جستجوی مقالات مرتبط با کلیدواژه « ح لات ناشناتته » در نشریات گروه « فنی و مهندسی »

Security is a significant issue in this world and is given several dimensions by varying circumstances. Among different security areas, cyber security can be claimed to have one of the most important places in new circumstances of this world. In this study, two virtual honeynets were designed in two different laboratories to help us study unknown attacks. Other scientific datasets were also used for this purpose. Imbalanced data always cause problems for network datasets and reduce the efficiency for the prediction of minority classes. To cope with this problem, ensemble learning methods were applied in order to detect network attacks and most specifically, unknown attacks, while taking advantage of different techniques and action model learning. It was found that ensemble learning method was suitable for describing the security problems because activities done on computer systems can be viewed at multiple levels of abstraction and information can be collected from multiple data sources. Statistical analysis was used as the research method in order to measure the reliability and validity of findings. Here, we applied statistical techniques and tests to show that the algorithm designed by the proposed weighted voting and based on the genetic algorithm has a better performance than other twelve classifiers.