Towards a Runtime Standard-Based Testing Framework for Dynamic Distributed Information Systems

International audienceIn this work, we are interested in testing dynamic distributed information systems. That is we consider a decentralized information system which can evolve over time. For this purpose we propose a runtime standard-based test execution platform. The latter is built upon the normalized TTCN-3 specification and implementation testing language. The proposed platform ensures execution of tests cases at runtime. Moreover it considers both structural and behavioral adaptations of the system under test. In addition, it is equipped with a test isolation layer that minimizes the risk of interference between business and testing processes. The platform also generates a minimal subset of test scenarios to execute after each adaptation. Finally, it proposes an optimal strategy to place the TTCN-3 test components among the system execution nodes

A New Model-Based Framework for Testing Security of IOT Systems in Smart Cities Using Attack Trees and Price Timed Automata

International audienceIn this paper we propose a new model-based framework for testing security properties of Internet of Things in Smart Cities. In general a model-based approach consists in extracting test cases from a formal specification either of the system under test or the environment of the considered system in an automatic fashion. Our framework is mainly built on the use of two formalisms namely Attack Trees and Price Timed Automata. An attack tree allows to describe the strategy adopted by the malicious party which intends to violate the security of the considered IOT system. An attack tree is translated into a network of price timed automata. The product of the constructed price timed automata is then computed using the well known UPPAALL platform. The obtained timed automata product serves as input for the adopted test generation algorithm. Moreover our framework takes advantage of the use of the standardized specification and execution testing language TTCN-3. With this respect, the obtained abstract tests are translated into the TTCN-3 format. Finally we propose a cloud-oriented architecture in order to ensure test execution and to collect the generated verdicts

Economic Denial of Sustainability Attacks Mitigation in the Cloud

Cyber security is one of the most attention seeking issues with the increasing advancement of technology specifically when the network availability is threaten by attacks such as Denial of Service attacks (DoS), Distributed DoS attacks (DDoS), and Economic Denial of Sustainability (EDoS). The loss of the availability and accessibility of cloud services have greater impacts than those in the traditional enterprises networks. This paper introduces a new technique to mitigate the impacts of attacks which is called Enhanced DDoS-Mitigation System (Enhanced DDoS-MS) that helps in overcoming the determined security gap. The proposed technique is evaluated experimentally and the result shows that the proposed method adds lower delays as a result of the enhanced security. The paper also suggests some future directions to improve the proposed framework

Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning

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Evaluation of Deep Learning and Conventional Approaches for Image Recaptured Detection in Multimedia Forensics

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges.publishedVersio

A New Correlation Coefficient for T-Spherical Fuzzy Sets and Its Application in Multicriteria Decision-Making and Pattern Recognition

The goal of this paper is to design a new correlation coefficient for -spherical fuzzy sets (TSFSs), which can accurately measure the nature of correlation (i.e., positive and negative) as well as the degree of relationship between TSFS. In order to formulate our proposed idea, we had taken inspiration from the statistical concept of the correlation coefficient. While doing so, we firstly introduce the variance and covariance of two TSFS and then constructed our scheme using these two newly defined notions. The numerical value of our proposed correlation coefficient lies within the interval , as it should be from a statistical point of view, whereas the existing methods cannot measure the negative correlation between TSFS, as their numerical value falls within the interval , which is not reasonable both statistically and intuitively. This aspect has also been thoroughly demonstrated using some numerical examples. The comparison results witnessed the dominance and upper hand of our proposed method over the existing definitions, with reliable and better results. In order to demonstrate the feasibility, usefulness, and practical application, we applied our proposed scheme to solve technical and scientific problems of multicriteria decision-making and pattern recognition. The numerical results show that our proposed scheme is practically suitable, technically applicable, and intuitively reasonable.publishedVersio

An Efficient Convolutional Neural Network with Transfer Learning for Malware Classification

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Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Technology has turned into a significant differentiator in the money and traditional recordkeeping systems for the financial industry. To depict two customers as potential investors, it is mandatory to give the complex innovation that they anticipate and urge to purchase. In any case, it is difficult to keep on top of and be a specialist in each of the new advancements that are accessible. By reappropriating IT administrations, monetary administrations firms can acquire prompt admittance to the most recent ability and direction. Financial systems, along with machine learning (ML) algorithms, are vital for critical concerns like secure financial transactions and automated trading. These are the key to the provision of financial decisions for investors and stakeholders for the firms which are working with the trade credit (TC) approach, in Small and Medium Industries (SMEs). Huge and very sensitive data is processed in a limited time. The trade credit is a reason for more financial gains. The impact of TC with predictive machine learning algorithms is the reason why intelligent and safe revenue generation is the main target of the proposed study. That is, the combination of financial data and technology (FinTech) domains is a potential reason for sales growth and ultimately more profit.publishedVersio

Leveraging convolutional neural network for COVID-19 disease detection using CT scan images

In 2020, the world faced an unprecedented pandemic outbreak of cor-onavirus disease (COVID-19), which causes severe threats to patients suffering from diabetes, kidney problems, and heart problems. A rapid testing mechanism is a primary obstacle to controlling the spread of COVID-19. Current tests focus on the reverse transcription-polymerase chain reaction (RT-PCR). The PCR test takes around 4–6 h to identify COVID-19 patients. Various research has recom-mended AI-based models leveraging machine learning, deep learning, and neural networks to classify COVID-19 and non-COVID patients from chest X-ray and computerized tomography (CT) scan images. However, no model can be claimed as a standard since models use different datasets. Convolutional neural network (CNN)-based deep learning models are widely used for image analysis to diag-nose and classify various diseases. In this research, we develop a CNN-based diagnostic model to detect COVID-19 patients by analyzing the features in CT scan images. This research considered a publicly available CT scan dataset and fed it into the proposed CNN model to classify COVID-19 infected patients. The model achieved 99.76%, 96.10%, and 96% accuracy in training, validation, and test phases, respectively. It achieved scores of 0.986 in area under curve (AUC) and 0.99 in the precision-recall curve (PRC). We compared the model’s performance to that of three state-of-the-art pretrained models (MobileNetV2, InceptionV3, and Xception). The results show that the model can be used as a diagnostic tool for digital healthcare, particularly in COVID-19 chest CT image classification