “Neural cryptography applied to key management protocol with mutual authentication in rfid systems,” in _Proceedings of International Conference for Internet Technology and Secured Transactions, (ICITST)_ , London, UK, November 2009.
  * [181] S. Noh and K.-H. Rhee, “Implicit authentication in neural key exchange based on the randomization of the public blockchain,” in _Proceedings of IEEE International Conference on Blockchain (Blockchain)_ , Rhodes, Greece, November 2020.
  * [182] D. Hu and Y. Wang, “Secure authentication on wimax with neural cryptography,” in _Proceedings of International Conference on Information Security and Assurance_ , Busan, Korea (South, April 2008.
  * [183] A. Baheti, L. Singh, and A. U. Khan, “Proposed method for multimedia data security using cyclic elliptic curve, chaotic system, and authentication using neural network,” in _Proceedings of Fourth International Conference on Communication Systems and Network Technologies_ , Bhopal, India, April 2014.
  * [184] Y. Sun, F. P.-W. Lo, and B. L. E. A. Article), “Light-weight internet-of-things device authentication, encryption and key distribution using end-to-end neural cryptosystems,” _IEEE Internet of Things Journal (Early Access Article)_ , pp. 1–1, 2021.
  * [185] B. Chatterjee, D. Das, and S. Sen, “Rf-puf: Iot security enhancement through authentication of wireless nodes using in-situ machine learning,” in _Proceedings of IEEE International Symposium on Hardware Oriented Security and Trust (HOST)_ , Washington, DC, USA, April 2018.
  * [186] A. Ferdowsi and W. Saad, “Deep learning-based dynamic watermarking for secure signal authentication in the internet of things,” in _Proceedings of IEEE International Conference on Communications (ICC)_ , Kansas City, MO, USA, May 2018.
  * [187] S. K. Narad and P. V. Chavan, “Neural network based group authentication using (n, n) secret sharing scheme,” in _Proceedings of International Conference on Advances in Computer Engineering and Applications_ , Ghaziabad, India, March 2015.
  * [188] E. A. Alkeem, S.-K. Kim, C. Y. Yeun, M. J. Zemerly, K. F. Poon, G. Gianini, and P. D. Yoo, “An enhanced electrocardiogram biometric authentication system using machine learning,” _IEEE Access_ , vol. 7, pp. 123 069–123 075, 2019\.
  * [189] S.-K. Kim, C. Y. Yeun, and P. D. Yoo, “An enhanced machine learning-based biometric authentication system using rr-interval framed electrocardiograms,” _IEEE Access_ , vol. 7, pp. 168 669–168 674, 2019\.
  * [190] S. K. Cherupally, S. Yin, D. Kadetotad, G. Srivastava, C. Bae, S. J. Kim, and J. sun Seo, “Ecg authentication hardware design with low-power signal processing and neural network optimization with low precision and structured compression,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 14, no. 2, pp. 198–208, 2020.
  * [191] S. K. Cherupally, G. Srivastava, S. Yin, D. Kadetotad, C. Bae, S. J. Kim, and J. sun Seo, “Ecg authentication neural network hardware design with collective optimization of low precision and structured compression,” in _Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS)_ , Sapporo, Japan, May 2019.
  * [192] I. Chamatidis, A. Katsika, and G. Spathoulas, “Using deep learning neural networks for ecg based authentication,” in _Proceedings of International Carnahan Conference on Security Technology (ICCST)_ , Madrid, Spain, October 2017.
  * [193] D. M, A. Jain, M. S.C, A. Melkot, and M. A.V, “Ecg based authentication using autocorrelation and artificial neural networks,” in _Proceedings of International Conference on Computing, Analytics and Security Trends (CAST)_ , Pune, India, December 2016.
  * [194] Y. Chen and W. Chen, “Finger ecg based two-phase authentication using 1d convolutional neural networks,” in _Proceedings of 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)_ , Honolulu, HI, USA, July 2018.
  * [195] J. R. Pinto and J. S. Cardoso, “An end-to-end convolutional neural network for ecg-based biometric authentication,” in _Proceedings of IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)_ , Tampa, FL, USA, September 2019.
  * [196] S. R. K. Gopal and D. Shukla, “Concealable biometric-based continuous user authentication system an eeg induced deep learning model,” in _Proceedings of IEEE International Joint Conference on Biometrics (IJCB)_ , Shenzhen, China, August 2021.
  * [197] S. Puengdang, S. Tuarob, T. Sattabongkot, and B. Sakboonyarat, “Eeg-based person authentication method using deep learning with visual stimulation,” in _Proceedings of 11th International Conference on Knowledge and Smart Technology (KST)_ , Phuket, Thailand, January 2019.
  * [198] T. Yu, C.-S. Wei, K.-J. Chiang, M. Nakanishi, and T.-P. Jung, “Eeg-based user authentication using a convolutional neural network,” in _Proceedings of 9th International IEEE/EMBS Conference on Neural Engineering (NER)_ , San Francisco, CA, USA, March 2019.
  * [199] T. MIYAKE, N. KINJO, and I. NAKANISHI, “Wavelet transform and machine learning-based biometric authentication using eeg evoked by invisible visual stimuli,” in _Proceedings of IEEE REGION 10 CONFERENCE (TENCON)_ , Osaka, Japan, November 2020.
  * [200] X. Zhang, Y. Zhang, L. Zhang, H. Wang, and J. Tang, “Ballistocardiogram based person identification and authentication using recurrent neural networks,” in _Proceedings of 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)_ , Beijing, China, October 2018.
  * [201] S. Shin, J. Jung, M. Kang, and Y. T. Kim, “A study on emg signal acquisition modules and artificial neural networks for personal authentication,” in _Proceedings of International Conference on Computational Science and Computational Intelligence (CSCI)_ , Las Vegas, NV, USA, December 2018.
  * [202] S. Shin, J. Jung, and Y. T. Kim, “A study of an emg-based authentication algorithm using an artificial neural network,” in _Proceedings of IEEE SENSORS_ , Glasgow, UK, October-November 2017.
  * [203] S. V. Joshi and R. D. Kanphade, “Deep learning based person authentication using hand radiographs: A forensic approach,” _IEEE Access_ , vol. 8, pp. 95 424–95 434, 2020.
  * [204] B. Moniga, D. Nandika, P. Monica, and V. Roopa, “Deep learning-based person authentication using convolutional neural architecture hand radiographs: A forensic approach,” in _Proceedings of 7th International Conference on Advanced Computing and Communication Systems (ICACCS)_ , Coimbatore, India, March 2021.
  * [205] S. Chauhan and K. Prema, “Effect of dimensionality reduction on performance in artificial neural network for user authentication,” in _Proceedings of 3rd IEEE International Advance Computing Conference (IACC)_ , Ghaziabad, India, February 2013.
  * [206] A. Tanaka, K. Kinoshita, and S. Kishida, “Effect of the neural network size on performance of authentication systems for fingerprints,” in _Proceedings of International Symposium on Intelligent Signal Processing and Communications Systems_ , Tamsui, Taiwan, November 2012.
  * [207] N. Harun, W. L. Woo, and S. S. Dlay, “Performance of keystroke biometrics authentication system using artificial neural network (ann) and distance classifier method,” in _Proceedings of International Conference on Computer and Communication Engineering (ICCCE)_ , Kuala Lumpur, Malaysia, May 2010\.
  * [208] Y. Gu, H. Yan, M. Dong, M. Wang, X. Zhang, Z. Liu, and F. Ren, “Wione: One-shot learning for environment-robust device-free user authentication via commodity wi-fi in man–machine system,” _IEEE Transactions on Computational Social Systems_ , vol. 8, no. 3, pp. 630–642, 2021.
  * [209] S. Palanivel, B. Venkatesh, and B. Yegnanarayana, “Real time face authentication system using autoassociative neural network models,” in _Proceedings of International Conference on Multimedia and Expo. ICME ’03. Proceedings (Cat. No.03TH8698)_ , Baltimore, MD, USA, July 2003.
  * [210] S. Sharma and S. Shaik, “Real time face authentication using convolutional neural network,” in _Proceedings of International Conference on Signal Processing (ICSP)_ , Vidisha, India, Novemberr 2016.
  * [211] A. Yazdinejad, A. Bohlooli, and K. Jamshidi, “Performance improvement and hardware implementation of open flow switch using fpga,” in _2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI)_. IEEE, 2019, pp. 515–520.
  * [212] B. Chakraborty, K. Nakano, Y. Tokoi, and T. Hashimoto, “An approach for designing low cost deep neural network based biometric authentication model for smartphone user,” in _Proceedings of IEEE Region 10 Conference (TENCON)_ , Kochi, India, October 2019.
  * [213] K. Sadeghi, A. Banerjee, J. Sohankar, and S. K. S. Gupta, “Performance and security strength trade-off in machine learning based biometric authentication systems,” in _Proceedings of 16th IEEE International Conference on Machine Learning and Applications (ICMLA)_ , Cancun, Mexico, December 2017.
  * [214] A. Yazdinejad, R. M. Parizi, G. Srivastava, and A. Dehghantanha, “Making sense of blockchain for ai deepfakes technology,” in _2020 IEEE Globecom Workshops (GC Wkshps_. IEEE, 2020, pp. 1–6.
  * [215] A. T. Kiyani, A. Lasebae, K. Ali, M. U. Rehman, and B. Haq, “Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach,” _IEEE Access_ , vol. 8, pp. 156 177–156 189, 2020.
  * [216] A. Yazdinejad, R. M. Parizi, A. Dehghantanha, and H. Karimipour, “Federated learning for drone authentication,” _Ad Hoc Networks_ , vol. 120, p. 102574, 2021.
  * [217] Y. Xun, J. Liu, and Z. Shi, “Multitask learning assisted driver identity authentication and driving behavior evaluation,” _IEEE Transactions on Industrial Informatics_ , vol. 17, no. 10, pp. 7093–7102, 2021.
  * [218] J. Liang, Y. Cao, C. Zhang, S. Chang, K. Bai, and Z. Xu, “Additive adversarial learning for unbiased authentication,” in _Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , Long Beach, CA, USA, June 2019.
  * [219] N. Gulati, R. Greenstadt, K. R. Dandekar, and J. M. Walsh, “Gmm based semi-supervised learning for channel-based authentication scheme,” in _Proceedings of IEEE 78th Vehicular Technology Conference (VTC Fall)_ , Las Vegas, NV, USA, September 2013.
  * [220] Z. Cui, Y. Zhao, C. Li, Q. Zuo, and H. Zhang, “An adaptive authentication based on reinforcement learning,” in _Proceedings of IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)_ , Yilan, Taiwan, May 2019\.
  * [221] A. Yazdinejad, E. Rabieinejad, A. Dehghantanha, R. M. Parizi, and G. Srivastava, “A machine learning-based sdn controller framework for drone management,” in _2021 IEEE Globecom Workshops (GC Wkshps)_. IEEE, 2021, pp. 1–6.
  * [222] F. Pan, H. Wen, R. Liao, Y. Jiang, A. Xu, K. Ouyang, and X. Zhu, “Physical layer authentication based on channel information and machine learning,” in _Proceedings of IEEE Conference on Communications and Network Security (CNS)_ , Las Vegas, NV, USA, October 2017.
  * [223] Y. Xun, J. Liu, N. Kato, Y. Fang, and Y. Zhang, “Automobile driver fingerprinting: A new machine learning based authentication scheme,” _IEEE Transactions on Industrial Informatics_ , vol. 16, no. 2, pp. 1417–1426, 2020.
  * [224] S. M. M. Islam, A. Rahman, E. Yavari, M. Baboli, O. Boric-Lubecke, and V. M. Lubecke, “Identity authentication of osa patients using microwave doppler radar and machine learning classifiers,” in _Proceedings of IEEE Radio and Wireless Symposium (RWS)_ , San Antonio, TX, USA, January 2020.
  * [225] A. Choubey and K. Chatterjee, “Secure remote user authentication for multi-server environment using machine learning technique,” in _Proceedings of International Conference on Circuit, Power and Computing Technologies (ICCPCT)_ , Nagercoil, India, March 2016.
  * [226] M. Misbahuddin, B. S. Bindhumadhava, and B. Dheeptha, “Design of a risk based authentication system using machine learning techniques,” in _Proceedings of IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)_, San Francisco, CA, USA, August 2017\.
  * [227] E. H. Enad and S. Younis, “Machine learning based decision stratigies for physical layer authentication in wireless systems,” in _Proceedings of 2nd Annual International Conference on Information and Sciences (AiCIS)_ , Fallujah, Iraq, November 2020.
  * [228] I. I. M. A. Sulayman and A. Ouda, “Human trait analysis via machine learning techniques for user authentication,” in _Proceedings of International Symposium on Networks, Computers and Communications (ISNCC)_ , Montreal, QC, Canada, October 2020.
  * [229] S. Singh, I. Das, M. G. Mohiuddin, A. Banerjee, and S. Gupta, “Design and implementation of gabor filter and svm based authentication system using machine learning,” in _Proceedings of Devices for Integrated Circuit (DevIC)_ , Kalyani, India, March 2019.
  * [230] K. Sadeghi, A. Banerjee, J. Sohankar, and S. K. S. Gupta, “Geometrical analysis of machine learning security in biometric authentication systems,” in _Proceedings of 16th IEEE International Conference on Machine Learning and Applications (ICMLA)_ , Cancun, Mexico, December 2017.
  * [231] S. D. Kaul and D. Hatzinakos, “Learning automata based secure multi agent rfid authentication system,” in _Proceedings of 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)_ , Kanpur, India, July 2019.
  * [232] Y. Hailemariam, A. Yazdinejad, R. M. Parizi, G. Srivastava, and A. Dehghantanha, “An empirical evaluation of ai deep explainable tools,” in _2020 IEEE Globecom Workshops (GC Wkshps_. IEEE, 2020, pp. 1–6.
  * [233] M. I. Obayya, M. El-Ghandour, and F. Alrowais, “Contactless palm vein authentication using deep learning with bayesian optimization,” _IEEE Access_ , vol. 9, pp. 1940–1957, 2021.
  * [234] C.-W. Chuang and C.-P. Fan, “Biometric authentication with combined iris and sclera information by yolo-based deep-learning network,” in _Proceedings of IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)_ , Taoyuan, Taiwan, September 2020.
  * [235] B. Hayes and J. V. Ringwood, “Student authentication for oral assessment in distance learning programs,” _IEEE Transactions on Learning Technologies_ , vol. 1, no. 3, pp. 165–175, 2008.
  * [236] A. Yazdinejad, H. HaddadPajouh, A. Dehghantanha, R. M. Parizi, G. Srivastava, and M.-Y. Chen, “Cryptocurrency malware hunting: A deep recurrent neural network approach,” _Applied Soft Computing_ , vol. 96, p. 106630, 2020.
  * [237] Z. Qin, G. Huang, H. Xiong, Z. Qin, and K.-K. R. Choo, “A fuzzy authentication system based on neural network learning and extreme value statistics,” _IEEE Transactions on Fuzzy Systems_ , vol. 29, no. 3, pp. 549–559, 2021\.
  * [238] M. Messerschmidt and M. Pleva, “Biometric systems utilizing neural networks in the authentication for e-learning platforms,” in _Proceedings of 17th International Conference on Emerging eLearning Technologies and Applications (ICETA)_ , Stary Smokovec, Slovakia, November 2019.
  * [239] Y. Chen and W. Chen, “Finger ecg-based authentication for healthcare data security using artificial neural network,” in _Proceedings of IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)_ , Dalian, China, October 2017.
  * [240] M. M. A. Abuqadumah, M. A. M. Ali, and R. R. O. Al-Nima, “Personal authentication application using deep learning neural network,” in _Proceedings of 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)_, Langkawi, Malaysia, February 2020.
  * [241] R. Shioji, S.-I. Ito, M. Ito, and M. Fukumi, “Personal authentication and hand motion recognition based on wrist emg analysis by a convolutional neural network,” in _Proceedings of 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS)_ , Toyama, Japan, December 2018.
  * [242] E. Derman and A. A. Salah, “Continuous real-time vehicle driver authentication using convolutional neural network based face recognition,” in _Proceedings of 13th IEEE International Conference on Automatic Face & Gesture Recognition_, Xian, China, May 2018.
  * [243] B. Moniga, D. Nandika, P. Monica, and V. Roopa, “Deep learning-based person authentication using convolutional neural architecture hand radiographs: A forensic approach,” in _Proceedings of 7th International Conference on Advanced Computing and Communication Systems (ICACCS)_ , Coimbatore, India, March 2021.
  * [244] R. Liao, H. Wen, F. Pan, H. Song, A. Xu, and Y. Jiang, “A novel physical layer authentication method with convolutional neural network,” in _Proceedings of IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)_ , Dalian, China, March 2019.
  * [245] Y. Chen, S. Real, H. Wen, B. Cheng, W. Wang, P.-H. Ho, and S. Y. Chang, “On physical-layer authentication via triple pool convolutional neural network,” in _Proceedings of IEEE Globecom Workshops (GC Wkshps)_ , Taipei, Taiwan, December 2020.
  * [246] H. Hu, Y. Li, Z. Zhu, and G. Zhou, “Cnnauth: Continuous authentication via two-stream convolutional neural networks,” in _Proceedings of IEEE International Conference on Networking, Architecture and Storage (NAS)_ , Chongqing, China, October 2018.
  * [247] H. Jun, S. Jinming, L. Shuai, L. Yue, W. Jingwei, and J. Peng, “Design of distance education authentication system based on deep convolutional neural netework,” in _Proceedings of Chinese Automation Congress (CAC)_ , Xian, China, November-December 2018.
  * [248] A. E. Sulavko, D. A. Volkov, S. S. Zhumazhanova, and R. V. Borisov, “Subjects authentication based on secret biometric patterns using wavelet analysis and flexible neural networks,” in _Proceedings of XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)_ , Novosibirsk, Russia, October 2018.
  * [249] K. S. Germain and F. Kragh, “Mobile physical-layer authentication using channel state information and conditional recurrent neural networks,” in _Proceedings of IEEE 93rd Vehicular Technology Conference (VTC)_ , Helsinki, Finland, April 2021.
  * [250] A. Yazdinejad, A. Dehghantanha, R. M. Parizi, M. Hammoudeh, H. Karimipour, and G. Srivastava, “Block hunter: Federated learning for cyber threat hunting in blockchain-based iiot networks,” _IEEE Transactions on Industrial Informatics_ , pp. 1–1, 2022.
  * [251] Y. Li, A. H. Aghvami, and D. Dong, “Intelligent trajectory planning in uav-mounted wireless networks: A quantum-inspired reinforcement learning perspective,” _IEEE Wireless Communications Letters (Early Access Article)_ , pp. 1–1, 2021.
  * [252] Q. Wei, H. Ma, C. Chen, and D. Dong, “Deep reinforcement learning with quantum-inspired experience replay,” _IEEE Transactions on Cybernetics (Early Access Article)_ , pp. 1–1, 2021.
  * [253] D. Dong, C. Chen, J. Chu, and T.-J. Tarn, “Robust quantum-inspired reinforcement learning for robot navigation,” _IEEE/ASME Transactions on Mechatronics_ , vol. 17, no. 1, pp. 86–97, 2012.
  * [254] N. Masuyama, C. K. Loo, M. Seera, and N. Kubota, “Quantum-inspired multidirectional associative memory with a self-convergent iterative learning,” _IEEE Transactions on Neural Networks and Learning Systems_ , vol. 29, no. 4, pp. 1058–1068, 2018.
  * [255] O. P. Patel, N. Bharill, A. Tiwari, and M. Prasad, “A novel quantum-inspired fuzzy based neural network for data classification,” _IEEE Transactions on Emerging Topics in Computing_ , vol. 9, no. 2, pp. 1031–1044, 2021.
  * [256] Y. Yuan, H. Ning, and X. Lu, “Bio-inspired representation learning for visual attention prediction,” _IEEE Transactions on Cybernetics_ , vol. 51, no. 7, pp. 3562–3575, 2021.
  * [257] M. Xu, F. Chen, L. Li, C. Shen, P. Lv, B. Zhou, and R. Ji, “Bio-inspired deep attribute learning towards facial aesthetic prediction,” _IEEE Transactions on Affective Computing_ , vol. 12, no. 1, pp. 227–238, 2021.
  * [258] S. Gibson, B. Issac, L. Zhang, and S. M. Jacob, “Detecting spam email with machine learning optimized with bio-inspired metaheuristic algorithms,” _IEEE Access_ , vol. 8, pp. 187 914–187 932, 2020.
  * [259] H. Lehnert, M. Araya, R. Carrasco-Davis, and M.-J. Escobar, “Bio-inspired deep reinforcement learning for autonomous navigation of artificial agents,” _IEEE Latin America Transactions_ , vol. 17, no. 12, pp. 2037–2044, 2019\.
  * [260] H. Duan, P. Li, Y. Shi, X. Zhang, and C. Sun, “Interactive learning environment for bio-inspired optimization algorithms for uav path planning,” _IEEE Transactions on Education_ , vol. 58, no. 4, pp. 276–281, 2015.
  * [261] Z. Yang, Y. Jin, and K. Hao, “A bio-inspired self-learning coevolutionary dynamic multiobjective optimization algorithm for internet of things services,” _IEEE Transactions on Evolutionary Computation_ , vol. 23, no. 4, pp. 675–688, 2019.
  * [262] Z. Tu, F. Fei, and X. Deng, “Bio-inspired rapid escape and tight body flip on an at-scale flapping wing hummingbird robot via reinforcement learning,” _IEEE Transactions on Robotics_ , vol. 37, no. 5, pp. 1742–1751, 2021.