Transactions on Emerging Telecommunications Technologies最新文献

Cyber security is very important in Wireless Sensor Networks (WSNs) for securing the transfer of files from attackers. Cyber-physical systems (CPs) are essential to monitor and observe the location of data in WSN. CPs are essential to monitor and track the location of data in a WSN. Many researchers have implemented different mechanisms to improve cybersecurity in WSN-enabled CP. These mechanisms are effectively performed based on the mobile anchor node or mobility of the head node. This algorithm suffers from computational complexity. Some traditional cybersecurity systems suffer from data loss, important data theft, and information leakage. In addition, the CPs also suffer from service interference issues. Black holes, scheduling, gray holes, and flooding are some of the examples of common WSN attacks that damage the entire security system in WSN. The WSN has disadvantages such as low identification rates, high computing overhead, and increased false alarm rates. Conventional cybersecurity systems are required to decrease data redundancy and increase the data correlation for better data transformation. In this paper, a new cybersecurity system in WSN is developed to detect WSN intrusions effectively to enhance adaptability and security. The normal and anomalous information is gathered from online resources. Initially, the gathered information is given to the Adaptive and Attention serial Cascaded Ensemble Network (A-ASCENet) for detecting various intrusions. Here, the variational autoencoder, Convolutional Neural Network (CNN), and extreme learning are integrated into a cascaded form to develop an A-ASCENet model. Here, the parameters are optimized using the Revised Fitness-based Lyrebird Optimization Algorithm (RF-ILOA) from A-ASCENet to enhance the performance of cybersecurity. At last, various WSN attacks like gray, scheduling, flooding, black holes, and holes are effectively detected. The performance of cybersecurity in WSN is compared over different traditional methods with some performance metrics.

The emergence of cloud computing has revolutionized business operations by providing effective scalability and flexibility. Security concerns have intensified due to the vast amount of data processed and stored in the cloud; hence protecting cloud infrastructure from cyber threats is crucial. Intrusion detection system plays a pivotal role in seamless monitoring of network traffic for exhibiting unauthenticated or malicious attempts. Recent advancements in IDS highlight certain issues such as low classification accuracy, high false positive rate, as well as overfitting when processing various network data. The feature extraction uses graylevel radial component analysis (GRCA) to extract salient features, while dimensionality reduction is performed by introducing the radial basis function principal component analysis. In this work, the crossover boosted dynamic cheetah optimization algorithm is employed in the feature selection process, which integrates Cheetah Optimization with dynamic evolutionary strategies to improve the overall search efficiency and tackle local optimal issues. The detection and classification of intrusion are performed by proposing a novel threshold-based kernel extreme learning machine, which uses different thresholds to enhance generalization capability. Extensive experimental and statistical analysis is carried out, and the results exhibit that the proposed framework achieves a classification accuracy, precision, recall, F1 score, and security rate of 98.84%, 97.22%, 97%, 97.2%, and 98.85%, respectively, compared to all other existing models. Finally, the classified data is stored in cloud infrastructure that allows third-party monitoring services to assess and analyze critical intrusions and also provide threat analysis.

Digital twin technology has emerged as a key innovation in digitalization, gaining significant attention for its wide applicability across space and manufacturing industries. Its primary goal is to enable efficient command execution and secure data access, empowering users within a virtual environment. Digital twins support various functions, such as real-time monitoring, data analysis, and synchronized operations. However, despite their growing adoption, critical issues related to data privacy and security within digital twin systems remain underexplored. To address this, the article introduces an advanced optimization algorithmic technique, Chronological_Fossa Optimization Algorithm_Secure Key Generation (CFOA_Seckeygen), for generating an optimal key to improve the security and privacy of data stored in a digital twin environment with a blockchain framework. Towards this, different entities, like the twin manager, data owner, database server, and data user, are involved in the authentication process, which is executed by considering different functions, like Exclusive OR (XOR) operations, cryptographic hashing, encryption, and keys. Following this, a secret key is generated using CFOA_Seckeygen to increase security as well as the privacy of digital twin data. Furthermore, the CFOA_Seckeygen model demonstrates superior performance, achieving a communication cost of 3007.556, memory usage of 43.876 MB, a normalized variance of 0.885, and a conditional privacy score of 0.886.

The Internet of Things is growing tremendously due to new technologies, advancements, and big data. With the digitization of data and continuous technological progress, network data traffic has seen a significant increase. This growth makes IoT networks more vulnerable to attacks because of the rising number of devices and the massive amount of data they generate. One of the emerging topics in the research field is security in IoT. The enormous volume of data poses significant challenges to privacy and cybersecurity, and the frequency of attacks is directly proportional to Internet usage. Intrusion Detection Systems (IDS) have proven effective in detecting various attacks, malicious activities, and unauthorized access in IoT networks, helping to prevent intrusions. Furthermore, advanced AI technologies such as machine learning, deep learning, ensemble learning, and transfer learning have shown promising results in efficiently identifying intrusions, attacks, and malicious actions. This paper presents the development of an effective Intrusion Detection System using Machine and Deep Learning algorithms, compares their performance, and identifies the most effective algorithm for securing IoT data while preserving privacy. Random Forest, Convolutional Neural Networks, and Deep Neural Networks are implemented, tested, and compared with other machine learning algorithms, including Decision Trees, Gaussian Naïve Bayes, and XG-Boost. The implementation is carried out in Python, using the benchmark KDD dataset. This paper covers the processes of data generation, preprocessing, analysis, and intrusion detection. The experimental results are compared with other state-of-the-art methods to evaluate overall performance. The performance metrics such as accuracy, precision, recall, and F1 score have been computed for the case of deep learning and machine learning for given IoT network.

Overcrowding of tourists at scenic spots can easily lead to safety accidents and a decline in tourists' travel experience. Designing or recommending tourist routes for tourists is an effective method of passenger flow guidance. The crowdedness of scenic spots is used to describe the crowded conditions of scenic spots, and a tourism experience utility function is proposed. Based on this, considering the constraints of scenic spot service time, travel time and cost budget, a travel route optimization model based on the maximization of travel experience utility is established, and an ant colony algorithm is designed to solve it. On this basis, a time-sensitive travel route recommendation method based on dynamic transition graphs is proposed, a dynamic transition graph model method based on hierarchical clustering is constructed, a method for removing popular sequence anomalies is designed, and a stable pattern law is established. The pattern law accurately recommends the best tourist route suitable for the user's travel time. Through the experimental verification of real data, compared with the existing work, the user's income has increased by more than 10%, which verifies the effectiveness of the proposed method.

This work introduces an orthogonal frequency division multiplexing based discrete cosine transform assisted index modulation with novel signal identification technique. To take use of the design flexibility offered by the twice the number of accessible subcarriers under the same bandwidth, it combines the concepts of IM and DCT assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM). The performance of DCT-OFDM-IM in contrast to OFDM-IM is enhanced in the proposed study by the employment of a deep learning detector. The Deep Learning based detector (DLD), in contrast to conventional detectors like Maximum Likelihood (ML), Greedy Detector (GD), Log Likelihood Ratio (LLR), and others, improves system performance and lowers system overhead. In order to perceive data bits at the OFDM-IM system's receiver in Rayleigh, Rician, and Nakagami-m Fading channels, the proposed DLD uses a Deep Neural Network with completely automated linking layers. To start with, DLD is trained offline by assembling datasets of simulated results in order to enhance BER performance. Next, the model is trained to recognize DCT-OFDM-IM signals at the receiver under various fading channels. The results demonstrate that the DLD outperforms conventional approaches for all multipath fading channels in terms of BER, and that BER for DCT OFDM-IM has improved over that of OFDM-IM.

The advancement of digital technologies is used for providing more enhanced healthcare services to patients in a timely and effective manner. The multi-modal data encompasses a huge amount of information when compared to the single-modal data. Fusing and analyzing various data types provides a more comprehensive understanding of the patient's condition. Fusing these multi-modal data poses several technical challenges because of its data incompatibility. Therefore, this research work focuses on implementing a deep learning-based disease prediction model using multi-modal data to generate precise prediction results regarding healthcare applications. Initially, the required multi-modal data such as signal, data and image are gathered from the standardized benchmark data sources. Then, the collected data is subjected to the implemented multi-modal data-based disease prediction network (MMPredNet). This network is developed by combining an adaptive hybrid deep learning network (AHDLN) and a multi-scale dilated residual attention network (MDRAN). Here, MDRAN performs a feature extraction process to extract the features from the input data. further, the prediction process is carried out using the AHDLN model. It is a hybridized network generated by fusing a deep Bayesian network (DBN) with a deep shallow network (DSN). The parameters of the AHDLN are optimized using the adaptive learning rate-based dove swarm optimization (ALR-DSO) algorithm to reduce FPR and enhance the precision, NPV, and accuracy of the prediction outcome. From the MMPredNet, the final disease prediction outcomes are provided. The performance of the implemented multi-modal data processing model is evaluated with various conventional methods to showcase its effectiveness in healthcare. The accuracy of the developed model on text data is 97.31%, images are 98.02%, and the signal is 97.23%, which is enhanced than the prior works. Hence, it is proved that the developed framework can accurately predict the disease at an early stage and helps to improve patient outcomes and prevent the progression of diseases in patients.

Windy conditions challenge precise payload delivery by unmanned aerial vehicle (UAV), but wind variability defined within lower and upper bounds at any instance can assist to optimize candidate release points. Therefore, at first, it is crucial to collect candidate release points caused by wind variability. In this paper, knowledge of candidate release points is drawn by applying ballistic equation. The knowledge about the points then initializes differential evolution (DE) optimization to search an optimum payload release point. Therefore, the proposed method is named as DE with knowledge-based initialization (KI), that is, DE-KI. Simulations demonstrate DE-KI's effectiveness by measuring landing error as root mean square error (RMSE) and achieve an average reduction in RMSE compared to existing methods. For instance, DE-KI outperforms two other alternative approaches by an average RMSE of $$ and $$ with varying payload weight, and $$ and $$ with varying wind speed.