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swarm intelligence algorithm

در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه swarm intelligence algorithm در مقالات مجلات علمی
  • F. Parandeh Motlagh*, A. Khatibi Bardsiri
    The internet and its various services have made users to easily communicate with each other. Internet benefits including online business and e-commerce. E-commerce has boosted online sales and online auction types. Despite their many uses and benefits, the internet and their services have various challenges, such as information theft, which challenges the use of these services. Information theft or phishing attacks are internet attacks that are major approach to success it is social engineering that the phisher has used. In these types of attacks, the attacker deceives the users and steals their valuable information by using a fake website that looks like real websites. The damage caused by fake websites and phishing attacks is so high that researchers are trying to identify these types of websites in different ways. So far, various methods have been developed to identify phishing web sites which most of them based on data- mining and learning machine are trying to identify these malicious websites. Artificial neural network is a data-mining method for identifying phishing websites which is used in most studies; however the error rate of this can be significant in detecting these websites, so learning-based optimization algorithm is used as a Swarm intelligence algorithm to reduce its error. In the proposed method, the error rate of multi-layer artificial neural network in detecting phishing websites is considered as a target function which minimized by using learning-based optimization algorithm. In the proposed method, learning- based optimization algorithm selects weights and bias of multi-layer artificial neural network optimally to minimize the error of clssification as an objective function. The datasets used to evaluate the proposed method are Phishing Websites explaind by others. The results of evaluating phishing attack dataset indicate that the rate of error of fake website detection in the proposed method is constantly reduced by repetition. The results of our assessment also indicate that the average accuracy, sensitivity, specificity, precision of the proposed method are 93.42, 92.27, 93.19 and 92.78%, respectively. The decision tree and regression are more accurate in detecting fake websites than artificial neural network.
    Keywords: Fake Websites, Phishing Attacks, Artificial Neural Network, Swarm Intelligence Algorithm, Learning based Optimization Algorithm
  • Sedigheh Navaezadeh, Iman Zangeneh, Mehnoosh Vahebi
    Every year, with regard to rapid development of data, Grid computing that is a kind of distributed computing system is that it has attracted the attention of most people and considerably taken into account. Computing that cannot be done by huge computers can be performed by Grid computing. If load balance is used, efficiency will increase in Grid. Resources have an important and effective role in Grid efficiency. When resources are available and accessible, the access time and efficiency will improve. In this paper, swarm intelligence algorithm has been used and applied in Grid in order to provide load balance. Since Grid environment is a dynamic environment, glowworm algorithm is used to provide load balance in sites. The results of algorithm simulation presented by Matlab software show that, by using this algorithm, efficiency as well as reliability will increase in Grid environment. Also, this algorithm has been compared with two other algorithms in this field.
    Keywords: Swarm Intelligence Algorithm, Glowworm Algorithm, PSO, LA(learning automat), Grid, Load balance
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