The Model Based Testing (MBT) is an original approach where test cases are automatically generated from the specifications of the system under tests. These specifications take the form of a behavioral model allowing the test generator to determine, on the one hand, the possible and relevant execution contexts. On the other hand, to predict the effects of these executions on the system. This paper proposes new methodology to generate vulnerability test cases based on SysML model of Europay-Mastercard and Visa (EMV) specifications. Our main aim is to ensure that not only the features described by the EMV specifications are met, but also that there is no vulnerability in the system. To meet these two objectives, we automatically generated concrete tests basing on SysML models. Indeed, this paper highlights the importance of modeling EMV specifications. We opted for the choice of SysML modeling language due to its ability to model Embedded Systems through several types of diagrams. In our work we used state machine diagram to generate vulnerability test cases for a secure and robust system.
Malware threat the security of computers and Internet. Among the diversity of malware, we have “ransomware”. Its main objective is to prevent and block access to user data and computers in exchange for a ransom, once paid, the data will be liberated. Researchers and developers are rushing to find reliable and safe techniques and methods to detect Ransomware to protect the Internet user from such threats. Among the techniques generally used to detect malware are machine learning techniques. In this paper, we will discuss the different types of neural networks, the related work of each type, aiming at the classification of malware in general and ransomware in particular. After this study, we will talk about the adopted methodology for the implementation of our neural network model (multilayer perceptron). We tested this model, firstly, with the binary detection whether it is malware or goodware, and secondly, with the classification of the nine families of Ransomware by taking the vector of our previous work and we will make a comparison of the accuracy rate of the instances that are correctly classified.
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