Accurate early diagnosis of COVID-19 viral pneumonia, primarily in asymptomatic people, is essential to reduce the spread of the disease, the burden on healthcare capacity, and the overall death rate. It is essential to design affordable and accessible solutions to distinguish pneumonia caused by COVID-19 from other types of pneumonia. In this work, we propose a reliable approach based on deep transfer learning that requires few computations and converges faster. Experimental results demonstrate that our proposed framework for transfer learning is a potential and effective approach to detect and diagnose types of pneumonia from chest X-ray images with a test accuracy of 94.0%.

Social networks play an important role in today’s society and in our relationships with others. They give the Internet user the opportunity to play an active role, e.g., one can relay certain information via a blog, a comment, or even a vote. The Internet user has the possibility to share any content at any time. However, some malicious Internet users take advantage of this freedom to share fake news to manipulate or mislead an audience, to invade the privacy of others, and also to harm certain institutions. Fake news seeks to resemble traditional media to establish its credibility with the public. Its seriousness pushes the public to share them. As a result, fake news can spread quickly. This fake news can cause enormous difficulties for users and institutions. Several authors have proposed systems to detect fake news in social networks using crowd signals through the process of crowdsourcing. Unfortunately, these authors do not use the expertise of the crowd and the expertise of a third party in an associative way to make decisions. Crowds are useful in indicating whether or not a story should be fact-checked. This work proposes a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party side. An experimentation has been conducted on 25 posts and 50 voters. A quantitative comparison with the majority vote model reveals that our aggregation model provides slightly better results due to weights assigned to accredited users. A qualitative investigation against existing aggregation models shows that the proposed approach meets the requirements or properties expected of a crowdsourcing system and a voting system.

Mobile money helps people accumulate, send and receive money using their mobile phones without having a bank account (i.e., in some African countries). Such technology is heavily and efficiently used in many areas where bank services are unavailable and/or in crisis (i.e., during the Covid-19 pandemic) when transportation and services are limited. However, malicious users such as scammers have leveraged social engineering techniques to abuse mobile money services through scams, and frauds, among others. Existing countermeasures, which are specific to mobile money security, mostly ignore the dynamic aspect of interactions between the malicious party and the victim. Considering the above insufficiency, this paper proposes a new approach to characterize mobile money phishing attacks based on reinforcement learning (RL) through Q -learning and Markov decision processes (MDP) and on deep reinforcement learning (DRL) through DRL algorithms, namely, Deep sarsa, Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Deep Q-learning (DQN). In fact, the proposed approach models the optimal sequences of attacker actions to achieve their goals through reinforcement learning and deep reinforcement methods. We experiment on real attack scenarios that have been encountered at Orange and MTN telecoms. Furthermore, we compared reinforcement learning and deep reinforcement learning algorithms to each other and thereby demonstrated the difference between them. This analysis showed a better performance in learning with RL. We also deduced that Q - learning takes less execution time than CDM and therefore its learning quality is better for characterizing mobile money phishing attacks. Finally, we found that some deep reinforcement algorithms, such as Deep Sarsa and A2C , can improve the characterization of scammer-victim interactions during mobile payments.

Mobile devices are vulnerable to malicious apps that jeopardize user privacy and device integrity. This includes single-app malware that operates independently and colluding Android apps that collaborate with each other to carry out a malicious attack. Existing detection methods primarily focus on single-app malware and hence will misclassify colluding Android apps. This paper introduces SigColDroid, a novel approach for detecting colluding Android apps and single-app malware by leveraging dangerous permissions. The research begins by extracting and identifying key features, such as permissions, smali file size, and permission rates, for model training. To facilitate comprehensive evaluation, a balanced dataset of 1455 apps is created, consisting of 485 benign apps, 485 randomly sampled single-app malware from the AndroZoo repository, and 485 colluding applications. Extensive experimentation is conducted using five ensemble classifiers: random forest, Extra Trees, AdaBoost, XGBoost, and LightGBM alongside our proposed custom Artificial Neural Network (ANN) and Deep Neural Network (DNN) architectures. The classifiers are evaluated based on five metrics: Precision, Recall, F1-score, accuracy, and the area under the receiver operation curve (ROC_AUC). The experimental findings highlight the following key insights: (i) Identification of the five most significant permission features for detecting colluding applications; (ii) Positive impact of smali file size and permission rates on classification performance; (iii) Superior performance of Random Forest with a ROC_AUC of 99.48% and LightGBM with 96.91% accuracy, 96.96% precision, 96.90% recall and 96.90% F1-score compared to other classifiers; (iv) Comparative analysis with previous research demonstrates that SigColDroid, despite utilizing fewer features, outperforms state-of-the-art approaches. The proposed approach presents an effective solution for detecting colluding Android apps using permissions and contributes to the advancement of improved detection and prevention mechanisms in mobile security.