درخت حوزه‌های تخصصی

Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.

The economic conditions prevailing in the country, sanctions, rapid changes and transformations of the markets, and the complexity and disorder of the market environment, have made it necessary to pay attention to the anti-fragility approach in the industries. One of the most interesting industries is the textile industry. Hence, due to the strategic nature and having a large domestic consumption market, the textile industry must be anti-fragile in order to quickly adapt to new conditions and maintain and improve its competitive value in a safe and stable condition. This research was conducted with a review of the literature in order to achieve the anti-fragility of textile industries throughout the supply chain. We identified the anti-fragility components and weighed them using the best-worst method. Then, we reviewed the literature on the textile industry and interview the experts in this industry, to identify and evaluate the fragility factors. By using the CoCoSo method the most important factors that disrupt anti-fragility were ranked. Finally, the results of the research showed that the components: unexpectedness, Adequate supervisors, and Attraction are the most important components. Also, the results obtained from the CoCoSo method showed that the of ''Lack of investment in the (clothing) industry", "Saturation of the market with counterfeit brands due to the structural weakness of the distribution and smuggling network", and "Undeveloped distribution network" are the most important in the supply chain of the textile industry.