scholarly journals Breaking Data Encryption Standard with a Reduced Number of Rounds Using Metaheuristics Differential Cryptanalysis

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1697
Author(s):  
Kamil Dworak ◽  
Urszula Boryczka
Keyword(s):  
Memetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Memetic Algorithms ◽  
Data Encryption ◽  
Differential Cryptanalysis ◽  
Search Process ◽  
Suggested Approach ◽  
Data Encryption Standard ◽  
Metaheuristic Techniques

This article presents the author’s own metaheuristic cryptanalytic attack based on the use of differential cryptanalysis (DC) methods and memetic algorithms (MA) that improve the local search process through simulated annealing (SA). The suggested attack will be verified on a set of ciphertexts generated with the well-known DES (data encryption standard) reduced to six rounds. The aim of the attack is to guess the last encryption subkey, for each of the two characteristics Ω. Knowing the last subkey, it is possible to recreate the complete encryption key and thus decrypt the cryptogram. The suggested approach makes it possible to automatically reject solutions (keys) that represent the worst fitness function, owing to which we are able to significantly reduce the attack search space. The memetic algorithm (MASA) created in such a way will be compared with other metaheuristic techniques suggested in literature, in particular, with the genetic algorithm (NGA) and the classical differential cryptanalysis attack, in terms of consumption of memory and time needed to guess the key. The article also investigated the entropy of MASA and NGA attacks.

Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

2000 ◽  
Vol 8 (1) ◽  
pp. 61-91 ◽  
Author(s):  
Peter Merz ◽  
Bernd Freisleben
Keyword(s):  
Local Search ◽  
Fitness Landscape ◽  
Memetic Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Dependency Graph ◽  
Gene Interactions ◽  
Local Minima ◽  
Problem Size ◽  
Better Than

The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

2013 ◽  
Vol 300-301 ◽  
pp. 645-648 ◽  
Author(s):  
Yung Chien Lin
Keyword(s):  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Batch Processes ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Genetic Operators ◽  
Search Methods

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

scholarly journals SURVEY: EFFICIENT HYBRID ALGOR ITHMS OF CRYPTOGRAPHY

2020 ◽  
Vol 2 (4) ◽  
pp. 1-16
Author(s):  
Mays M. HOOBI
Keyword(s):  
Data Exchange ◽  
Search Space ◽  
Research Unit ◽  
Data Encryption ◽  
Digital Data ◽  
Secret Data ◽  
Data Encryption Standard ◽  
Hybrid Approaches ◽  
Private And Public ◽  
Secure Transmission

Day after day, the digital data sizes undergo rapid increases over Internet, it is significant; the data shouldn’t be accessed by the unauthorized users. The attackers attempt at accessing those sensitive part of the data. There is a necessity for the prevention of the unauthorized access of the data and guarantee the secure data exchange. A variety of the cryptographic approaches have been used for the conversion of the secret data of the users into secure ciphertext formats. The cryptographic methods have been based on, private and public keys. The researchers have worked on the efficient and secure transmission of data and presented a variety of the cryptographic approaches. For the efficient and secure transmission of the data over networks, there is a necessity of using hybrid approaches of encryption. In this article, various encryption methods are reviewed such as Rijndael, Number Theory Research Unit, Data Encryption Standard, 3 Data Encryption Standard, Elliptic Curve Cryptography, Rivest–Shamir–Adleman, Optimal Asymmetric Encryption Padding, Diffie-Hellman, HiSea, Improved Caesar, Digital Signature, and Advance Encryption Standard. Keywords: Brute Force Attack, Cryptography, Digital Data, Hybrid Encryption, Search Space.

scholarly journals Contrast Enhancement of Images Using Meta-Heuristic Algorithm

2021 ◽  
Vol 38 (5) ◽  
pp. 1345-1351
Author(s):  
Sunkavalli Jaya Prakash ◽  
Manna Sheela Rani Chetty ◽  
Jayalakshmi A
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Search Space ◽  
Primary Objective ◽  
Suggested Approach ◽  
Visual Contrast ◽  
Krill Herd

One of the most important processes in image processing is image enhancement, which aims to enhance image contrast and quality of information. Due to the lack of adequate conventional image enhancement and the challenge of mean shift, intelligence-based image enhancement systems are becoming an essential requirement in image processing. This paper proposes a new approach for enhancing low contrast images utilizing a modified measure and integrating a new Chaotic Crow Search (CCS) and Krill Herd (KH) Optimization-based metaheuristic algorithm. Crow Search Algorithm is a cutting-edge meta-heuristic optimization technique. Chaotic maps are incorporated into the Crow Search Method in this work to improve its global optimization. The new Chaotic Crow Search Algorithm depends on chaotic sequences to replace a random location in the search space and the crow's recognition factor. Based on a new fitness function, Krill Herd optimization is utilized to optimize the tunable parameter. The fitness function requires different primary objective functions that use the image's edge, entropy, grey level co-occurrence matrix (GLCM) contrast, and GLCM energy for increased visual, contrast, and other descriptive information. The results proved that the suggested approach outperforms all-new methods in terms of contrast, edge details, and structural similarity, both subjectively and statistically.

scholarly journals Enhancing Security in Digital Data using various Function of S-box in Data Encryption Standard Method

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 767-789
Author(s):  
Leya Elizabeth Sunny ◽  
Dr. Varghese Paul
Keyword(s):  
Standard Method ◽  
Data Encryption ◽  
Differential Cryptanalysis ◽  
Digital Data ◽  
Linear Cryptanalysis ◽  
Data Encryption Standard ◽  
Plain Text ◽  
Cryptographic Algorithms

Stage of networking is quintessential task in which security comes into play. Securing these networks which contains confidential digital data that needs to secured will be the agenda of cryptography. Many cryptographic algorithms increment their strengths over parameters like key size, increasing the rounds of iteration and finally using confusion box as S-box as it has best robustness. So, this paper mainly focusses over securing digital data with the help of S-box function over Data Encryption Standard (DES) algorithm. For this, a plain text and key will be given to this DES as it extracts 8x8(64) bit characters from the key and converting them into its corresponding ASCII value and are concatenating to form an 8 value by taking mod16. These will give to 8 S-box in order to generate its corresponding output to make even more secure and also shows dynamic DES gives much result than other crypto methods. The evaluation of this integrated s-box and DES shows much fruitful results over factors like non-linearity, Avalanche criterion, Balance, Robustness to linear cryptanalysis, Robustness to differential cryptanalysis.

scholarly journals Efficient Hybrid Cryptography Algorithm

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
Mayes M. Hoobi
Keyword(s):  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Search Space ◽  
Data Encryption ◽  
Key Generation ◽  
Data Encryption Standard ◽  
Standard Algorithm ◽  
Encryption Algorithms ◽  
Brute Force Attack ◽  
Complexity Increase

Cryptography is the operation of transferring a specific message to prevent an attacker from accessing the contents of a message. To increase the level of security in any communication, both parties must have a copy of the encryption key. The Data Encryption Standard algorithm is insufficient due to its weak key generation, so that the key must be reconfigured to make this algorithm more secure, effective, and stronger. The key for encryption enhances securities of the Data Encryption Standard algorithm. This research assumed a combination of two efficient encryption algorithms to achieve the goal of information security by adding a new level of security to the Data Encryption Standard algorithm using the elliptic curve cryptography algorithm. This aim was met by adding two new key functions; the first one is EncK(), and the second one is DecK() for an encryption and decryption key of the Data Encryption Standard algorithm to make this algorithm more secure against attackers. The results obtained from this research also demonstrate good resistance against a brute-force attack, which makes the system more effective by applying the elliptic curve cryptography algorithm to encrypt and decrypt keys using the Data Encryption Standard. In addition, these modifications enhance the degree of complexity, increase key search space, and make the ciphered message more difficult for an attacker to crack.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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