Abstract:

Background: With the development and expansion of technologies and computing technologies, today the frontiers of knowledge are developing rapidly. One of the most important factors in starting these technologies is the formation of a computer network. purpose: Network traffic is very large and this leads to high data size and increased noise and makes it very difficult to extract meaningful information to detect abnormal events. Network training to detect abnormalities helps to identify the time of the attack. Early detection of attacks improves the stability of a system. Methods: The goal of this study is to design a proposed model to identify and detect malware attacks with appropriate accuracy and reduce the rate of malfunctions. To meet these goals, we used participatory machine learning algorithms. Results: According to the results, our proposed model has a better performance in intrusion detection of wireless sensor network attacks than the compared algorithms of decision tree, random tree and Naïve Bayes. Conclusion: Due to recent advances in data mining, the use of participatory model in identifying and detecting attacks of wireless sensor networks is very effective.

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