Earticle

This paper proposes an FPGA-based network intrusion detection system for the IEC 61850-based industrial network that is specially designed for substation automation. The proposed system uses the Shift-And algorithm for detecting malicious network packets within IEC 61850 messages. To implement a complex rule matching module with a limited memory size of FPGA, a specially designed rule matching module was proposed in this paper. For feasibility evaluation, a prototype with 265 regular expression matching modules was implemented using Xilinx Zynq-7030 FPGA and its performance is presented in this paper.

In this letter, we are the first to focus on the issue of reliable and flexible radio resource allocation (RRA) for beam-hopping (BH) in satellite internet network (SIN). The main new challenges are accurate and dynamic radio resource modeling and high-efficiency RRA in high-dynamic scenarios. Therefore, we propose a novel RRA scheme for BH-SIN based on graph mapping and generative adversarial network (GAN). In our scheme, the characteristics of radio resources are first to be converted to graphical features. Then, the former RRA schemes are modeled as one of the adversarial objects, to adaptively optimize the next best solution to the changeable scenarios and situations. The simulation results show that the proposed RRA schemes improve the throughput and quality of service by 15% and 22%. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

As urban networks integrate heterogeneous radio access technologies (RATs), such as Wi-Fi and 5G/B5G, modeling performance becomes challenging due to interference, spatial variability, and propagation conditions. This paper proposes a graph neural network (GNN)-based framework for predicting throughput, delay, and jitter in multi-RAT environments, considering RAT type. The model encodes network topology and channel characteristics using node and edge features, capturing spatial configuration, congestion, and line-of-sight (LoS) versus non-line-of-sight (NLoS) conditions. The results show that GNNs exhibit robustness across station densities and spatial conditions. The message-passing GNN method performs well for throughput and delay, while non-graph methods better estimate jitter.

To effectively manage the escalating traffic, attention is drawn to the coexistence mechanism of cellular and Wi-Fi networks which offload the cellular traffic from the licensed band to the unlicensed band. For the coexistence of 5G new radio-unlicensed and Wi-Fi, we tackle the energy efficiency maximization problem by sequentially determining the duty cycle and transmit power through the use of distributed deep Q-network (DQN) techniques. When utilizing the existing unlicensed band, the proposed method minimizes the impact on Wi-Fi networks while achieving optimal energy efficiency. Simulations validate the superior energy efficiency of the proposed coexistence mechanism over various benchmark methods.

Recently, as data demand has increased owing to the rapidly increasing demand for wireless devices and the influence of data traffic, various technologies are being developed to support it. Among them, millimeter-wave (mmWave) frequencies with rich spectra and high data-transmission rates suffer from the problem of large path loss. Accordingly, there is a growing interest in unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which can be utilized advantageously to reconstruct wireless communication environments. Therefore, this work considers a large-scale system comprising a number of users and Flying RISs, combining UAVs and RISs to increase algorithm utilization. We propose a deep neural network-based algorithm that places Flying RISs in an appropriate location so that they can support as many users as possible. Simulation results confirmed that the proposed technique could place Flying RISs in an efficient location with higher accuracy and speed in large-scale systems compared to existing techniques.

This study proposes a novel transmit antenna selection (TAS) method for improving communications between the unmanned aerial vehicle (UAV) and ground station (GS). By selecting an appropriate UAV antenna, the signal-to-noise ratio (SNR) at the GS can be significantly enhanced. However, obtaining the necessary channel state information between the UAV and GS is challenging due to the UAV’s movement and resulting channel variations. To overcome this challenge, we propose an innovative approach that leverages a convolutional neural network to predict SNRs and a missing SNR completion method. The numerical evaluation demonstrates that the proposed method can effectively enhance TAS accuracy.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time network traffic data analysis.

Due to the sensitivity of the information required to detect network intrusions efficiently, collecting huge amounts of network transactions is inevitable and the volume and details of network transactions available in recent years has been high. The meta-heuristic anomaly based assessment is vital in an exploratory analysis of intrusion related network transaction data. In order to forecast and deliver predictions about intrusion possibility from the available details of the attributes involved in network transaction. In this regard, a meta-heuristic assessment model called the feature correlation analysis and association impact scale is explored to estimate the degree of intrusion scope threshold from the optimal features of network transaction data available for training. With the motivation gained from the model called “network intrusion detection by feature association impact scale” that was explored in our earlier work, a novel and improved meta-heuristic assessment strategy for intrusion prediction is derived. In this strategy, linear canonical correlation for feature optimization is used and feature association impact scale is explored from the selected optimal features. The experimental result indicating that the feature correlation is has a significant impact towards minimizing the computational and time complexity of measuring the feature association impact scale.

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

Versatile Video Coding (VVC) promised to provide the same video quality as HEVC with 50 % bitrate reduction, which was introduced in 2020. Our suggested method for VVC Intra-coding is residue super-resolution convolutional neural network (RSR-CNN) utilizing downsampling and upsampling procedures. We present an effective complexity reduced VVC intra-coding scheme based on residue SR-CNN. Reducing an original video's resolution in both the vertical and horizontal directions is all that is required to execute down sampling. Increasing the video dimensions for improved visual quality, convolutional neural networks are utilized in the upsampling process to create residue super-resolution. Specifically, for every block, we train a CNN model to perform residue SR after downsampling and compressing the residue at low resolution, and then we carry out motion estimation (ME) and motion compensation (MC) to extract the residue. Using the MC prediction signal, a new residue SR-CNN is designed. Additionally, this work comprehensively examines the complexity and performance of VVC intra-coding tools and integrates them with the residue SR-CNN method. The experiments demonstrate a substantial time savings of 40 % in encoding with BDBR coding gains of 4.2 %, and 2.9 % in AI and RA configurations respectively.

This paper presents a systematic review of deep learning (DL) techniques for Network-based Intrusion Detection Systems (NIDS) based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses: (PRISMA2020) guidelines. It explores recent advancements in data preparation, DL architectures, and performance evaluation metrics for NIDS. The review provides insights into various datasets and tools used in the field, highlighting the effectiveness of DL in improving NIDS performance. Additionally, it discusses the applications of NIDS across different industries and identifies emerging research trends, offering a comprehensive resource for researchers and practitioners in cybersecurity.

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.

Radio maps are essential for electromagnetic spectrum awareness, supporting communication network optimization and spectrum management. However, existing methods often lack sufficient accuracy in complex propagation environments. To address this, we propose RMG-SRGAN, an improved radio map generation network based on an enhanced super-resolution generative adversarial network. The generator incorporates a Multi-scale Attention Aggregation (MAA) module that strengthens feature representation using multi-scale fusion and dual-path attention in spatial and channel dimensions. The discriminator includes a Feature Enhancement (FE) module to boost discriminative power through multi-stage feature processing.In our evaluation, we prioritize physical fidelity and structural reliability over generic perceptual metrics. Consequently, we employ Root Mean Square Error (RMSE) to quantify the precision of predicted signal strength and the F1-Score to assess the classification accuracy of coverage zones versus blind spots. Extensive experiments on the RadioMapSeer dataset demonstrate that RMG-SRGAN achieves state-of-the-art performance, securing the lowest RMSE and highest F1-Score compared to existing baselines.

The ongoing softwarization of networks creates a big need for automated testing solutions to ensure service quality. This becomes even more important if agile environments with short time to market and high demands, in terms of service performance and availability, are considered. In this paper, we introduce a novel testing solution for virtualized, microservice-based network functions and services, which we base on TTCN-3, a well known testing language defined by the European standards institute (ETSI). We use TTCN-3 not only for functional testing but also answer the question whether TTCN-3 can be used for performance profiling tasks as well. Finally, we demonstrate the proposed concepts and solutions in a case study using our open-source prototype to test and profile a chained network service.

In a duck cage, ducks are placed in various states. In particular, if a duck is overturned and falls or dies, it will adversely affect the growing environment. In order to prevent the foregoing, it was necessary to continuously manage the cage for duck growth. This study proposes a method using an object detection algorithm to improve the foregoing. Object detection refers to the work to perform classification and localization of all objects present in the image when an input image is given. To use an object detection algorithm in a duck cage, data to be used for learning should be made and the data should be augmented to secure enough data to learn from. In addition, the time required for object detection and the accuracy of object detection are important. The study collected, processed, and augmented image data for a total of two years in 2021 and 2022 from the duck cage. Based on the objects that must be detected, the data collected as such were divided at a ratio of 9 : 1, and learning and verification were performed. The final results were visually confirmed using images different from the images used for learning. The proposed method is expected to be used for minimizing human resources in the growing process in duck cages and making the duck cages into smart farms.

Virtual network embedding plays a vital role in network virtualization, as it determines the deployment and connection of virtual networks to the physical network in the 5G and beyond. An efficient virtual network embedding algorithm is essential to ensure that virtual networks are embedded in a way that meets the performance, security, and resource requirements of the virtual networks and their users. The integration of reinforcement learning with virtual network embedding can lead to more intelligent and efficient network management, which can enhance the performance of large-scale networked systems. Reinforcement learning has the potential to improve and overcome some limitations of traditional algorithms, such as the need for prior knowledge of network conditions and the difficulty in dealing with non-linear and dynamic network environments. Therefore, we conducted this survey to provide a comprehensive overview and examine potential future directions for the optimal reinforcement learning-based virtual network embedding solutions. However, applying reinforcement learning directly to virtual network embedding is a challenging task that requires further research and study. Additionally, it encourages researchers to examine the potential of reinforcement learning in virtual network embedding, identify the challenges for its application, and cover various factors related to the reinforcement learning application in virtual network embedding, including motivations, performance metrics, and challenges.

This paper proposed a system that will guard infrastructures against incoming threats from drones by detecting it with the use of a radar device-based detection scheme. The database acquired, named Real Doppler RAD-DAR (Radar with Digital Array Receiver) is constructed by a Microwave and Radar Group. The radar used uses a Frequency Modulated Continuous Wave (FMCW) on an 8.75 GHz based frequency band with a BW of 500 MHz. The proposed Convolutional Neural Network (CNN), CNN-32DC is varied with different number of filters, combination layers, and number of feature extraction blocks, the preference that will give the most accurate result was selected and compared with different machine learning and classification learning algorithms gained an accuracy that exceeds other networks with less processing time.

Network Slicing (NS) was proposed as a viable solution in Release 15 of Third Generation Partnership Project (3GPP) to allocate the limited resources among different service types for improving their Quality-of-Service (QoS). However, the advanced vehicular applications such as autonomous driving, platooning, remote driving, etc. have stringent QoS demands and the standard NS architecture is not sustainable for these services. Therefore, we propose a solution compatible with the standard 3GPP NS architecture that implements an Actor-Critic based Deep Reinforcement Learning (DRL) algorithm in the Network Slice Subnet Management Function (NSSMF). The algorithm allocates and manages the limited resources among different slices based on their real-time traffic demands. We generate real-time traffic for each service type and train the algorithm to improve the QoS of each service type in the network. The proposed method is evaluated for the training performance of the proposed algorithm as well as the Service level agreement Satisfaction Ratio (SSR) of each slice. The results exhibit that the proposed method not only improves SSR of each slice, but also performs well in case of increased node density in the network.

In-band network telemetry (INT) is an emerging network monitoring framework based on a protocol-independent packet processor (P4). Network devices can be programmed with a domain-specific language to embed switch-internal states into data packets as they traverse through networks. However, the current P4-based INT does not support a sampling; thus, INT headers should be augmented for all incoming packets, which will incur high overhead in a large-scale network. In this paper, we propose a flexible sampling-based INT (FS-INT) scheme to address the aforementioned issues. Simulation results show that FS-INT can reduce the protocol overhead in a controlled manner while providing sufficiently high accuracy.

Reinforcement learning (RL) has been used in combination with cooperative caching to deal with growing traffic in mobile networks, but the performance of RL based caching policies depends heavily on network settings. This paper investigates the impact of access delays within network infrastructures and popularity and similarity properties of the contents requested on network performance. A deep Q-network based caching framework is established in both basic and extended cooperative edge networks. Our simulation results reveal explicit relationships between the performance and influential parameters, which can provide a guidance and benchmark for the design of effective caching polices with RL and cooperation technologies.