Journal of Network and Systems Management最新文献

The rapid growth of Artificial Intelligence (AI) algorithms has created the opportunity to solve complex problems such as Internet of Things (IoT) botnet attacks. The severity of IoT botnet attacks is a critical challenge for improving the smart IoT environment. Therefore, there is an urgent need to design and implement an efficient detection model to deal with various IoT bot attacks and simultaneously handle issues related to the massive feature space. This paper introduces a wrapper feature selection technique by adapting the Artificial Rabbit Optimization (ARO) algorithm and the Decision Tree (DT) algorithm to detect various types of IoT botnet attacks. During the design of the suggested DT-ARO model, the N-BaIoT datasets were used as a testbed environment. The feature space optimization step was carried out using the ARO algorithm to select only the high-priority features for detecting the IoT botnet attacks. The binary vector technique was used to distinguish the optimal features. The detection engine was performed using the DT algorithm. The conducted experiments have demonstrated the ability of the suggested DT-ARO model to detect various types of IoT botnet attacks, where the accuracy rate was 99.89%. Meanwhile, effectively reducing the feature’s space. In addition, the accomplished results were compared with the latest typical approaches. The DT-ARO model was found to be competitive with these methods and even outperformed them in reducing the feature space.

To keep up with the increasing number of connected devices in people’s daily lives, it is necessary to develop intelligent mechanisms that perform the entire network management, interconnecting Wi-Fi, and the emerging beyond Fifth-Generation (5G) communications. Hence, it is essential to consider multiple usage scenarios, while managing end devices’ limitations. As a result, developing a system that allows network operators to link Wi-Fi services on their main networks becomes a critical issue. These include a new paradigm that tackles optimal and dynamic resource allocation techniques. Thus, to consider in a combined way, the applications requirements, the resources available, and the different tiers involved, mechanisms such as virtualization and slicing have emerged to handle the heterogeneous context of the next-generation wireless communications. Moreover, the allocation of Radio Access Network (RAN) resources needs to be addressed. For this purpose, Open-RAN has in mind an open environment, which relies on virtualized functions and is mostly vendor agnostic. This technology will enable high data rates while maintaining adequate Quality of Service (QoS) in wireless communications. This paper advances current literature, which mainly discusses these themes individually, by providing a comprehensive survey in Next Generation Wireless Communications, highlighting their integration with beyond 5G Communications. First, we introduce the Wi-Fi evolution and explain the main standards developed over the years. Second, we present the most recent Wi-Fi standards, Wi-Fi 6 and 7, compared with 5G and beyond. Lastly, we explain the concepts related to slicing, virtualization, RAN, Open-RAN and the open research challenges.

In recent years, not only CPUs with few cores but also heterogeneous hardware such as GPUs, FPGAs, and multi-core CPUs are increasingly used in many applications. However, to fully utilize these, users need to have technical knowledge that covers hardware such as CUDA. To overcome this high technical barrier, we have proposed environment-adaptive software that enables high-performance operation by automatically converting application code written for normal CPUs by engineers in accordance with the deployed environment and by setting appropriate amounts of resources. So far, we have also verified the elemental technologies that automatically offload to GPU and FPGA before the start of operation. Until now, we only considered conversions and settings before the start of operation. In this paper, we verify that the logic is reconfigured in accordance with the usage characteristics during operation. Especially for GPU logic, there is no example of reconfiguration during operation, so the proposed method can be expected to have a great impact on clouds or similar businesses. We propose a GPU reconfiguration method during operation and find that the application running on the GPU is reconfigured to other offload loops or other offload applications in accordance with the current usage trends. Through a reconfiguration experiment, performance improvement and break time are measured, and the effectiveness of the method is demonstrated.

Various applications of intelligent transport systems require road traffic data that can be collected from vehicles and sent over a vehicular ad hoc network (VANET). Due to rapid mobility and limited channel capacity in a VANET, where vehicles must compete to access the roadside units (RSUs) to report their data, clustering is used to create a group of vehicles to collect, aggregate, and transfer data to RSUs acting as sink nodes. Unlike prior works that mostly focus on cluster head selection for prolonging cluster lifetime or maximizing throughput, we applied the coalitional game model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize the number of RSUs. The coalitional game models the profit and cost of nodes as the utility, which is a weighted function of the coverage area, amount of cluster’s members, relative velocities, distances among nodes, and transmission delay toward the sink nodes. Due to the problem complexity, the genetic algorithm is developed to obtain the model solution. The simulation results reveal that the solution quickly converges within a few generations, where the most suitable structure attains the maximum summation utility from all nodes in the coalition. Additionally, the GA-based solution approach outperforms the brute-force approach in terms of the problem scale, and the coalitional game model yields higher coverage areas compared to those obtained from the non-cooperation model.

In Vehicular Ad-hoc NETworks (VANETs), it is important to consider the quality of the path used to forward data packets. Because of the fluctuating conditions of VANETs, stringent requirements have been imposed on routing protocols and thus complicating the entire process of packet delivery. To determine which path is the best, a routing protocol relies on a path assessment mechanism. In this paper, the problem of link quality estimation in VANET networks is addressed. Based on the information gathered from the packet decoding errors at the physical layer, a novel link quality estimator is proposed. The proposed link quality estimator named LSENN for Link State estimation based on Neural Networks, has been tested under realistic physical layer and mobility models for reactivity, accuracy and stability evaluation.

Phishing evolves rapidly nowadays, causing much damage to finance, brand reputation, and privacy. Various phishing detection methods have been proposed along with the rise of phishing, but there are still research issues. Phishing websites mainly steal users’ information through visual deception and deep learning methods have been proved very effective in computer vision applications but there is a lack in the research on visual analysis using deep learning algorithms. Moreover, most research use balanced datasets, which is not the case in a real Web environment. Therefore, this paper proposes a security indicator area (SIA) which contains most security indicators that are designed to help users identify phishing sites. The proposed method then takes screenshots of SIA and uses a convolutional neural network (CNN) as a classifier. To prove the efficiency of the proposed method, this paper carries out several comparative experiments on an unbalanced dataset with much fewer phishing sites, which increases detection difficulty but also makes the detection closer to reality. The results show that the proposed method achieves the highest F1-score among the compared methods, while providing advantages on detection efficiency and data expansibility in phishing detection.