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In urban environments, efficiently decrypting CP-ABE in VANETs is a significant challenge due to the dynamic and resource-constrained nature of these networks. VANETs are critical for ITS that improve traffic management, safety, and infotainment through V2V and V2I communication. However, managing computational resources for CP-ABE decryption remains difficult. To address this, we propose a hybrid RL-DE algorithm. The RL agent dynamically adjusts the DE parameters using real-time vehicular data, employing Q-learning and policy gradient methods to learn optimal policies. This integration improves task distribution and decryption efficiency. The DE algorithm, enhanced with RL-adjusted parameters, performs mutation, crossover, and fitness evaluation, ensuring continuous adaptation and optimization. Experiments in a simulated urban VANET environment show that our algorithm significantly reduces decryption time, improves resource utilization, and enhances overall efficiency compared to traditional methods, providing a robust solution for dynamic urban settings.

As the cloud moves from monolithic infrastructure to a self-isolated cloud native microservice environment, automation is becoming an important aspect for the management of the application life cycle. In this context, there are many tools available that can monitor these applications and raise alarms. However, automated orchestration is still in its early stages, and the available solutions are not capable of monitoring the whole system from application to hardware levels and performing automated operations within the system. Moreover, IP Multimedia Subsystem (IMS), which is the core part of the Telecom industry, has switched to a microservice environment. These IMS services are critical and need to be proactively monitored to provide automated orchestration operations. In this paper, we address the aforementioned problem by proposing a new scheme for monitoring the metrics from different sources and proactively and automatically performing orchestration using machine learning while improving the scalability of the cloud native Virtual IMS. Experiments carried out with a real cloud-native IMS running in a kubernetes cluster explore the relevance, efficiency and scalability of the proposed scheme.

Predicting context-aware activities using machine-learning techniques is evolving to become more readily available as a major driver of the growth of IoT applications to match the needs of the future smart autonomous environments. However, with today’s increasing security risks in the emerging cloud technologies, which share massive data capabilities and impose regulation requirements on privacy, as well as the emergence of new multiuser, multiprofile, and multidevice technologies, there is a growing need for new approaches to address the new challenges of autonomous context awareness and its fine-grained security-enforcement models. The solutions proposed in this work aim to extend our previous LCA-ABE work to provide an intelligent, dynamic creation of context-aware policies, which has been achieved through deploying smart-learning techniques. It also provides data consent, automated access control, and secure end-to-end communications by leveraging attribute-based encryption (ABE). Moreover, our policy-driven orchestration model is able to achieve an efficient, real-time enforcement of authentication and authorization (AA) as well as federation services between users, service providers, and connected devices by aggregating, modelling, and reasoning context information and then updating consent accordingly in autonomous ways. Furthermore, our framework ensures that the accuracy of our algorithms is above 90% and their precision is around 85%, which is considerably high compared to the other reviewed approaches. Finally, the solution fulfills the newly imposed privacy regulations and leverages the full power of IoT smart environments.

With the increasing usage of smartphones in banks, medical services and m-commerce, and the uploading of applications from unofficial sources, security has become a major concern for smartphone users. Malicious apps can steal passwords, leak details, and generally cause havoc with users’ accounts. Current anti-virus programs rely on static signatures that need to be changed periodically and cannot identify zero-day malware. The Android permission system is the central security mechanism that regulates the execution of application tasks. Although recent advances in research have provided various approaches and detection methods for finding malware apps, the available literature lacks a full analysis of this subject. We fill this gap by: 1) Systematically and automatically building a large dataset of malware and benign apps, which we have made available to the community. Our dataset has around 16K apps and 118 features. 2) We offer a novel approach for automatically identifying permission usage patterns, which are groupings of permissions that developers frequently utilise together. The approach combines SOM and K-means clustering algorithms to classify permissions according to app usage categories. The results demonstrate that the proposed methodology is able to detect most of the consistent and coherent permission usage patterns across a wide variety of application categories. To assess our strategy, we add the identified patterns as features to our dataset and then apply an SVM classifier for malware detection. Our results indicate that the identified patterns improve the performance of the classifier.

In this paper, we present a methodology for quantifying the decentralization degree of a blockchain network. To accomplish this, we use two well-known graph models of Erdös-Rény and Barabási–Albert in order to study the blockchain network topology. We then quantify the decentralization degree using the clustering coefficient of our network models. We validate our approach through extensive simulations and analyze the decentralization degree with respect to network parameters such as the number of connections per node and the peer selection algorithm. Our results expose the trade-off between the average shortest path and the decentralization degree. Furthermore, we observe the impact of the average shortest path on the network speed and traffic overhead. Finally, we demonstrate that the presence of hub-like nodes such as relay gateways negatively impacts the decentralization degree of blockchain networks.

As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system’s security vulnerabilities. Although several approaches have been proposed in the research literature for the detection of Android malwares, many of them suffer from issues such as small training datasets, there are few features (most studies are limited to permissions) that ultimately affect their performance. In order to address these issues, we propose an approach combining advanced machine learning techniques and Android vulnerabilities taken from the AndroVul dataset, which contains a novel combination of features for three different vulnerability levels, including dangerous permissions, code smells, and AndroBugs vulnerabilities. Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware. Our results show that both models are capable of detecting malware encoded in Android APK files with about 99% accuracy, which is better than the current state-of-the-art approaches.