Collated, Sequential and ML Approaches of Multi-Variant Features of Users on Authentication
Authors
-
Amity Institute of Information Technology Kolkata, Amity University Kolkata. India. ,IN
Supriya Chakraborty -
Amity Institute of Information Technology Kolkata, Amity University Kolkata. India. ,INArnab Chakraborty
-
Amity School of Engineering and Technology Kolkata, Amity University Kolkata, India. ,INHarshit Purohit
DOI:
https://doi.org/10.18311/jmmf/2023/34156Keywords:
Authentication, Collated Password, Sequential Password, ML Based Authentication, Biometrics.Abstract
In the recent days, gradually essential services and activities are becoming digitally enabled, including banking, electricity, water, etc. The envision is that digital attacks will not be limited to person, but tends to civil, caste or country wars. The authentication schemes allow the valid and original users to access such digital resources. The state-of-the-art study revealed four research questions on the sustainable and vulnerabilities on authentication schemes. These research questions are related to less interaction, resource requirements, coercive verses sequential features, and IT knowledge of users in the authentication process.
In this work, comprehensive experimental research is performed with different perspectives in the authentication process. The notion is to allow the users through different level of stringency in the authentication process to access from lower to high level of confidential digital resources. An organization have different impact on different digital resources. In such a context, three approaches different from each other are performed. The first approach coercively validates the authentication process whereas the second approach in sequential order. The objective is to find out the merits and demerits of each one, and in accordance with the level of confidentiality, the policy could be framed within the organization. The last approach uses the machine learning technique for the authentication process with no interactions of users. In all the approaches, the multiple features of users are considered for the experiment.
Downloads
Metrics
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Soumen Dutta (2022): Presented the Survey of Pricewaterhouse Coopper Private Limited - A Technical Litarature for Future Direction, July.
Dhatri Raval and Abhilash Shukla (2015): “Security using 3D Password”, CMPICA CHARUSAT, Changa, International Journal of Computer Applications (0975-8887) Volume 120 – No.7, June. DOI: https://doi.org/10.5120/21242-4024
Dinesha H A and Agrawal V K: “Multi-level Authentication Techniques for Accessing Cloud Services”. CORI, Bangalore. Tanvi Naik, Sheetal Koul Multi-Dimensional and Multi-Level Authentication Techniques,Information Technology, SKNCOE, Pune
Fawaz A. Alsulaiman, Abdulmotaleb El Saddik, (2008): “Three-Dimensional Password for More Secure Authentication”, IEEE transactions on instrumentation and measurement, vol. 57, no. 9 September. DOI: https://doi.org/10.1109/TIM.2008.919905
Fawaz A Alsulaiman and Abdulmotaleb El Saddik, (2006): “A Novel 3D Graphical Password Schema”, Multimedia Communications Research Laboratory University of Ottawa, Canada, IEEE 2006 International DOI: https://doi.org/10.1109/VECIMS.2006.250805
Grover Aman, Narang Winnie, (2012): “4-D Password: Strengthening the Authentication Scene”, Int. Journal of Scientific & Engineering Research, Volume 3, Issue 10, October, 1 ISSN 2229-5518
Jianhua Song1, Degang Wang, Zhongyue Yun1, Xiao Han1 “Alphapwd: APassword Generation Strategy Based on mnemonic Shape” School of Computer Science and Information Engineering, Hubei University, Hubei Wuhan 430062, DOI 10.1109/ ACCESS.2019.2937030, IEEE.
Karen Renaud”, Peter Mayer†, Melanie Volkamer† and Joseph Maguire, (2013): “Are Graphical Authentication Mechanisms As Strong As Passwords?”, IEEE
Mrs. Aakansha S. Gokhalea, Prof. Vijaya S.Waghmareb (2016): “The Shoulder Surfing Resistant Graphical Password Authentication Technique”, 7th International Conference on communication, Computing and Virtualization, published by Elesvier, Vol: 79. DOI: https://doi.org/10.1016/j.procs.2016.03.091
Ms. Vidya Mhaske Dhamdhere and Prof. G. A. Patil,” Three Dimensional Object Used for Data Security”, International Conference on Computational Intelligence and Communication networks, IEEE DOI 10.1109/CICN.2010.83,
Ms. Swati Bilapatte, Prof. Sumit Bhattacharjee, “3D Password: A novel approach for more secure authentication”, International Journal of Computer Science & Engineering Technology(IJCSET)
Neha, Mr. Vinod Saroha, (2016): “ Hybrid Approach of 3D Password Using Naive Bayes Classification”, IJCSMC, Vol. 5, Issue. 6.
Prof. Sudhakar Jadhav, Prathamesh Bhawtankar, “Securing the data using advance authentication technique”, GRD journals - Global Research and Development Journal for Engineering,Volume 1, Issue 7, June 2016 ISSN: 2455-5703
Paul C.V. Oorschot, Amirali Salehi-Abari, J. Thorpe, (2010): “Purely Automated Attacks on Pass Points-StyleGraphical Passwords”, IEEE Transactions on Information Forensics and Security, vol.5, No.3, Sept. DOI: https://doi.org/10.1109/TIFS.2010.2053706
Prof. Sonkar S.K, Dr. Ghungrad. S.B, (2011): “Minimum Space and Huge Security in 3D Password Scheme” International Journal of Computer Applications; Vol.29, No.4, September. DOI: https://doi.org/10.5120/3552-4872
P.K. Dhanya, M. Keerthiga, S. Dinakar, (2014): “Secured Authentication using 3D Password by Applying Ultimate Planar Algorithm”, Int. Journal of Scientific & Engineering Research, Vol. 5, Issue 5, May.
Paruthi Ilam Vazhuthi P, Geetha M, Parthiban S, (2015): “A Survey on the Use of 3D Password in Security”, www.ijraset.com Volume 3 Issue II, February, ISSN: 2321-9653 Int. Journal for Research in Applied.
Priya Matta , Bhasker Pant, (2018): “TCpC: a graphical password scheme ensuring authentication for IoT resources”, Bharati Vidyapeeth’s Institute of Computer Applications and Management 2018, Springer. DOI: https://doi.org/10.1007/s41870-018-0142-z
Ronak Talati, Shubham Shah, “Vishwakarma Institute of Information Technology, India,” 4-D Authentication Mechanism”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661.
Sadiq Almuairfi, Prakash Veeraraghavan, Naveen Chilamkurti, (2013): “A novel image-based implicit password authentication system (IPAS) for mobile and non-mobile devices”, Mathematical and Computer Modelling Elesvier, Doi: 10.1016/j.mcm.2012.07.005. DOI: https://doi.org/10.1016/j.mcm.2012.07.005
Sanket Prabhu, Vaibhav Shah, (2015): “Authentication using session based passwords”, Int. con. on advanced computing technologies and applications, Elesvier, Doi:10.1016/j.procs.2015.03.079, Vol.45. DOI: https://doi.org/10.1016/j.procs.2015.03.079
Shums Tabrez, Jagadeesh Sai D, “Pass-Matrix authentication”, Ramaiah Institute of Technology Bangalore, Karnataka, India, 978-1-5386-2745-7/17/ ©2017 IEEE
Shah Zaman Nizamani Syed Raheel Hassan Riaz Ahmed Shaikh, (2019): “TQ-Model: A New Evaluation Model for Knowledge-Based Authentication Schemes”, springer. DOI: https://doi.org/10.1007/s13369-019-04137-6
V.Sindhuja, S.Shiyamaladevi, S.Vinitha, “A Review of 3D Protected Password”, International Journal of Innovative Research in Computer and Communication Engineering, ISSN (Online): 2320-9801
Vaishali Jain1, Akhil Dubey1, Banita Chadha, (2014): “A Survey in Shoulder Surfing Attack in 3D Password”, IEC-College of Engineering & Technology Greater Noida, India, International Journal of Engineering Research & Technology(IJERT) Vol. 3 Issue 2, February.
Zubayr Khalid, Pritam Paul, Soummyo Priyo Chattopadhyay, Anik Naha Biswas, “Secure Authentication with Dynamic Password”, Institute of Engineering and Management, Kolkata, India.
Ahad A., Tahir M., Sheikh M.A., Ahmed K.I., Mughees, A., Numani, A. (2020): Technologies trend towards 5g network for smart health-care using iot: A review. Sensors 2020, 20, 4047. DOI: https://doi.org/10.3390/s20144047
Kolias C., Kambourakis G., Stavrou A., Voas J. (2017): DDoS in the IoT: Mirai and other botnets. Computer 2017, 50, 80–84. DOI: https://doi.org/10.1109/MC.2017.201
Ashraf Q.M., Habaebi M.H. (2015): Autonomic schemes for threat mitigation in Internet of Things. J. Netw. Comput.Appl., 49, 112–127. DOI: https://doi.org/10.1016/j.jnca.2014.11.011
Celesti A., Fazio M., Villari M. (2017): Enabling secure XMPP communications in federated IoT clouds through XEP 0027 and SAML/SASL SSO. Sensors 2017, 17, 301. [CrossRef] [PubMed] DOI: https://doi.org/10.3390/s17020301
Xiao L., Wan X., Han Z. (2018): PHY-Layer Authentication with Multiple Landmarks with Reduced Overhead, IEEE Trans. Wirel. Commun. 17, 1676–1687. DOI: https://doi.org/10.1109/TWC.2017.2784431
Branch J.W., Giannella C., Szymanski B., Wolff R., Kargupta H. (2013): In-network outlier detection in wireless sensor networks. Knowl. Inf. Syst., 34, 23-54. DOI: https://doi.org/10.1007/s10115-011-0474-5
Xiao L., Yan Q., Lou W., Chen G., Hou, Y.T. (2013): Proximity-based security techniques for mobile users in wireless networks. IEEE Trans. Inf. Forensics Secur, 8, 2089–2100. DOI: https://doi.org/10.1109/TIFS.2013.2286269
B Noura Abd El-Moez Semary, Mohiy M. Hadhoud, Alaa M. Abbas; (2011): Space Transformation for HSL Model Encoding; ICICIS 2011.
Wang F., Franco-Penya, HH., Kelleher, J.D., Pugh, J., Ross, R. (2017): An Analysis of the Application of Simplified Silhouette to the Evaluation of k-means Clustering Validity. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. vol 10358. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-62416-7_21
Yen-Lin Lee, Truong XUAN Nguyen, (2010): High frame rate Motion Compensated Frame Interpolation in High-Definition video processing; 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP);
A.B. Gadicha and V.B. Gadicha, “Virtual Realization using 3D Password”, Int. Journal of Electronics and Computer Science Engineering, ISSN 2277-1956/ V1N2-216-222
Cachin C. (1997): Entropy Measures and Unconditional Security in Cryptography. Ph.D. thesis, ETH.
Catuscia Palamidessi, Marco Romanelli. Feature selection with Rényi min-entropy. Artificial Neural Networks in Pattern Recognition - 8th IAPR TC3 Workshop (ANNPR 2018), Siena, Italy. pp.226-239. DOI: https://doi.org/10.1007/978-3-319-99978-4_18
