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.

Organizations face many challenges in the current unpredictable and changing business in which superiority in competition is one of their main goals. In this trend, the field of production has moved towards a paradigm called "Agile Production". If an organization wants to be agile, it must shorten the workflow of the process and provide the conditions for saving time. If a hospital is agile, it can respond when faced with internal and external changes and compete with other hospitals and provide appropriate and fast services to patients. Today, hospitals are currently trying to continuously evaluate their performance due to the increasing advancement of technology. A method in evaluating the performance of the organization is the Balanced Score Card (BSC) approach. In this, indicators measure the desired goals in the organization in four aspects of Internal process, Growth and Learning, Financial and Customer. In this approach, important aspects of sustainable development are ignored , therefore, the new method of Sustainable Balanced Score Card (SBSC) is used to evaluate the performance, which takes into account the environmental index. This study aimed to determine the factors that can be important for evaluating agility in the hospital and then classify them based on the perspectives of SBSC. In the next step, using a questionnaire and statistical hypothesis test, the effectiveness of these factors was confirmed. Then, the importance of each perspective in the field of agility was determined by hierarchical analysis, and this model was presented using the scoring method. Accordingly, the "Economic" index had the highest priority and the "Environmental" index had the lowest priority. Due to the high importance of the Economic index, suggestions were made, the most important of which is the use of long-term loans for the promotion and development of hospitals, effective cooperation, and the use of various insurance services of insurance organizations, considering inventory and tools. , Equipment, essential devices in the warehouse for greater efficiency. Regarding the innovation of this research, we can refer to the evaluation of agility from the perspective of the scorecard by considering the criterion of stability in the hospital.