JOURNAL ARTICLE

EEDF: Enhanced Encryption Decryption Framework for Device Authentication in IoT.

  • Published In: Adhoc & Sensor Wireless Networks, 2023, v. 56, n. 3/4. P. 163 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: SINGH, ROHIT; AWASTHI, LALIT KUMAR; SHARMA, K. P. 3 of 3

Abstract

IoT technology shows a number of hazards and privacy concerns mainly for security purposes and resistance to attacks. One among the device authentication is password authentication, such as Standard password authentication, Time Password authentication, Biometric authentication, Computer recognition authentication, CAPTCHAs, etc. We are now obtaining a PSK from a previous message or password, in which, the security of the shared message is a major problem and threat. To overcome these challenges, we have proposed an EEDF: Enhanced Encryption Decryption Framework in Device Authentication for IoT. In our proposed model, we have given a framework for encrypting and decrypting a shared message to avoid theft and improve its security. Here we have used a lightweight ECC-based Ed25519 to carry over the entire communication/process. The next is the verification process. It is done by BAN Logic. The text’s source, its recentness, and its reliability are all verified by BAN Logic. Authentication is done at this step. After that, the verified message is fed into a newly proposed novel algorithm namely Compound Xchacha20-ECC Algorithm. This Compound Xchahca20-ECC Algorithm is used for the encryption and decryption process. As a result, our proposed approach reduces the key generation time and resistance to various attacks. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Adhoc & Sensor Wireless Networks. 2023/11, Vol. 56, Issue 3/4, p163
  • Document Type:Article
  • Subject Area:Information Technology
  • Publication Date:2023
  • ISSN:15519899
  • DOI:10.32908/ahswn.v56.9841
  • Accession Number:171980378
  • Copyright Statement:Copyright of Adhoc & Sensor Wireless Networks is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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