To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments

Recently, massive research has achieved significant development in improving the performance of rumor detection. However, identifying rumors in an invisible domain is still an elusive challenge. To address this issue, we propose an unsupervised cross-domain rumor detection model that enhances contrastive learning and cross-attention by label-aware learning to alleviate the domain shift. The model performs

The challenge of link overutilization in networking persists, prompting the development of load-balancing methods such as multi-path strategies and flow rerouting. However, traditional rule-based heuristics struggle to adapt dynamically to network changes. This leads to complex models and lengthy convergence times, unsuitable for diverse QoS demands, particularly in time-sensitive applications. Existing

The traditional architecture of networks in Software Defined Networking (SDN) is divided into three distinct planes to incorporate intelligence into networks. However, this structure has also introduced security threats and challenges across these planes, including the widely recognized Distributed Denial of Service (DDoS) attack. Therefore, it is essential to predict such attacks and their variants

High computing power and large storage capacity are necessary for running big data tasks, which leads to high infrastructure costs. Infrastructure-as-a-Service (IaaS) clouds can provide configuration environments and computing resources needed for running big data tasks, while saving users from expensive software and hardware infrastructure investments. Many studies show that the cost of computation

Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic

The advent of 5th Generation (5G) networks has introduced the strategy of network slicing as a paradigm shift, enabling the provision of services with distinct Quality of Service (QoS) requirements. The 5th Generation New Radio (5G NR) standard complies with the use cases Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC)

With the increasing popularity of IoT, there has been a noticeable surge in security breaches associated with vulnerable IoT devices. To identify and counter such attacks. Intrusion Detection Systems (IDS) are deployed. However, these IoT devices use device-specific application layer protocols like MQTT and CoAP, which pose an additional burden to the traditional IDS. Several Machine Learning (ML)

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies

Authenticated Key Exchange (AKE) has been playing a significant role in ensuring communication security. However, in some Multi-access Edge Computing (MEC) scenarios where a moving end-node switchedly connects to a sequence of edge-nodes, it is costly in terms of time and computing resources to repeatedly run AKE protocols between the end-node and each edge-node. Moreover, the cloud needs to be involved

In recent years, due to the existence of intricate interactions between multiple entities in complex networks, ranging from biology to social or economic networks, community detection has helped us to better understand these networks. In fact, research in community detection aims at extracting several almost separate sub-networks called communities from the complex structure of a network in order to

The smart logistics industry utilizes advanced software and hardware technologies to enhance efficient transmission. By integrating smart components, it identifies vulnerabilities within the logistics sector, making it more susceptible to physical attacks aimed at theft and control. The main goal is to propose an effective logistics monitoring system that automates theft prevention. Specifically, the

Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be

With the increasing complexity of network protocol traffic in the modern network environment, the task of traffic classification is facing significant challenges. Existing methods lack research on the characteristics of traffic byte data and suffer from insufficient model generalization, leading to decreased classification accuracy. In response, we propose a method for encrypted traffic classification

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labeled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and

Cyber crimes related to malware families are on the rise. This growth persists despite the prevalence of various antivirus software and approaches for malware detection and classification. Security experts have implemented Machine Learning (ML) techniques to identify these cyber-crimes. However, these approaches demand updated malware datasets for continuous improvements amid the evolving sophistication

Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth

Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network’s capability to adapt to changing environments and improve the overall system’s

In the recent years, emergence huge Edge-Cloud environments faces great challenges like the ever-increasing energy demand, the extensive Internet of Things (IoT) devices adaptation, and the goals of efficiency and reliability. Containers has become increasingly popular to encapsulate various services and container migration among Edge-Cloud nodes may enable new use cases in various IoT domains. In

Undoubtedly, blockchain technology has emerged as one of the most fascinating advancements in recent decades. Its rapid development has attracted a diverse range of experts from various fields. Over the past five years, numerous blockchains have been launched, hosting a multitude of applications with varying objectives. However, a key limitation of blockchain-based services and applications is their

In real-world complex systems, most networks are interconnected with other networks through interlayer dependencies, forming multilayer interdependent networks. In each system, the interactions between nodes are not limited to pairwise but also exist in a higher-order interaction composed of three or more individuals, thus inducing a multilayer interdependent higher-order network (MIHN). First, we

Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analyzed. We propose a Private Token Transfer System (PTTS) for the Ethereum public

Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated

In traditional networks, the static configuration of devices increases the complexity of network management and limits the development of network functions. Software-Defined Networking (SDN) employs controllers to manage switches, thereby simplifying network management. However, with the expansion of network scale, the early single controller architecture gradually became a performance bottleneck for

Fifth-generation (5G) wireless networks are likely to offer high data rates, increased reliability, and low delay for mobile, personal, and local area networks. Along with the rapid growth of smart wireless sensing and communication technologies, data traffic has increased significantly and existing 5G networks are not able to fully support future massive data traffic for services, storage, and processing

Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable

With the surge of transportation data and diversification of services, the resources for data processing in intelligent transportation systems become more limited. In order to solve this problem, this paper studies the problem of computation offloading and resource allocation adopting edge computing, NOMA communication technology and edge(content) caching technology in intelligent transportation systems

In a network, influence maximization addresses identifying an optimal set of nodes to initiate influence propagation, thereby maximizing the influence spread. Current approaches for influence maximization encounter limitations in accuracy and efficiency. Furthermore, most existing methods are aimed at the IC (Independent Cascade) diffusion model, and few solutions concern dynamic networks. In this

Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT

The fifth-generation (5G) network is considered a game-changing technology that promises advanced connectivity for businesses and growth opportunities. To gain a comprehensive understanding of this research domain, it is essential to scrutinize past research to investigate 5G-radio access network (RAN) architecture components and their interaction with computing tasks. This systematic literature review

The rapid proliferation of Android apps has given rise to a dark side, where increasingly sophisticated malware poses a formidable challenge for detection. To combat this evolving threat, we present an explainable hybrid multi-modal framework. This framework leverages the power of deep learning, with a novel model fusion technique, to illuminate the hidden characteristics of malicious apps. Our approach

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a significant transformation across multiple industries, as it has facilitated the automation of jobs, extraction of valuable insights from extensive datasets, and facilitation of sophisticated decision-making processes. Nevertheless, optimizing efficiency has become a critical research field due to AI systems’ increasing

In recent years, serverless computing has gained considerable attention in academic, professional, and business circles. Unique features such as code development flexibility and the cost-efficient pay-as-you-go pricing model have led to predictions of widespread adoption of serverless services. Major players in the cloud computing sector, including industry giants like Amazon, Google, and Microsoft

Crowdsourcing provides a new problem-solving paradigm that utilizes the intelligence of crowds to solve computer-hard problems. Task assignment is a foundation problem in crowdsourcing systems and applications. However, existing task assignment approaches often assume that workers operate independently. In reality, worker cooperation is necessary. In this paper, we address the cooperative task assignment

Distributed Predictive Analytics (DPA) refers to constructing predictive models based on data distributed across nodes. DPA reduces the need for data centralization, thus, alleviating concerns about data privacy, decreasing the load on central servers, and minimizing communication overhead. However, data collected by nodes are inherently different; each node can have different distributions, volumes

Conventional clustering algorithms do not recognize patterns and structures with contradicting objectives in large, distributed datasets. Distributed clustering leverages rapid processing capabilities to allow multiple nodes to work together. This paper proposes a Distributed clustering based on Multiobjective Evolutionary Algorithm by Decomposition (D-MOEA/d) to solve various multiobjective optimization

The advent of sixth-generation (6G) networks is expected to start a new era in mobile networks, characterized by unprecedented high demands on dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence (AI) is at the forefront of this progress, optimizing and enabling intelligence for essential 6G functions such as radio resource allocation, slicing, service offloading

5G-assisted Device to Device (D2D) communication can be broadly categorized into three use case scenarios: In Coverage, Relay Coverage, and Out-of Coverage. The main challenge lies in ensuring secure communication in Out-of Coverage scenarios, as in this situation, neither of the two devices is within the 5G network’s coverage area. Although several researchers have developed authentication mechanisms

Over the past decade, the adoption of Internet of Things (IoT) devices has greatly revolutionized the retail and commerce industries. However, these devices are vulnerable to attacks, such as theft, which raises significant security and privacy concerns for business assets. Securing such business-owned devices is challenging, particularly due to the business contexts that require not only authenticating

Optimizing data traffic is a key concern in content distribution networks to reduce the bandwidth needed to serve the requested content to the final users. In this context, hierarchical coded caching has been proposed as an effective method for reducing traffic. The typical two-level scenario consists in a tree-like structure: on the first layer, a set of intermediate nodes or helpers with local caches

In recent years, the website fingerprinting (WF) attack against the Tor anonymity system has become a hot research issue. The state-of-the-art WF studies have shown that the detection accuracy of websites is up to more than 95%. However, they are mainly conducted under the single-tab assumption, where each sample contains only one website traffic. The single-tab setting could not be realistic because

Surveillance systems are very important to prevent situations where armed people appear. To minimize human supervision, there are algorithms based on artificial intelligence that perform a large part of the identification and detection tasks. These systems usually require large data processing servers. However, a high number of cameras causes congestion in the networks due to a large amount of data

This survey delves into the synergy between Digital Twin technology and Task Offloading within safety-critical sectors, offering a nuanced understanding of their integration, potential benefits, and associated challenges. By defining fundamental concepts and exploring real-world implementations, this study evaluates the impact of Digital Twin-Assisted Task Offloading on optimizing resource utilization

Blockchain technology has emerged as a transformative force in various sectors, including finance, healthcare, supply chains, and intellectual property management. Beyond Bitcoin’s role as a decentralized payment system, Ethereum represents a notable application of blockchain, featuring Smart Contract functionality that enables the development and execution of decentralized applications (DApps). A

Two-tier 360° video streaming provides a robust solution for handling inaccurate viewport prediction and varying network conditions. Within this paradigm, the client employs a dual-buffer mechanism consisting of a long buffer for panoramic basic-quality segments and a short buffer for high-quality tiles. However, designing an efficient edge caching strategy for two-tier 360° videos is non-trivial.

The popularity of the Android operating system has itself become a reason for privacy concerns. To deal with such malware threats, researchers have proposed various detection approaches using static and dynamic features. Static analysis approaches are the most convenient for practical detection. However, several patterns of feature usage were found to be similar in the normal and malware datasets.

The development of the industrial Internet of Things enables industrial control systems to become inter-networked and inter-connected, making them intelligent with high productivity. However, these systems are exposed to external environments and vulnerable to network attacks, which also suffer from internal vulnerabilities. Fuzz testing, in short fuzzing, is a technique to enhance the security of

Wireless Sensor Networks (WSNs) security is a serious concern due to the lack of hardware protection on sensor nodes. One common attack on WSNs is the cloning attack, where an adversary captures legitimate nodes, creates multiple replicas, and reprograms them for malicious activities. Therefore, creating an efficient defense to mitigate this challenge is essential. Several witness node-based techniques

Named Data Networking (NDN) aims to establish an efficient content delivery architecture. In NDN, secure and effective identity authentication schemes ensure secure communication between producers and routers. Currently, there is no feasible solution to perform identity authentication of mobile producers in NDNs. Identity authentication schemes in other networks are either weak in security or performance

To accelerate the convergence speed and improve the accuracy of the federated shared model, this paper proposes a Federated Transfer Learning method based on frozen network parameters. The article sets up frozen two, three, and four layers network parameters, 8 sets of experimental tasks, and two target users for comparative experiments on frozen network parameters, and uses homomorphic encryption

Cloud gaming (CG), as an emergent computing paradigm, is revolutionizing the gaming industry. Currently, cloud gaming service providers (CGSPs) begin to integrate edge computing with cloud to provide services, with the aim of maximizing gaming service revenue while considering the costs incurred and the benefits generated. However, it is non-trivial to maximize gaming service revenue, as future requests

In the Internet of Things (IoT) era, wireless sensor networks play a critical role in communication systems. One of the most crucial problems in wireless sensor networks is the sensor deployment problem, which attempts to provide a strategy to place the sensors within the surveillance area so that two fundamental criteria of wireless sensor networks, coverage and connectivity, are guaranteed. In this

In the rapidly changing cybersecurity landscape, threat hunting has become a critical proactive defense against sophisticated cyber threats. While traditional security measures are essential, their reactive nature often falls short in countering malicious actors’ increasingly advanced tactics. This paper explores the crucial role of threat hunting, a systematic, analyst-driven process aimed at uncovering

In this paper, we address the challenge of minimizing system energy consumption for task offloading within non-line-of-sight (NLoS) mobile edge computing (MEC) environments. Our approach integrates an active reconfigurable intelligent surface (RIS) and employs a hybrid transmission scheme combining time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) to enhance the quality

With the rapid growth of data volume, the escalating complexity of data businesses, and the increasing reliance on the Internet for daily life and production, the scale of data centers is constantly expanding. The data center network (DCN) is a bridge connecting large-scale servers in data centers for large-scale distributed computing. How to build a DCN structure that is flexible and cost-effective

The Consensus algorithm is the core of the permissioned blockchain, it directly affects the performance and scalability of the system. Performance is limited by the computing power and network bandwidth of a single leader node. Most existing blockchain systems adopt mesh or star topology. Blockchain performance decreases rapidly as the number of nodes increases. To solve this problem, we first design

This study introduces a groundbreaking method for predicting network quality in LTE and 5G environments using only GPS data, focusing on pinpointing specific locations within a designated area to determine network quality as either good or poor. By leveraging machine learning algorithms, we have successfully demonstrated that geographical location can be a key indicator of network performance. Our