Transactions on Emerging Telecommunications Technologies最新文献

Aiming at the problem that ADS-B (Automatic Dependent Surveillance Broadcast) data jump characteristics and multi-source heterogeneous errors are difficult to dynamically associate, this paper constructs a data mining framework that combines time series anomaly detection and multi-modal feature fusion. First, this paper performs missing value interpolation, time alignment and standardization preprocessing on the collected ADS-B data, energy status parameters, material environment parameters and flight environment information; secondly, based on the LSTM-AE (Long Short-Term Memory-Autoencoder) model, an unsupervised model for ADS-B data jump detection is constructed, which identifies the jump point by reconstructing the error and extracts jump features with physical significance. Finally, this paper introduces the Transformer architecture, cross-modally fuses the jump features with multi-source heterogeneous parameters, constructs a semantic alignment mechanism, and combines the error evolution modeling strategy to explore the complex influence mechanism of different factors on positioning errors. Experimental results show that the proposed method has better jump recognition accuracy (average 90.7%) and error prediction mean square error (average 5.81) than Isolation Forest and Variational Autoencoder (VAE), providing a new technical path for improving the positioning reliability and safety control of unmanned aerial vehicles (UAVs).

The traditional cybersecurity solution cannot offer the necessary protection against all forms of cyberattacks for management systems since cyberattacks are of a scattered nature. Thus, an efficient security method is suggested in this work to prevent data loss from the information system using optimization and deep learning. The major aim of this work is to offer suitable recommendations for developing a cybersecurity decision support model for information systems with risk analysis and cybersecurity models. In the information system, the security threats are eliminated using this model. So, the authentication, prediction of the risk levels, and the mitigation of the security threats are regarded as the major objectives of this research work. From the CICIDS 2017 KNN dataset, UNSW-NB15 dataset, and NSL-KDD dataset, the data is collected. The collected data is given to the executed Deep Security Network (DSecNet). In the DSecNet, the Restricted Boltzmann Machine (RBM) is utilized to retrieve the features and then the extracted features from the RBM are fused with optimized weights, which are selected using the Enhanced Sandpiper Optimization Algorithm (ESOA). The obtained weighted features are further provided to the Deep Capsule Neural Network (DeepCapsNet). The implemented DSecNet is used for verifying the authentication of the user, detecting the intrusion, and determining the associated risk levels caused by the attacks on the information system. Once the threats are detected, the mitigation of the threat based on their risk level is carried out. The effectiveness of the proposed model is proven by the validation process. From the simulation outcomes, the accuracy rate of the developed model is 97.11% in the NSL-KDD dataset and 96.70% in the UNSW-NB15 dataset based on intrusion detection performance. Therefore, the efficacy of the developed security system is higher for the performance of the authentication, intrusion detection, and risk prediction, which elevates the security of the cyber networks.

One of the most prevalent attacks that cause significant harm and impair cloud performance is Distributed Denial of Service (DDoS). DDoS attacks pose a significant threat to cloud environments, degrading performance and disrupting services. To address this issue, we propose a hybrid bio-inspired deep learning model for DDoS attack detection that leverages big data analytics in the cloud. The proposed model incorporates a MapReduce framework to efficiently process large-scale network traffic data, extracting crucial features such as raw features, packet-based features, improved correlations, and statistical features. These extracted features are further refined using an improved recursive feature elimination (RFE) method, which selects the most relevant attributes for attack detection. The attack detection phase employs a hybrid classifier (HC) that integrates Long Short-Term Memory (LSTM) and Deep MaxOut (DMO) models. To ensure optimal performance, the weights of LSTM and DMO are fine-tuned using the White Shark Updated Remora Optimization (WSU-ROA), enhancing classification accuracy. The proposed HC + WSU-ROA model outperforms other methods, achieving the highest accuracy of 93.98%, compared to the other existing methods, demonstrating its superior effectiveness in DDoS attack detection.

Channel State Information (CSI) is necessary for wireless systems supported by reconfigurable intelligent surfaces to regulate wireless channels and increase bandwidth along energy efficiency. In this paper, RIS-Aided mmWave MIMO Channel Estimation with Rotation-Invariant Coordinate Convolutional Neural Network and Compressive Sensing (RIS-MIMO-CE-RICCNN) is proposed. Reconfigurable Intelligent Surface (RIS) is utilized to estimate frequency-flat and frequency-selective cascaded channels with minimal overhead in multi-user millimeter wave big multiple inputs multiple outputs (MIMO) systems. The unique angle cascaded channels visible to different users have totally shared non-zero rows including user-specific column supports; it makes use of both the double-structured sparsity property of angular cascaded channel (ACC) matrices and the common sparsity property among the various subcarriers. Rotation-Invariant Coordinate Convolutional Neural Networks (RICCNN) is used to accurately detect channel supports. Then, channel estimation is done by Harbor Seal Whiskers Optimization Algorithm (HSWOA). The performance metrics like Signal to Noise Ratio (SNR), Normalized mean square error (NMSE), Bit error rate (BER), Peak-signal to noise ratio (PSNR), Computational complexity, Loss function, BER versus SNR and energy consumption are evaluated. The performance of the RIS-MIMO-CE-RICCNN technique is evaluated against existing methods. The RIS-MIMO-CE-RICCNN achieves 16.28%, 30.78% and 25.29% lower SNR, 15.08%, 20.58%, and 15.25% lower NMSE, 28.96%, 30.21%, and 23.89% lower BER, and 26.28%, 31.26%, 19.66% lower PSNR when compared to the existing models: RIS-assisted mmWave MIMO channel estimation utilizing deep learning with compressive sensing (RIS-MIMO-CE-CS), channel estimation for reconfigurable intelligent surface enabled multiple user mmWave MIMO systems (RIS-MIMO-CE-MU), Beam Pattern along Reflection Pattern Design of Channel Estimation in RIS-Assisted mmWave MIMO Schemes (BPRPD-CE-RIS-MIMO)respectively.

Smart agriculture uses Internet of Things (IoT) and sensors to increase productivity, optimize resource use, and enhance decision-making. However, the large and varied agricultural data sets pose challenges for traffic management and network security. Traditional software-defined networking (SDN) offers centralized, flexible traffic control, but it cannot detect malicious or abnormal activities in real-time. To fill this gap, we introduce an Artificial Intelligence (AI)-based anomaly detection and traffic management framework for SD-IoT networks in smart agriculture. This system combines a machine learning (ML) Intrusion Detection System (IDS) with the SDN controller, enabling real-time identification of abnormal traffic and adaptive prioritization of critical data flows. The IDS detects unusual traffic patterns, while the SDN controller allocates resources and routes flows based on quality of service (QoS) and security needs. Simulations demonstrate that our system achieves high accuracy in anomaly detection, reduces latency for emergency flows, enhances throughput, and reduces packet loss compared to traditional methods. This work highlights the importance of integrating AI-powered IDS with SDN traffic management to enhance security and communication in smart agricultural networks.

Underwater Acoustic Sensor Networks (UASNs) are essential for offshore engineering, military surveillance, environmental monitoring, and oceanographic exploration. However, challenges including high latency, congested networks, low bandwidth, and energy limitations make it difficult to communicate effectively in UASNs. In order to tackle these problems, this study suggests a new Energy-Efficient Double-Level Deep Reinforcement Learning with Chaotic Chimp Optimization (E2D2RL-ChCo) model that is intended to improve localization precision, reduce packet loss, and effectively handle network congestion. The proposed E2D2RL-ChCo model uses transformer-based Markov Decision Processes (MDPs) for node localization at the top level and congestion-aware routing at the bottom level to guarantee efficient task scheduling and energy-efficient data transfer. By integrating Chaotic Chimp Optimization (ChCo), learning parameters are adjusted to enhance convergence and solution quality. Simulation results demonstrate up to 33.8% reduction in energy consumption, 61.5% decrease in end-to-end delay, and over 52% improvement in successful packet delivery compared to baseline models. The proposed model also improves localization error to 0.0075, outperforming existing strategies. This work fills gaps in existing literature by integrating deep reinforcement learning and metaheuristics to optimize both localization and congestion in real-time UASN scenarios. This approach enables more sustainable and reliable UASN operations, paving the way for enhanced performance in real-world aquatic environments.

The Internet of Things (IoT) can offer more precise, intelligent, and low- or non-human involvement approaches to various sectors. One of the significant uses of the IoT is in smart cities, which includes a variety of services, including smart home, garbage disposal, and smart grid. Many different collaborative IoT integrations are available in smart cities because of these diverse services. A digitized smart city was created to offer complete government cooperation solutions based on digitization and automation to improve residents' quality of life. Information safety and privacy concerns arise when various services need to work together seamlessly. Trustworthy data is vital to the federal government and its constituents, and data accuracy and privacy must be ensured. In this work, we presented a smart contract-enabled smart city and software-defined networking (SDN) in limited contexts during collaborative activities based on a controlled network and decentralization. The proposed collaborative application safety structure is being tested on the Hyperledger blockchain networks. We describe a unique approach to data security through collaborative work in intelligent city governmental design, utilizing Proof-of-Trust Collaboration (PoTC) in Hyperledger blockchains. A security approach based on SDN and smart contracts is employed to safely manage and monitor all connections and transactions across diverse IoT networks. To assess the viability of the proposed decentralized security framework, we created a supported scenario for collaborative activities in an SDN-enabled IoT design. We have conducted various experimental simulations to test the proposed blockchain-integrated SDN-based IoT architecture for a smart city, including throughput, access delay, and trust evaluation. The simulation results show that the proposed SDN-enabled blockchain networks provide better results than existing works.

The digital world has significantly evolved with advancements in internet services and cloud computing. While these improvements have enabled better data protection in cloud environments, they have also introduced new challenges due to the rapid development of various technologies. One of the greatest serious and ever-growing threats is DDoS attacks, which can severely disrupt critical computer systems and render them ineffective. A novel approach is proposed to address this issue by integrating Taylor Elephant Herd Optimization (TEHO) with Univariate Ensemble Feature (UEF) selection. The bio-inspired TEHO algorithm enhances intrusion network features, improving attack detection efficiency and solving complex network challenges. Meanwhile, UEF is employed to extract and refine relevant intrusion network features. The ensemble classification technique further strengthens detection accuracy by combining the results of four different classifiers through a majority voting process. This innovative method is crucial in fortifying the security of essential internet networks and systems against evolving cyber threats. The proposed TEHbUEF framework enhances DDoS attack detection through advanced feature extraction, optimization, and ensemble classification techniques. Initially, the UEF method identifies and selects the greatest significant features using Gain Ratio, Information Gain and Chi-Square tests. These features are further refined using the Relief algorithm to reduce noise and focus on the most relevant data points. The TEHO algorithm then optimizes these selected features by iteratively adjusting their weights to improve their effectiveness within the network, significantly enhancing the system's ability to detect threats. Finally, an ensemble classification approach—combining Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression, and Decision Trees (DT)—ensures robust and accurate detection through majority voting. The comprehensive nature of this approach leads to superior performance in accuracy, false detection rate, recall, precision, detection rate, and F-measure, proving the effectiveness of TEHbUEF over existing methods. With an accuracy of 99.35%, recall of 99.36%, precision of 99.19%, detection rate of 98.47%, and an F-measure of 99.28%, the TEHbUEF approach demonstrates outstanding performance. These results highlight its exceptional ability to detect DDoS attacks with greater accuracy and efficiency, surpassing existing techniques in both detection effectiveness and overall system performance.