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Showing 3 results for Genetic Algorithm
Mahdi Ahmadipari, Meysam Moradi,
Volume 4, Issue 1 (9-2015)
Abstract
In recent years, the use of Meta-heuristic algorithms on various problem taken into consideration. Meta-heuristic algorithms in solving various problem, different performance show. An Meta-heuristic algorithm to solve a particular problem may have better performance than other algorithms and poorer performance have in other issue. In this study the performance of Meta-heuristic algorithms for a specific problem that explore cryption key Vigenere encryption algorithm will be examined. And Meta-heuristic different algorithms performance in terms of accuracy and speed of convergence of the results will be cryptanalyzed and the best algorithm is selected
Meysam Moradi, Mahdi Abbasi,
Volume 7, Issue 2 (3-2019)
Abstract
For many years, cryptanalysis has been considered as an attractive topic in jeopardizing the security and resistance of an encryption algorithm. The SDES encryption algorithm is a symmetric cryptography algorithm that performs a cryptographic operation using a crypt key. In the world of encryption, there are many search algorithms to cryptanalysis. In these researches, brute force attack algorithm has been used as a complete search algorithm, genetic algorithm as an evolutionary intelligence algorithm, and standard particle swarm as an optimization a swarm intelligence as algorithm. Along with these algorithms, a genetic algorithm has been also introduced by adjusting and designing the parameters and design algorithms has been introduced to discover of crypt key. There are attempts to evaluate the performance of different algorithms for cryptanalysis of the SDES encryption algorithm.
Dr Majid Fani, Dr Mohammadamin Torabi, Dr Matineh Moghaddam,
Volume 9, Issue 2 (2-2021)
Abstract
Not all phishing attacks are always done in the form of website forgery and telephone phishing. Emails and messages that appear to be sent by the bank and receive information from the user can also be a phishing attack. Feature selection and sample selection are two very important issues in the data processing stage in detecting malicious emails. In particular, identifying spam without data reduction will not be nearly as accurate in the results. Most articles and research have focused on one of these issues, and there are few articles that have worked in combination to detect malicious emails. Therefore, the purpose of the present study is to provide a method to reduce the data in identifying emails by selecting features and samples simultaneously. In the proposed method in this paper, the forbidden search algorithm and the genetic algorithm are used in combination and simultaneously. For the suitability of this method, the evaluation vector machine evaluation function was used. The results showed that the detection rate of spam and e-mails in LineSpam and UCI datasets was 97.28, which was the highest possible value compared to other algorithms proposed in previous studies.
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