2022 International Wireless Communications and Mobile Computing (IWCMC) 

About: 슬라브어 연구 is an academic conference. The conference publishes majorly in the area(s): Computer science & Engineering..

Transfer Learning-Driven Intrusion Detection for Internet of Vehicles (IoV)

Abstract: The Internet of Vehicles (IoV) is a set of connected vehicles supported with sensors, communication technologies, and software connected by the Internet as an infrastructure. With the evolution of 5G technology, automation, and artificial intelligence, the IoV is expected to replace traditional transportation systems in the near future. On the other hand, with this evolution, the possibility of new cyberattacks has increased. This paper proposes a security framework in which intrusion detection secures the Intra/Inter-Vehicular communications within the IoV network. The proposed framework uses multi-task trans-fer learning to transfer knowledge gained from two different benchmark datasets. To the best of our knowledge, this is the first work that uses transfer learning to transfer the knowledge between two different benchmark datasets. The performance of the intrusion detection engine is evaluated using two different deep learning algorithms, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), in terms of accuracy, precision, recall and F1-score. In addition to achieving satisfying performance and reduced training/fine-tuning time for the target domains, our analysis illustrates the computational effectiveness of the proposed model by transferring the knowledge from the smaller to the larger dataset.

Measuring Decentralization in Emerging Public Blockchains

Abstract: Bitcoin and Ethereum have always been the two major heavyweight infrastructures in the blockchain space. However, Low throughput and high cost hinder their further development. Recently, some emerging public blockchains have become popular. They all have efficient transaction confirmation mechanism and cheap interaction costs. However, the advantages are actually a sacrifice of decentralization. As we all know, decentralization is an essential feature of blockchain. Therefore, it requires the conceiving of up-to-date metrics of decentralization measurement. However, there is little research on the degree of decentralization of these emerging public blockchains in the past. This paper studies nine popular public chains such as Binance Smart Chain, Cardano, and Avalanche. Since these public chains mostly use the consensus mechanism of POS variants, the distribution of governance token balances on the chain can reflect the decentralization of the blockchain. Hence, We evaluate the distribution of the token balance of those public blockchains to indicate their decentralization degree. Two kinds of indicators are adopted and redesigned: information entropy and Gini coefficients. Among the nine public blockchains we selected, Cardano, Tron and Polkadot have a higher degree of decentralization, while Elrond and Binance Smart Chain have a lower degree of decentralization. We think our work will be helpful for future research on the degree of blockchain decentralization.

Towards Multi-view Android Malware Detection Through Image-based Deep Learning

Abstract: Over the last years, several works have proposed highly accurate Android malware detection techniques. Surprisingly, modern malware apps can still pave their way to official markets, thus, demanding the provision of more robust and accurate detection approaches. This paper proposes a new multi-view Android malware detection through image-based deep learning, implemented threefold. First, apps are evaluated according to several feature sets in a multi-view setting, thus, increasing the information provided for the classification task. Second, extracted feature sets are converted to an image format while maintaining the principal components of the data distribution, keeping the information for the classification task. Third, built images are jointly represented in a single shot, each in a predefined image channel, enabling the application of deep learning architectures. Experiments on a new version of a publicly available Android malware dataset composed of over 11 thousand Android apps have shown our proposal's feasibility. It reaches true-negative rates of up to 99.5% when implemented with a single-view approach with our new image-building technique. In addition, if our proposed multi-view scheme is used, the classification accuracies of malware families become more stable, reaching a true-positive rate of up to 98.7%.

Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving

Abstract: Recently, the concept of autonomous driving became prevalent in the domain of intelligent transportation due to the promises of increased safety, traffic efficiency, fuel economy and reduced travel time. Numerous studies have been conducted in this area to help newcomer vehicles plan their trajectory and velocity. However, most of these proposals only consider trajectory planning using conjunction with a limited data set (i.e., metropolis areas, highways, and residential areas) or assume fully connected and automated vehicle environment. Moreover, these approaches are not explainable and lack trust regarding the contributions of the participating vehicles. To tackle these problems, we design an Explainable Artificial Intelligence (XAI) Federated Deep Reinforcement Learning model to improve the effectiveness and trustworthiness of the trajectory decisions for newcomer Autonomous Vehicles (AVs). When a newcomer AV seeks help for trajectory planning, the edge server launches a federated learning process to train the trajectory and velocity prediction model in a distributed collaborative fashion among participating AVs. One essential challenge in this approach is AVs selection, i.e., how to select the appropriate AVs that should participate in the federated learning process. For this purpose, XAI is first used to compute the contribution of each feature contributed by each vehicle to the overall solution. This helps us compute the trust value for each AV in the model. Then, a trust-based deep reinforcement learning model is put forward to make the selection decisions. Experiments using a real-life dataset show that our solution achieves better performance than benchmark solutions (i.e., Deep Q-Network (DQN), and Random Selection (RS)).

Network Slicing Mobility Aware Control to Assist Handover Decisions on e-Health 5G Use Cases

Abstract: In the context of the 5G e-health vertical, Network Slicing (NS) promotes mobile e-health (m-health) applications with high innovative facilities through a set of network resource components that can be extended through physical resource virtualization strategies and softwarization. The Cloud-Network Slicing (CNS) approach was recently introduced to offer services across multiple administrative and technological domains distributed across the federated cloud and network infrastructures. The CNS approach can improve m-health user's experience by allowing high content and service delivery flexibility through Multi-Access Edge Computing (MEC) capabilities within the Radio Access Networks (RAN) closer to the healthcare data source. In this scenario, characterized by the inevitability of handover between the various cells existing in the RAN, the infrastructure management system must be extended with improved capabilities to enable handover decisions to maintain the m-health UE experience during mobility events. This paper introduces a network-slicing mobility-aware control approach for paving 5G CNS-enabled systems with automated and proactive mobility control and management capabilities. Simulation results revealed that our proposal could provide m-health applications with service-level slicing-driven handover procedures while keeping connectivity constraints.