DNN testing evaluates the vulnerability of neural networks through adversarial test cases. The developers implement minor perturbations to the seed inputs to generate test cases, which are guided by meticulously designed testing criteria. Nevertheless, current coverage-guided testing methods rely on covering model states rather than analyzing the influence of seed inputs on inducing erroneous behaviors

Anomaly detection is crucial for maintaining the stability and security of systems. However, anomaly detection systems often generate numerous false positives or irrelevant alerts, which obscure genuine security threats. To both reduce false positives in time series detection and accurately identify the source of anomalies, leveraging artificial intelligence techniques has emerged as a promising solution

In the context of disaster response and recovery within 6th Generation (6G) networks, achieving both low-latency and energy-efficient communication under compromised infrastructure remains a critical challenge. This paper introduces a novel framework that integrates a solar-powered High-Altitude Platform (HAP) with multiple Unmanned Aerial Vehicles (UAVs) equipped with Reconfigurable Intelligent Surfaces

As internet services and Internet of Things (IoT) devices rapidly expand, Data Center Networks (DCN) have become essential for supporting online services, cloud computing, and big data analysis. These devices generate massive amounts of data continuously, leading to uneven and sudden network loads that traditional networks struggle to handle. Existing routing strategies often rely on fine-grained routing

Swarm intelligence systems are a class of distributed systems in which device nodes utilize distributed algorithms to achieve data consensus and execute complex collective tasks. These systems operate in highly dynamic environments, where unstable network conditions, often induced by environmental complexities, can significantly affect the progress and efficiency of data consensus. To tackle this challenge

Information propagation speed on blockchain social networks depends on activation of users on social network and blockchain network as well. Many researches have been done to increase information propagation speed on social networks. Most of these methods consist of finding influential nodes by different methods to propagate information by these nodes. Some researches have been done to increase information

Web tracking and advertising (WTA) have become pervasive on the Internet, presenting significant challenges to user privacy and data security. Although current defense mechanisms, such as filter list based interceptors and machine learning methods, provide a solution, they do not perform well in complex network environments with missing features, and their large size makes both performance and overhead

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain

Mobile Edge Computing (MEC) has been identified as an innovative paradigm to improve the performance and efficiency of mobile applications by offloading computation-intensive tasks to nearby edge servers. However, the effective implementation of task offloading in MEC systems faces challenges due to uncertainty, heterogeneity, and dynamicity. Deep Reinforcement Learning (DRL) provides a powerful approach

Wireless Sensor Networks (WSNs) are self-organizing distributed network systems composed of numerous sensor nodes. In order to optimize node deployment in WSNs to effectively control energy consumption, improve perception service quality and extend network lifetime, a multi-objective Transit Algorithm (TS) algorithm based on density sorting and cylindrical grid mechanism was proposed for WSN layout

In Federated Learning (FL), clients collaborate with edge and cloud servers to train a global model without sharing raw data. However, cloud servers cannot be fully trusted due to the risk of malicious servers forging aggregation results to compromise model updates. Consequently, secure aggregation of local gradients and verification of the integrity of global parameters are crucial. While existing

Authentication and Key Agreement (AKA) is a critical component for ensuring access control for legitimate users in the Industrial Internet of Things (IIoT). Traditional three-factor AKA schemes rely on the user’s password, biometric data, and smart card to provide robust security. However, existing schemes often suffer from vulnerabilities, such as password guessing attacks, which can occur if either

In wireless rechargeable sensor networks, broadcast charging provides a promising and efficient node energy replenishment approach that allows mobile chargers (MCs) to charge multiple nodes simultaneously. However, MCs usually charge nodes at a fixed location in existing work, which may result in large variability in residual energy levels after charging. Such non-equalization in node energy levels

DNS is a critical component of the Internet infrastructure, and securing it has been an active research domain, with a particular emphasis on countering DDoS attacks. With the rise of programmable data planes, novel defensive strategies taking advantage of their flexibility and line-rate packet processing capabilities have been developed to counter a range of DDoS attacks. This study proposes two novel

The Internet of Things (IoT) has revolutionized the industrial sector, yet digital transformation in this domain faces challenges due to the lack of standardized methodologies for IoT platform development. Many existing approaches struggle with issues such as data heterogeneity, lack of interoperability, and limited scalability when applied to large-scale industrial environments. In order to address

Software-Defined Networking (SDN) is an innovative network architecture that enhances network flexibility by decoupling the data plane from the control plane. However, SDN is also faces severe security threats. One of the most damaging threats is the Distributed Denial-of-Service (DDoS) attack, which can disrupt network functionality and adversely affect legitimate users. Current solutions against

Crowdsourced last-mile delivery leverages workers willing to complete delivery tasks posted by requesters for incentives through dedicated platforms. While this is effective and affordable, crowdsourced delivery faces significant challenges, including the workforce’s uncertain availability and capability to deliver packages safely, inadequate package monitoring, and a lack of trust among the participants

At present, the Global System for Mobile Communications- Railway (GSM-R) is widely used in high-speed railway, but it is a 2G narrowband system that cannot meet the needs of intelligent development of high-speed railways. In the future, space-ground integrated railway communication network will gradually become an inevitable trend of railway development. Aiming at the problems of identity non-mutual

Traditional methods for analyzing Broken Access Control (BAC) vulnerabilities have limitations regarding low coverage of access control rules, high false positive rate (FPR). Additionally, state-of-the-art strategies for repairing BAC vulnerabilities utilizing statement-level replacement as a repair method may introduce new logical errors. To address these challenges, we propose a novel approach called

Detecting network anomalies is a critical cybersecurity task, yet existing methods struggle with high-dimensional data and limited interpretability in latent space. These challenges hinder precise differentiation between normal and anomalous activities due to (i) the chaotic distribution of normal samples, (ii) the absence of constraints to optimize the normal region’s hypervolume leading to high false

The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost

In the digital era, the Internet of Things (IoT) platform is progressively utilized in several applications due to its versatility, adaptability, and accessibility. Despite that, the lack of security protocols, improper device updates, and unauthorized access exposes the IoT environment to diverse cyber threats, which affect network security and user confidentiality. To enhance cybersecurity, various

With the process of democratization of the network edge, hardware and software for networks are becoming available to the public, overcoming the confines of traditional cloud providers and network operators. This trend, coupled with the increasing importance of AI in 6G and beyond cellular networks, presents opportunities for innovative AI applications and systems at the network edge. While AI models

The traditional spectrum management paradigm is no longer sufficient to meet the increasingly urgent demand for efficient utilization of spectrum resources by Internet of Things (IoT) devices. Dynamic spectrum access, as an emerging solution, allows devices to intelligently select appropriate spectrum resources based on real-time demands and environmental changes. In this paper, we propose a dynamic

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of “Shannon’s trap” by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management

This paper examines the collaborative recovery issue for the scalable video broadcasting (SVB) system, where two proximate user nodes are able to maintain a local out-of-band device-to-device (D2D) pair to cooperatively recover their lost broadcasted packets. Traditional error protection methods, such as Forward Error Correction (FEC) and error concealment, often require the source node to dynamically

Software Defined Networking (SDN) enhances network management by decoupling the control plane from the data plane, centralizing control in a software-based controller. While this architecture simplifies network administration, it also introduces vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the central controller and disrupt network operations. Current

The storage performance of blockchain suffers from serious limitations due to its employed full-replication strategy, especially in large-scale network services such as Jointcloud computing and big data processing. To address this challenge, some storage partitioning mechanisms integrating Erasure Coding with Byzantine Fault Tolerant (BFT) consensus protocol are developed, like BFT-Store and PartitionChain

Advancements in technology are driving the demand for high-speed, real-time interactive applications with requirements for faster data rates, reduced latency, and expanded network capacity to deliver immersive user experiences. Additionally, the increasing connectivity needs surpass the capabilities of fifth-generation (5G) networks. Sixth-generation (6G) networks are emerging to address these demands

Wireless mesh network (WMN) technology enhances wireless communication coverage and increases end-to-end (E2E) delay. The delay in WMNs is influenced by various mesh topologies, which are shaped by transmission power factors. Due to the dynamic nature of WMNs, conventional offline topology optimization methods are ineffective. This paper presents a reinforcement learning (RL)-based dynamic collaborative

The Internet of Things (IoT) has revolutionized industries by creating a vast, interconnected ecosystem. Still, the rapid deployment of IoT devices has introduced severe security risks, including DDoS, DoS GoldenEye, DoS Hulk attacks, and Port scanning. Traditional Machine Learning (ML)-based Intrusion Detection Systems (IDS) often operate passively, detecting threats without taking action, and are

Wireless sensor networks (WSNs) face challenges in achieving robust target coverage and connectivity, particularly when varying priorities for targets are modeled with Q-Coverage and Q-Connectivity constraints. However, existing studies often neglect minimizing the number of nodes under these constraints in 3D environments or focus on sensor-to-sensor connections, which are less suitable for target-oriented

Accurate geographical information about nodes is essential in wireless multi-hop networks. Most existing localization algorithms focus on locating nodes in regular network environments, posing challenges for irregular multi-hop networks. To mitigate the impact of irregularities on localization, we propose an Accurate and Stable Localization algorithm (ASL). ASL first infers a hop threshold based on

With the rapid development of cloud storage, a growing number of data owners prefer to outsource their large-scale data to the remote cloud data centers, thus effectively avoiding the heavy burden of storing the massive data by themselves in local. Due to the promising market prospect, plenty of companies invest cloud storage and offer data storage services, which equipped with different access speed

The current Cloud/Edge/Fog platforms, available in practice, do not support dynamic management of computational and network resources in clusters with moveable and constraint nodes, like in applications such as: connected vehicles, Internet of Things (IoT), spacecraft computing, etc. This leads to increased total response time and low QoS for the long-living applications composed of many inter-dependent

Phishing represents a major threat to the financial and privacy security of Internet users, and often serves as a precursor to cyberattacks. While many deep learning-based methods focus on analyzing URLs to detect phishing due to their simplicity and efficiency, they face challenges. Hidden phishing websites may employ tactics like concealing URL addresses, deceiving deep learning models, and attackers

Traditional architectures for implementing Machine Learning Operations (MLOps) usually struggle to cope with the demands of Stream Learning (SL) environments, where deployed models must be incrementally updated at scale and in near real-time to handle a constantly evolving data stream. This paper proposes a new distributed architecture adapted for deploying and updating SL models under the MLOps framework

Federated Learning (FL), a decentralized machine learning paradigm, has gained attention for enabling collaborative model training without sharing raw data. However, traditional FL architectures rely on a central server, creating trust issues, single points of failure, and vulnerabilities to Byzantine attacks due to the lack of effective gradient validation. In this paper, we introduce the Committee-Based

Users living in Cyber-Physical-Social Systems (CPSS) generate massive amounts of data every day. The CPSS data may imply some reliable rules that can help CPSS better provide highly reliable services to humans. Nevertheless, the high-level reliable rules are very difficult to be mined and formalized. Therefore, we propose a Cloud-Fog-Edge Cooperative Reliable CPSS (CFECRC) framework for possibly adding

Accurate traffic flow forecasting is essential for various traffic applications, such as real-time traffic signal control, demand prediction, and route guidance. However, the increasing complexity and non-linearity of big data in the traffic domain pose a challenge for accurate forecasting, necessitating powerful models. This paper proposes a Spatio-temporal model for traffic flow prediction based

Time-Synchronized Channel Hopping (TSCH) is playing an essential role in enabling reliable and energy-efficient communication in low-power wireless applications, thanks to its scheduling and adaptive channel hopping capabilities. Advancements in these areas are vital for further improving TSCH networks performance. Enhanced scheduling algorithms can reduce energy consumption and increase network capacity

The quantum network aims to establish connections among multiple quantum nodes, supporting various innovative applications. Many of these applications necessitate the sharing of entangled pairs among communicating parties. However, the inherent nature of entanglement leads to an exponential decrease as the distance between quantum nodes increases. This phenomenon makes it challenging for entangled

Connectivity problems are among the most challenging issues in Mobile Wireless Sensor Networks (MWSNs). Ensuring connectivity in such networks means finding network configurations in which all mobile sensors can connect to a base station during data gathering events. This paper considers MWSNs in which a minimal number of relay nodes need to be placed in order to maintain connectivity. Two algorithms

In the evolution of Fifth-Generation (5G) oriented wireless communication technology, the conventional wireless communication performance indicators, including network capacity, spectrum efficiency, and Quality of Services (QoS), need to be continuously improved to optimize the utilization of the wireless spectrum. As a key candidate technology for 5G, Device-to-Device (D2D) communication improves

The popularity of information and communication technology in automobiles has led to the next generation of smart vehicles known as Connected and Autonomous Vehicles (CAVs). The advent of the CAVs has rapidly emerged as an essential component of Intelligent Transportation Systems (ITS) due to the usage of advanced technologies for autonomous navigation, sensing, and improved vehicle safety. Moreover

Internet of Things is significantly advancing the development of modern interconnected networks. Coordinated with cloud computing, this technology becomes even more powerful, cost-effective, and reliable. These advancements are rapidly being integrated into modern healthcare through innovations such as smart ambulances, remote monitoring systems, and smart hospitals, enhancing tracking, analysis, and

Generic notions about associations between certain diseases and diets are quite popular, but there are many evidences of other unknown disease-diet associations in literature that need to be fully explored. Such associations are currently being studied by medical researchers through meta-analysis or other prospective studies limiting it to a certain population or area. This study aims to use a combined

Maritime data security plays a crucial role in in Navy and Coastal Areas, where the detection of vessels is sensitive as well as boundless and demands privacy preservation and accurate identification. While manual identification of vessels can be challenging, advancements in Cryptographic hash functions, Deep Learning technology, and image processing have simplified the task. However, existing techniques

Underwater Wireless Sensor Networks (UWSNs) play a pivotal role in marine exploration and monitoring, yet face significant challenges in maintaining energy efficiency and dynamic adaptability. This paper introduces the Energy-efficient Hierarchical Dynamic Depth Adjustment Routing Protocol (EHDDAHRP) for UWSNs. EHDDAHRP integrates innovative features, including Distributed Sink Mobility and Semantic

The widespread use of blockchain technology has highlighted smart contracts as crucial components in digital transactions. However, their susceptibility to vulnerabilities poses significant challenges to security and dependability. This survey presents a comprehensive evaluation of 256 smart contracts analysis tools, categorized by methodologies such as fuzzing, machine learning, symbolic execution

Challenges pertaining to user anonymity and data privacy are among the major concerns in blockchain-based bilateral Peer-to-Peer Energy Trading (P2P-ET). However, existing solutions focus only on user anonymity and are severely exposed to fake energy offers that can lead to denial-of-service attacks. Moreover, the off-chain communication mechanism used for private energy negotiation is computationally

Automated Exposure Notification techniques have been developed as pervasive risk assessment tools in public crisis management, particularly for the containment of infectious diseases. However, the widely adopted Bluetooth Low Energy or location-based AEN techniques face challenges in simultaneously providing fined-grained, real-time, and location privacy-preserving detection and notification. Therefore

Named Data Network (NDN) is a content-centric, name-based communication architecture, with a push-based communication model naturally supports consumer mobility. However, the management of producer prefix authentication during mobility is challenging due to NDN’s name-based mechanism, which facilitates direct interaction between producers and the forwarding plane. The current solutions fail to balance

The Network-on-Chip (NoC) integrates all components within a System-on-Chip (SoC), positioning itself as the SoC’s most critical element. The interconnection network, which forms the foundational topology of the NoC, significantly impacts its performance. As network scale and complexity increase, the inevitability of faults emerges, underscoring the crucial need for robust fault tolerance. In this

Cloud computing manages many resources and alterations to meet the demands made by consumers at multiple locations and in numerous applications. Cloud computing presents a significant obstacle to efficient resource usage and balance of loads due to the dynamic nature of consumer requirements and tasks. The inflexibility of conventional methods guarantees inadequate outcomes and waste of resources.

The regulatory of task classification or clustering and hybrid containers in cloud data centers has a lower overhead of cost compared to virtual machines, also it has a direct impact on the load balance, accessibility of virtual machines, and increase of efficiency. Therefore, additional resources with high computing power usage are one of the important issues. In the proposed method merging the index

With the rapid growth of the Industrial Internet of Things (IIoT), an abundance of data is generated, and various data acquisition, analytics, and storage mechanisms are developed intelligently for smart industrial productions. Big heterogeneous data of IIoT suffers from security and privacy issues, which are the main hurdles for smooth industrial operations and pose a serious concern to the widespread

The escalating prevalence of Advanced Persistent Threats (APTs) necessitates the development of more robust solutions capable of effectively thwarting these attacks by monitoring system activities across individual hosts. Existing cloud-native security applications utilize a combination of rule-based and machine learning-based detection techniques to protect digital assets. However, these approaches