Kombinasi Sinkronisasi Jaringan Syaraf Tiruan dan Vigenere Cipher untuk Optimasi Keamanan Informasi

Abdul  Fadlil; Imam  Riadi; Achmad  Nugrahantoro

doi:10.31849/digitalzone.v11i1.3945

Abdul Fadlil Universitas Ahmad Dahlan
Imam Riadi Universitas Ahmad Dahlan
Achmad Nugrahantoro Universitas Ahmad Dahlan

DOI: https://doi.org/10.31849/digitalzone.v11i1.3945

Keywords: Kriptografi, Jaringan Syaraf Tiruan (JST), Tree Parity Machine (TPM), Vigenere Cipher

Abstract

Kriptografi pengubahan pesan asli menjadi disamarkan berguna menjaga kerahasiaan, integritas, keaslian, autentikasi pesan ketika proses komunikasi. Kriptografi klasik dengan subtitusi polialfabetik Vigenere memiliki tabel alphabet 26 baris yang relatif sederhana menjamin kerahasiaan. Kini pendekatan pembelajaran mesin Jaringan Syaraf Tiruan (JST) menjadi solusi layak untuk kriptografi dengan membentuk kunci rahasia dalam bobot jaringan sulit terpecahkan. Kunci dihasilkan dari bidirectional learning, dua pohon paritas saling tersinkronisasi dengan paramater hidden neuron, input neuron dan bobot. Sinkronisasi pada saluran publik dengan mengadopsi cara kerja Tree Parity Machine (TPM) dengan tipe feed forward. Pendekatan Kriptografi JST bermanfaat sebagai perlindungan dan serangan kriptografi. Penelitian ini memanfaatkan kombinasi sinkronisasi JST dan Vigenere dalam bentuk generator untuk optimasi pesan. Hasil pengujian kombinasi metode tidak berpengaruh dengan jumlah tampungan karakter pesan dan nilai parameter. Keunggulan kunci yang dihasilkan tidak bisa digunakan secara berulang meski nilai parameter sama, namun panjang karakter kunci berjumlah sama. Sisi fungsionalitas menghasilkan nilai 100%.

Kata kunci: Kriptografi, Jaringan Syaraf Tiruan (JST), Tree Parity Machine (TPM), Vigenere Cipher

Abstract

Cryptography changes the original message to be disguised useful to maintain the security message. Vigenere polyalphabetic substitution relatively simple 26-row alphabetical table guaranteeing confidentiality. Machine learning approach Artificial Neural Network (ANN) becomes feasible solution for cryptography by forming secret key in the weight of the network that’s difficult to solve. The key’s generated from bidirectional learning, two parity trees synchronized with hidden neurons, input neurons, and weights. Synchronize public channels by adopting the work of Tree Parity Machine (TPM) with feedforward type. This research utilizes the combination of synchronization ANN and Vigenere from generators. The result of testing the combination of methods doesn’t affect the number of message character and parameter values. The advantages of the resulting key cannot be used repeatedly even though the parameter values are the same, but the key length is the same number of characters. The functionality produces 100% value.

Keywords: Cryptography, Artificial Neural Networks (ANN), Tree Parity Machine (TPM), Vigenere Cipher

Downloads

Download data is not yet available.

References

D. Ariyus, “Pengantar Ilmu Kriptografi: Teori Analisis & Implementasi,” l. Yogyakarta: Penerbit Andi, 2008.

S. D. Putra, M. Yudhiprawira, S. Sutikno, Y. Kurniawan, and A. S. Ahmad, “Power Analysis Attack Against Encryption Devices: a Comprehensive Analysis of AES, DES, and BC3,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 3, p. 1282, 2019.

P. S. Nugroho and E. Aribowo, “Pengembangan Modul Enkripsi dan Dekripsi pada Php dengan Modifikasi Metode Kriptografi Vigenere Cipher dan Cipher Block Chaining (Studi Kasus pada Geekybyte. com),” J. Sarj. Tek. Inform., vol. 2, no. 1, pp. 333–341, 2014.

R. Munir, “Pengantar Kriptografi,” ITB, Bandung, 2006.

A. G. Konheim, “Computer Security and Cryptography,” John Wiley & Sons, 2007.

V. Wati, H. Sa’diyah, and D. Ariyus, “Pendekatan Stego-Kripto Mode Cipher Block Chaining Untuk Pengamanan Informasi Pada Citra Digital,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 2, pp. 197–204, 2020.

Y. Arta, “Implementasi Intrusion Detection System Pada Rule Based System Menggunakan Sniffer Mode Pada Jaringan Lokal,” IT J. Res. Dev., vol. 2, no. 1, p. 43, Nov. 2017.

P. Irfan, Y. Prayudi, and I. Riadi, “Image Encryption using Combination of Chaotic System and Rivers Shamir Adleman (RSA),” Int. J. Comput. Appl., vol. 123, no. 6, pp. 11–16, 2015.

N. Laila and A. S. R. Sinaga, “Implementasi Steganografi LSB Dengan Enkripsi Vigenere Cipher Pada Citra,” Sci. Comput. Sci. Informatics J., vol. 1, no. 2, p. 47, 2019.

A. Sofwan, A. B. P, and T. Susanto, “Aplikasi Kriptografi dengan Algoritma Message Digest 5 (Md5),” Transmisi, vol. 8, no. 1, pp. 22–27, 2006.

M. D. Irawan, “Implementasi Kriptografi Vigenere Cipher dengan Php,” J. Teknol. Inf., vol. 1, no. 1, p. 11, 2017.

P. P. Hadke and S. G. Kale, “Use of Neural Networks in Cryptography: A Review,” IEEE WCTFTR 2016 - Proc. 2016 World Conf. Futur. Trends Res. Innov. Soc. Welf., pp. 1–4, 2016.

M. Abadi and D. G. Andersen, “Learning to Protect Communications with Adversarial Neural Cryptography,” arXiv Prepr. arXiv1610.06918, pp. 1–15, 2016.

P. P. Hadke and P. M. R. Dubey, “Neural Cryptography for Secret Key Exchange,” Int. J. Mod. Trends Sci. Technol., vol. 03, no. March, pp. 15–18, 2017.

S. Pattanayak and S. A. Ludwig, “Encryption Based on Neural Cryptography,” Adv. Intell. Syst. Comput., vol. 734, pp. 321–330, 2018.

S. Wallner, “Designing low-cost cryptographic hardware for wired- or wireless point-to-point connections,” Commun. Comput. Inf. Sci., vol. 36, pp. 1–10, 2009.

A. Singh and N. Aarti, “Neural Cryptography for Secret Key Exchange and Encryption with AES,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, no. 5, pp. 376–381, 2013.

S. Man and S. Shrestha, “C ++ Implementation of Neural Cryptography for Public Key Exchange and Secure Message Encryption with Rijndael Cipher,” Academia.Edu, pp. 1–8, 2013.

A. Amrulloh and E. I. H. Ujianto, “Kriptografi Simetris Menggunakan Algoritma Vigenere Cipher,” J. CoreIT, vol. 5, no. 2, pp. 71–77, 2019.

H. Sadiyah, V. Wati, and D. Ariyus, “Telematika Implementasi Keamanan Pesan pada Citra Steganografi Menggunakan Modifikasi Cipher Block Chaining ( CBC ) Vigenere,” Telematika, vol. 13, no. 1, pp. 44–55, 2020.

M. A. Maricar and N. P. Sastra, “Efektivitas Pesan Teks Dengan Cipher Substitusi, Vigenere Cipher, dan Cipher Transposisi,” Maj. Ilm. Teknol. Elektro, vol. 17, no. 1, p. 59, 2018.

B. C. Hrishikesh, D. Queenie, A. Krati, and K. Lavanya, “Vectorized Neural Key Exchange using Tree Parity Machine,” An Int. J. Adv. Comput. Technol., vol. 8, no. 5, 2019.

I. Riadi, A. Wirawan, and S. -, “Network Packet Classification using Neural Network based on Training Function and Hidden Layer Neuron Number Variation,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 6, 2017.

A. Ruttor, “Neural Synchronization and Cryptography,” 2007.

E. H. A. Mendrofa, E. Y. Purba, B. Y. Siahaan, and R. W. Sembiring, “Collaborative Encryption Algorithm Between Vigenere Cipher, Rotation of Matrix (ROM), and One Time Pad (OTP) Algoritma,” Adv. Sci. Technol. Eng. Syst. J., vol. 2, no. 5, pp. 13–21, 2017.

Wikibooks, “Visual Basic for Applications,” 2020. .