PWLCM-Based Random Search for Strong Substitution-Box Design

2015 ◽  
pp. 471-478 ◽  
Author(s):  
Musheer Ahmad ◽  
Danish Raza Rizvi ◽  
Zishan Ahmad
Keyword(s):  
Random Search ◽  
Substitution Box

Random Search Based Efficient Chaotic Substitution Box Design for Image Encryption

2018 ◽  
Vol 5 (2) ◽  
pp. 131-147 ◽  
Author(s):  
Musheer Ahmad ◽  
Zishan Ahmad
Keyword(s):  
Secure Communication ◽  
Design Methodology ◽  
Random Search ◽  
Statistical Tests ◽  
Chaotic Map ◽  
Substitution Box ◽  
Differential Uniformity ◽  
Substitution Boxes ◽  
Plain Image ◽  
New Challenges

Cryptographic Substitution-boxes are source of nonlinearity in modern block encryption systems. The robustness and confusion imparted through these systems heavily rely on the strength of their S-boxes. This brings new challenges to design cryptographically potent S-boxes to develop strong encryption systems. In this paper, an effective method to design efficient 8×8 S-box is proposed. The design methodology incorporates piece-wise linear chaotic map based random search. The S-box obtained by the proposed methodology is tested against standard statistical tests like bijective property, strict avalanche criteria, nonlinearity, differential uniformity, bits independent criteria, and linear approximation probability, revealing its outstanding performance. The proposed S-box is compared with some recent chaos-based 8×8 S-boxes. Moreover, the proposed S-box is applied to encrypt plain-image with proposed S-box transformation to unveil and highlight its inherent great encryption strength. The results confirm that the design is consistent and suitable for building strong encryption systems for secure communication.

Table Substitution Box Method for Increasing Security in Interval Splitting Arithmetic Coding

2017 ◽  
Vol 13 (10) ◽  
pp. 6552-6557
Author(s):  
E.Wiselin Kiruba ◽  
Ramar K.
Keyword(s):  
Compression Ratio ◽  
Arithmetic Coding ◽  
Substitution Box ◽  
Plain Text ◽  
T Box ◽  
Novel Approach ◽  
Permutation Methods ◽  
Message Digest ◽  
Box Method ◽  
Arithmetic Coder

Amalgamation of compression and security is indispensable in the field of multimedia applications. A novel approach to enhance security with compression is discussed in this  research paper. In secure arithmetic coder (SAC), security is provided by input and output permutation methods and compression is done by interval splitting arithmetic coding. Permutation in SAC is susceptible to attacks. Encryption issues associated with SAC is dealt in this research method. The aim of this proposed method is to encrypt the data first by Table Substitution Box (T-box) and then to compress by Interval Splitting Arithmetic Coder (ISAC). This method incorporates dynamic T-box in order to provide better security. T-box is a method, constituting elements based on the random output of Pseudo Random Generator (PRNG), which gets the input from Secure Hash Algorithm-256 (SHA-256) message digest. The current scheme is created, based on the key, which is known to the encoder and decoder. Further, T-boxes are created by using the previous message digest as a key.  Existing interval splitting arithmetic coding of SAC is applied for compression of text data. Interval splitting finds a relative position to split the intervals and this in turn brings out compression. The result divulges that permutation replaced by T-box method provides enhanced security than SAC. Data is not revealed when permutation is replaced by T-box method. Security exploration reveals that the data remains secure to cipher text attacks, known plain text attacks and chosen plain text attacks. This approach results in increased security to Interval ISAC. Additionally the compression ratio  is compared by transferring the outcome of T-box  to traditional  arithmetic coding. The comparison proved that there is a minor reduction in compression ratio in ISAC than arithmetic coding. However the security provided by ISAC overcomes the issues of compression ratio in  arithmetic coding. 

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

Parameter optimization by random search using hybrid computer techniques

1966 ◽  
Author(s):  
G. A. Bekey ◽  
M. H. Gran ◽  
A. E. Sabroff ◽  
A. Wong
Keyword(s):  
Parameter Optimization ◽  
Random Search ◽  
Hybrid Computer

scholarly journals Sensitivity-informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2402
Author(s):  
David S. Ching ◽  
Cosmin Safta ◽  
Thomas A. Reichardt
Keyword(s):  
Bayesian Inference ◽  
Transfer Function ◽  
Network Topology ◽  
Random Search ◽  
Network Models ◽  
Training Data ◽  
Unknown Parameters ◽  
Network Parameter ◽  
Dimensional Parameter ◽  
Discrete Random Variables

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

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