The integration of federated learning and zero-trust security offers a promising solution for enhancing wireless communication security. This comprehensive exploration examines the distinct ...functionalities of these methodologies and their synergistic potential in fortifying security measures. Given the escalating complexity of cyber threats, there is an urgent need for robust, adaptable security frameworks, a requirement that can be addressed by this innovative combination. By leveraging the decentralized data processing capabilities of federated learning and the comprehensive security controls of zero-trust models, resistance against potential breaches can be significantly bolstered. The work also acknowledges and proposes solutions for inherent challenges in the implementation. The conclusion emphasizes the immense potential of this synergy to revolutionize wireless communication security, providing a robust platform for future research.
In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of ...intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%.
Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the future vision of globalization. While IoT is about sensing data from physical devices, DTs reflect their ...digital representation and enable optimized decision-making by tightly integrating Artificial Intelligence (AI). Although swiftly growing, DTs are raising new challenges in privacy concerns, which are nowadays addressed by FL. However, the limited IoT resources, the communication overhead, and the lack of trust among clients are major obstacles that hinder the effectiveness of learning systems. In this paper, we design a new IoT-based architecture empowered by DT to improve the efficiencies of limited-resources devices. On top of this architecture, we leverage FL to construct the DT models. We further propose CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. Particularly, we study the computing resources of the clients and the quality of their models, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.
IoT has the potential to transform the way we think about information and communication technology. IoT has been studied extensively across many disciplines such as the networking, communication, ...security, business, and management communities. However, many unsolved challenges, especially in managing heterogeneous IoTs, remain to be discussed. Recent studies propose using blockchain, an emerging technology that enables decentralized coordination, to address inherent challenges in IoT. This article presents a preliminary study on an architecture that implements blockchain in managing heterogeneous IoT systems. We start by pointing out the limitations of prior IoT systems and the difficulties of integrating IoT and blockchain. Then we outline an architecture to manage a large-scale heterogeneous IoT system. Our main goal is to stimulate further effort and cross-disciplinary collaboration by providing guidance and reference for future studies.
Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network ...security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.
Wireless sensor and actuator networks are widely adopted in various applications such as critical infrastructure monitoring where sensory data in big volumes and velocity are prone to security ...vulnerabilities for the network and the monitored infrastructure. Despite the vulnerabilities of the big data phenomenon, intelligent data analytics technique can enable the analysis of huge amount of data and identification of intrusive behavior in real time. The main performance targets for any Intrusion Detection System (IDS) involve accuracy, detection, precision, F<sub>1</sub> score and Receiver Operating Characteristics. Pursuant to these, this paper proposes a big data-driven IDS approach in Wireless Sensor Networks by harnessing reinforcement learning techniques on a hybrid IDS framework. We study the performance of RL-IDS and compare it to the previously proposed Adaptive Machine Learning-based IDS (AML-IDS) namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS). The experimental results show that RL-IDS can achieve  100% success in detection, accuracy and precision-recall rates whereas its predecessor ASCH-IDS performs with an accuracy level that is slightly above 99%.
In this article, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the ...data in one location, FL, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each data set over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this article, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology, such as Docker, to build efficient environments using Internet of Things and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. A multiobjective optimization problem representing the client and model deployment is solved using the genetic algorithm (GA) due to its evolutionary strategy. The performed experiments using the mobile data challenge (MDC) data set and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.
Wireless sensor networks have become integral components of the monitoring systems for critical infrastructures such as the power grid or residential microgrids. Therefore, implementation of robust ...Intrusion Detection Systems (IDS) at the sensory data aggregation stage has become of paramount importance. Key performance targets for IDS in these environments involve accuracy, precision, and the receiver operating characteristics which is a function of the sensitivity and the ratio of false alarms. Furthermore, the interplay between machine learning and networked systems has led to promising opportunities, particularly for the system level security of wireless sensor networks. Pursuant to these, in this paper, we propose Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS) for wirelessly connected sensor clusters that monitor critical infrastructures. The proposed ASCH-IDS mechanism is built on a hybrid IDS framework, and transforms the previous work by continuously monitoring the behavior of the receiver operating characteristics, and adaptively directing the incoming packets at a sensor cluster towards either misuse detection or anomaly detection module. We evaluate the proposed mechanism by introducing real attack data sets into simulations, and show that our proposal performs at 98.9% detection rate and approximately 99.80% overall accuracy to detect known and unknown malicious behavior in the sensor network.
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of ...machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server's side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against the VanillaFL selection process as well as other state-of-the-art approach and showcase the superiority of our proposal.
