Transactions on Emerging Telecommunications Technologies最新文献

Voice over Internet Protocol (VoIP) has emerged as a game-changing communication technology given that it allows for low-cost long-distance conversations with plenty of additional benefits. In this era of cloud computing, VoIP can offer even cheaper calls and scalable services with the help of virtualized telephone infrastructure. The integration of virtualized telephone infrastructure with VoIP is known as “cloud-based VoIP.” In this paper, we investigate a cloud-based VoIP under the advanced persistent threat (APT) attack. An APT attack is a sophisticated type of cyberattack that tries to steal personal information by staying in the infected system for an extended period of time, thereby impacting the system dependability. “Dependability is a measure of a system's availability, reliability, maintainability, and in some cases, other characteristics such as durability, safety and security”. Hence, we develop a robust mechanism for mitigating APT attack in a cloud-based VoIP phone system and investigate its dependability to minimize the aftermaths of the attack. We employ a semi-Markov process (SMP) model to study the dependability as it gives consideration to the non-Markovian nature of the holding times of various system states. The SMP model is then used to analyze both the time-dependent behavior and the long-term (stationary) performance characteristic of the cloud-based VoIP system, specifically in terms of availability, reliability, and confidentiality. Numerical results are displayed graphically, and the proposed dependability model is supported by stochastic simulation. It has been established from the numerical results that the cloud-based VoIP is the most sensitive and critical when it is exploited by cyberattacks, and the lifetime of the system can be extended if the weaknesses of the system are discovered before it is exploited by the attackers.

The scalability, adaptability, and pay-per-use nature of cloud computing have contributed to its meteoric rise to prominence, enabling customers to access services regardless of their physical location. A major obstacle to effective resource management is the wide variety of services provided and the wide variety of user needs. Due to inadequate resource use and suboptimal scheduling tactics, cloud data centers, which consist of physical machines (PMs) hosting many virtual machines (VMs), frequently experience significant energy consumption. A task scheduling technique is introduced in this study to tackle energy efficiency in cloud environments. It integrates two meta-heuristic algorithms. A Slack-based classification algorithm is used first to cluster tasks and then rank them according to their criticality. In order to schedule vital work, we use the Remora Optimization Algorithm (ROA). For noncritical jobs, we use Particle Swarm Optimization (PSO). Researchers tested several configurations ofVMs and job counts in an experimental setting and then compared the outcomes to those of more conventional approaches like Genetic Algorithm (GA) and baseline PSO. The proposed approach shows promise as an efficient scheduling approach for environmentally conscious cloud computing, thanks to its substantial reductions in execution time and energy consumption. Evaluations were carried out in a simulated cloud environment, incorporating different task counts and VM configurations. The proposed mechanism underwent a comparative analysis with eight benchmark methods. The findings indicate that the proposed method shows a marked superiority over current techniques, realizing a 33.5% decrease in execution time (168.57 s compared to 253.47 s) and an 11%–52% enhancement in energy efficiency (0.653 kWh vs. a maximum of 0.852 kWh). The results validate the efficacy of the scheduling strategy in improving energy efficiency and performance within cloud computing environments.

Vehicular ad hoc networks (VANETs) enable real-time communication but are vulnerable to security threats, particularly distributed denial of service (DDoS) attacks, that cause delays and network failures. Traditional static detection systems struggle to adapt to dynamic traffic conditions. To address this problem, we propose ELITE, a lightweight and intelligent DDoS detection framework designed for secure VANETs. ELITE employs a three-layer architecture featuring a random fuzzy tree (RFT) classifier, which combines the speed of decision trees with adaptive fuzzy reasoning for efficient anomaly detection. It also includes a latency-aware scheduling system that ensures the urgent traffic is handled, while a few essential requests are sent to nearby edge servers or to the cloud. This work has three distinct contributions: integration of X and Y into one intelligent smart-environment architecture, like edge-cloud optimization model 96% stability of delay-sensitive edge-cloud optimization, and development of a lightweight threat detection module with improved accuracy and real-time capability. Experimental results demonstrate that ELITE achieves a high detection accuracy of 95.7%, effectively adapts to traffic changes, reduces false positives, and improves latency performance.

Over the last few years, cloud computing has emerged as the best option for offering various applications. It can supply databases, web services, processing, storage, development platforms, and web services to help businesses swiftly expand their infrastructure and service offerings. However, massive amounts of data will severely burden the cloud computing environment. Due to this, load-balanced task scheduling has remained a crucial aspect of resource distribution from a data center, ensuring that each virtual machine (VM) has a balanced load to fulfill its full potential. Overloading or underloading a host or server can cause issues with processing speed or even cause a system crash. To prevent this, we need an intelligent way to schedule tasks. Therefore, the hybrid optimization algorithm called gazelle coati optimization algorithm (GCOA) is introduced in this paper to schedule tasks in a cloud environment. This algorithm integrates the coati optimization algorithm (COA) and the gazelle optimization algorithm (GOA) to enhance the GOA's exploitation process. The main objective of this hybrid approach is to optimize scheduling, maximize VM throughput and resource utilization, and establish load balancing between VMs based on makespan, energy, and cost. The performance assessment of the proposed approach is conducted on two real-world workloads, such as Google Cloud Jobs (GoCJ) and the heterogeneous computing scheduling problems (HCSP) datasets, using several performance metrics, and the results are compared with the previous scheduling and load balancing methods. The experiment results show that the suggested strategy produced significant gains in makespan, energy, cost, resource utilization, and throughput—up to 10% and 60%, respectively—making it appropriate for real-world cloud infrastructures.

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.