Bio-inspired cryptosystem with DNA cryptography and neural networks

被引:23
|
作者
Basu, Sayantani [1 ]
Karuppiah, Marimuthu [1 ]
Nasipuri, Mita [2 ]
Halder, Anup Kumar [2 ]
Radhakrishnan, Niranchana [1 ]
机构
[1] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India
[2] Jadavpur Univ, Dept Comp Sci & Engn, Kolkata 700032, India
关键词
Cryptosystem; Bio-inspired; Central dogma; Key generation; AUTHENTICATION SCHEME; ROAMING SERVICE;
D O I
10.1016/j.sysarc.2019.02.005
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Bio-Inspired Cryptosystems are a modern form of Cryptography where bio-inspired and machine learning techniques are used for the purpose of securing data. A system has been proposed based on the Central Dogma of Molecular Biology (CDMB) for the Encryption and Decryption Algorithms by simulating the natural processes of Genetic Coding (conversion from binary to DNA bases), Transcription (conversion from DNA to mRNA) and Translation (conversion from mRNA to Protein) as well as the reverse processes to allow for encryption and decryption respectively. All inputs are considered to be in the form of blocks of 16-bits. The final outputs from the blocks can be concatenated to form the final cipher text in the form of protein bases. A Bidirectional Associative Memory Neural Network (BAMNN) has been trained using randomized data for key generation which is capable of saving memory space by remembering and regenerating the sets of keys in a recurrent fashion. The proposed bio-inspired cryptosystem shows competent encryption and decryption times even on large data sizes when compared with existing systems.
引用
收藏
页码:24 / 31
页数:8
相关论文
共 50 条
  • [41] BioGenTool: A Generic Bio-Inspired Neural Tool
    Gan, Kim Soon
    Chang, Sim Vui
    Chin, Kim On
    Anthony, Patricia
    ADVANCED SCIENCE LETTERS, 2018, 24 (02) : 1532 - 1537
  • [42] Neural & Bio-inspired Processing and Robot Control
    Khan, Ameer Hamza
    Li, Shuai
    Zhou, Xuefeng
    Li, Yangming
    Khan, Muhammad Umer
    Luo, Xin
    Wang, Huanqing
    FRONTIERS IN NEUROROBOTICS, 2018, 12
  • [43] Dynamic pursuit with a bio-inspired neural model
    Sánchez, CC
    Girau, B
    ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PROCEEDINGS, 2005, 3708 : 284 - 291
  • [44] Synchronization properties of a bio-inspired neural network
    Ascoli, Alon
    Tetzlaff, Ronald
    Lanza, Valentina
    Corinto, Fernando
    2015 IEEE 15TH INTERNATIONAL CONFERENCE ON NANOTECHNOLOGY (IEEE-NANO), 2015, : 621 - 624
  • [45] Influence of bio-inspired activity regulation through neural thresholds learning in the performance of neural networks
    Lopez-Hazas, Jessica
    Montero, Aaron
    Rodriguez, Francisco B.
    NEUROCOMPUTING, 2021, 462 (462) : 294 - 308
  • [46] A bio-inspired approach for cognitive radio networks
    HE ZhiQiang *
    Science Bulletin, 2012, (Z2) : 3723 - 3730
  • [47] BIONETS: BIO-inspired NExt generaTion networks
    Carreras, I
    Chlamtac, I
    Woesner, H
    Kiraly, C
    AUTONOMIC COMMUNICATION, 2005, 3457 : 245 - 252
  • [48] On the crashworthiness analysis of bio-inspired DNA tubes
    Najibi, Amir
    Zhang, Liwen
    Zheng, Dongli
    SCIENTIFIC REPORTS, 2024, 14 (01)
  • [49] Bio-Inspired Mechanisms in Wireless Sensor Networks
    Khan, S.
    Lloret, Jaime
    Macias-Lopez, Elsa
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [50] Bio-Inspired Management for Enterprise Information Networks
    Habib, Sami J.
    Marimuthu, Paulvanna N.
    Saleem, Sara A.
    2013 INTERNATIONAL CONFERENCE ON COMPUTING, MANAGEMENT AND TELECOMMUNICATIONS (COMMANTEL), 2013, : 410 - 414