Multi-operation data encryption mechanism using dynamic data blocking and randomized substitution

Existing cryptosystems deal with static design features such as fixed sized data blocks, static substitution and apply identical set of known encryption operations in each encryption round. Fixed sized blocks associate several issues such as ineffective permutations, padding issues, deterministic brute force strength and known-length of bits which support the cracker in formulating of modern cryptanalysis. Existing static substitution policies are either not optimally fit for dynamic sized data blocks or contain known S-box transformation and fixed lookup tables. Moreover, static substitution does not directly correlate with secret key due to which it has not been shown safer especially for Advanced Encryption Standard (AES) and Data Encryption Standard (DES). Presently, entire cryptosystems encrypt each data block with identical set of known operations in each iteration, thereby lacked to offer dynamic selection of encryption operation. These discussed, static design features are fully known to the cracker, therefore caused the practical cracking of DES and undesirable security pitfalls against AES as witnessed in earlier studies. Various studies have reported the mathematical cryptanalysis of AES up to full of its 14 rounds. Thus, this situation completely demands the proposal of dynamic design features in symmetric cryptosystems. Firstly, as a substitute to fixed sized data blocks, the Dynamic Data Blocking Mechanism (DDBM) has been proposed to provide the facility of dynamic sized data blocks. Secondly, as an alternative of static substitution approach, a Randomized Substitution Mechanism (RSM) has been proposed which can randomly modify session-keys and plaintext blocks. Finally, Multi-operation Data Encryption Mechanism (MoDEM) has been proposed to tackle the issue of static and identical set of known encryption operations on each data block in each round. With MoDEM, the encryption operation can dynamically be selected against the desired data block from the list of multiple operations bundled with several sub-operations. The methods or operations such as exclusive-OR, 8-bit permutation, random substitution, cyclic-shift and logical operations are used. Results show that DDBM can provide dynamic sized data blocks comparatively to existing approaches. Both RSM and MoDEM fulfill dynamicity and randomness properties as tested and validated under recommended statistical analysis with standard tool. The proposed method not only contains randomness and avalanche properties but it also has passed recommended statistical tests within five encryption rounds (significant than existing). Moreover, mathematical testing shows that common security attacks are not applicable on MoDEM and brute force attack is significantly resistive

Best S-box amongst differently sized S-boxes based on the avalanche effect in ‎the advance encryption standard algorithm

Substitution boxes are essential nonlinear modules that are popular in block ‎cipher algorithms. They ‎also play a significant role in the security area because of ‎their robustness to different linear ‎cryptanalysis. Each element of the state in a S-‎box is nonlinearly replaced using a lookup table. This ‎research presents the S-‎box, one of the fundamental parts of the advanced encryption standard ‎‎(AES) ‎algorithm. The S-box represents the confusion part in the AES. However, when ‎information ‎is shared between different devices in an authorized manner, the ‎algorithm should be able to ‎combine a sufficient number of confusion layers to ‎guarantee the avalanche effect (AE). ‎Subsequently, this research selects the best ‎S-box by comparing different sizes (4×4, 8×8, and ‎‎16×16) and measuring them ‎on the basis of the million-bit encryption. The AE is the main criterion ‎used in ‎choosing the best S-box. A robust and strong cryptography algorithm should be ‎able to ‎confirm the AEs. Results indicate that the 16×16 S-box with a 52% AE ‎ratio is the superior S-bo

Data protection based neural cryptography and deoxyribonucleic acid

The need to a robust and effective methods for secure data transferring makes the more credible. Two disciplines for data encryption presented in this paper: machine learning and deoxyribonucleic acid (DNA) to achieve the above goal and following common goals: prevent unauthorized access and eavesdropper. They used as powerful tool in cryptography. This paper grounded first on a two modified Hebbian neural network (MHNN) as a machine learning tool for message encryption in an unsupervised method. These two modified Hebbian neural nets classified as a: learning neural net (LNN) for generating optimal key ciphering and ciphering neural net CNN) for coding the plaintext using the LNN keys. The second granulation using DNA nucleated to increase data confusion and compression. Exploiting the DNA computing operations to upgrade data transmission security over the open nets. The results approved that the method is effective in protect the transferring data in a secure manner in less tim