Website Fingerprinting (WF) is an emerging traffic analysis technique that enables a passive adversary to infer which websites a user visits. However, most existing studies, whether in single-tab or multi-tab settings, rely on the unrealistic assumption that users only access website homepages, diverging significantly from real-world browsing behavior. Even recent works extending WF to subpages primarily focus on website-level identification, without distinguishing which specific subpages are visited, thereby limiting the attack's granularity and scope. In this paper, we propose HiFi-WF (Hierarchical Fine-grained Website Fingerprinting), a novel framework that breaks the homepage-only assumption and extends WF to multi-tab recognition and fine-grained subpage identification. We formulate the task as a hierarchical multi-label classification problem, jointly modeling the distinctions and correlations between homepages and subpages. To this end, HiFi-WF integrates a unified CNN-based extractor and layered encoder with a Feature Interaction Module based on multi-head cross-attention to capture inter-level dependencies. An Enhanced SubHead enforces hierarchical constraints to suppress invalid subpage predictions, while a cascaded channel–spatial attention mechanism refines discriminative features for precise hierarchical identification. Experimental results demonstrate that HiFi-WF achieves state-of-the-art performance at both hierarchical levels, attaining F1-scores of 92.1% (homepage) and 81.9% (subpage), thereby validating its effectiveness in advancing WF attacks toward realistic, fine-grained, and multi-tab browsing scenarios. Related codes and datasets can be found in https://github.com/wusongyang02-blip/HiFi-WF.

Industrial time series often display complex, non-stationary behaviors with trends, periodicity, and abrupt fluctuations. Generating high-quality synthetic data in such domains is essential for simulation, forecasting, and anomaly detection in Industrial Internet of Things (IIoT) applications. However, distributional heterogeneity, sparse failure patterns, and long-term dependencies make this task highly challenging. We introduce HNRF-TS, a rectified flow framework with hybrid noise initialization, designed for scalable and robust time series generation. The hybrid prior combines isotropic Gaussian noise with structured codes from a lightweight generative adversarial network (GAN), yielding semantically aligned and diverse latent representations. To improve sampling efficiency, we propose a bimodal adaptive strategy that allocates denser ordinary differential equation (ODE) steps at the beginning and end of the trajectory while using coarser steps in smoother middle regions. This preserves critical temporal features while lowering computational cost. We further enhance fidelity with modules dedicated to modeling trends and seasonality, which capture global drifts and periodic signals inherent in industrial data. Across multiple IIoT datasets, HNRF-TS outperforms state-of-the-art baselines, including GAN-based and diffusion-based methods. It achieves up to 75.8% reduction in Context-FID and over 60% improvement in correlation metrics on long-horizon tasks. Moreover, high-quality samples can be generated with as few as 20 sampling steps, offering significant efficiency gains without sacrificing accuracy.

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

Mobile edge computing (MEC) has been proposed as a promising solution to provide real-time computing service for wireless devices (WDs) at the edge of wireless networks. However, the performance of the MEC system is severely affected by wireless attenuation, which fundamentally limits the full potential of the MEC. To address this issue, a system is conceived wherein an unmanned aerial vehicle (UAV) is equipped with MEC servers by fully exploiting the UAV’s high mobility. We develop a comprehensive optimization framework considering real-time dynamic arrival of user tasks. Based on Lyapunov optimization theory, the overall problem is firstly decomposed into sub-optimization problems for each time slot. Then, a high-quality sub-optimal solution is obtained by optimizing WDs’ local computation frequency, scheduling indicator, and UAV trajectory to minimize WDs’ energy consumption alternately under the constraints of the task queue and UAV energy queue stability. Subsequently, an algorithm for task offloading and real-time trajectory optimization is proposed. Simulation results show that the proposed algorithm can effectively reduce energy consumption on the WD side while maintaining the stability of all queues.

Website Fingerprinting (WF) attacks allow passive eavesdroppers to deduce the websites a user visits by analyzing encrypted traffic, threatening user privacy. While current WF attacks achieve high accuracy, they typically assume single-tab browsing, which is unrealistic as users often open multiple tabs, creating mixed traffic. Existing multi-tab WF approaches require large datasets and frequent retraining due to evolving website content, limiting their practicality. In this paper, we introduce Few-shot Multi-tab Website Fingerprinting (FMWF), a novel approach designed to address the limitations of existing multi-tab WF attacks. FMWF directly tackles the challenges of mixed, overlapping traffic traces generated from multi-tab browsing, leveraging two key innovations: (1) an advanced data augmentation technique that synthesizes realistic multi-tab traffic sequences from easily collected single-tab traces, thereby dramatically reducing the need for large-scale real-world traffic data; and (2) a powerful fine-tuning algorithm based on transfer learning that adapts pre-trained models to new, multi-tab environments with minimal additional data. This two-stage framework enables FMWF to capture the complex effectively, overlapping traffic patterns inherent in multi-tab browsing while maintaining a high level of flexibility and significantly lowering computational and data collection burdens. Our experiments, conducted using real traffic traces collected from three widely-used browsers-Microsoft Edge, Google Chrome, and Tor Browser-highlight the superior performance of FMWF in both closed-world and open-world scenarios. Notably, FMWF achieves a minimum 12.3% improvement in accuracy compared to ARES (SP'23) [7], TMWF (CCS'23) [13], and BAPM (ACSAC'21) [10] in the open-world scenario. The code with related datasets is available at https://github.com/WW-Meng/FMWF.

Nowadays, covert communication is envisioned as a promising and secure method of delivering private information. However, higher bit error rates, limited data rates, and vulnerability to advanced machine learning detection methods significantly challenge the application of covert communication. In this paper, we propose a multiple carrier index keying orthogonal frequency division multiplexing (MCIK-OFDM) based covert communication system aided by a chaotic modulation scheme to improve covert data rate and covertness. First, we propose a covert information embedding method by dynamically selecting the activation or deactivation of a subcarrier to embed covert bits according to a previously negotiated covert key between the transmitter and receiver. Then, the chaotic modulation scheme is developed to mask transmitted signals with generated chaotic signals. Moreover, we propose an adversarial machine learning-based (AML) perturbation algorithm to resist the eavesdropper’s detection of covert signals. Furthermore, the closed-form bit error rate (BER) and the achievable covert rate of the proposed covert communication system are derived. Numerical and simulation results demonstrate that the BER of the proposed MCIK-OFDM-based hybrid chaotic covert communication system is much lower than that of conventional chaotic communication systems. In addition, the proposed AML perturbation algorithm can more effectively protect covert communication from being detected by supervised and unsupervised machine learning methods compared to traditional algorithms.

Abstract Due to the explosive growth of communication devices and vehicles, the ever‐increasing communication data traffic is a challenge for mobile communication in the Internet of Vehicles (IoV). A practical solution is to offload the traffic from cellular networks to Wireless Local Area Network (WLAN), that is, WiFi, where the spectrum is license‐free. However, the coordination of unlicensed spectrum severely reduces the utilization rate on the spectrum. In this case, unlicensed networks assisted access is proposed to facilitate long‐term evolution technology in unlicensed spectrum (LTE‐U). To improve the data rate for LTE‐U and WLAN users, an allocation optimization scheme for the resources of the licensed and unlicensed spectrum is proposed. Accordingly, the data rate of LTE‐U users and WLAN users can be maximized by adjusting the transmit power and the frequency occupancy time ratio of the unlicensed spectrum for the small cell and WLAN users. Numerical results show that the proposed hybrid LTE networks with WLAN can improve the communication efficiency for the served users.

The act of website fingerprinting, which involves monitoring traffic features to infer private user information, has attracted much attention in the research community recently. While previous studies primarily focused on classifying fingerprint information using clean traffic data, it remains a challenging task to apply fingerprinting to noisy traffic data or evade defensive measures. This work aims to address the challenges associated with website fingerprinting attacks and defense strategies in the presence of noise. Specifically, we introduce two novel attack methods: filter-assisted attack and augmentation-assisted attack. The first attack method leverages packet size distribution to effectively filter out noise, while the second one trains a classification model by incorporating artificial noise. Compared with the traditional website fingerprinting attacks, these proposed attack methods demonstrate superior resilience to noise and exceptional evasion capabilities against defensive measures (e.g., random packet defense, Walkie-Talkie, WTF-PAD, etc.). In parallel, we propose a list-assisted defense strategy that strikes a balance between defense performance and network overhead. This defense mechanism offers effective protection against website fingerprinting attacks while minimizing the impact on network performance. In our experiments, we employ a comprehensive dataset encompassing TCP/IP traffic with packet size information collected from three prominent web browsers, as well as Tor cell traffic without packet size information obtained from a Tor browser. We thoroughly evaluate our proposed methods in both closed-world and open-world scenarios. Our experimental results shed valuable insights into the influence of noise and the efficacy of different attack and defense approaches on website fingerprinting.

Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods mostly rely on centralized deep training, that is, the sensitive data obtained by edge devices needs to be uploaded to the server, which raises latency and privacy issues. To tackle these issues, we propose to accomplish object detection through sparse federated training with dynamic model aggregation, namely FedWeg, to reduce the communication cost and privacy leakage induced by data transmission. Specifically, FedWeg performs sparse training in edge devices and uploads the lightweight models to the server. To reduce the unnecessary transmission overhead, we propose a dynamic sparsity adjustment scheme that gradually increases the sparsity ratios. Then, we propose to utilize the inverse ratio of sparsity ratios from different edge devices to calculate aggregate weights to diminish the negative impact of sparse training on learning performance. Moreover, we theoretically analyze the convergence rate of FedWeg, which reveals that the impact of network sparsity on model performance, and higher average sparsity rates result in greater errors. Finally, we conduct extensive experiments on four real-life datasets using YOLOv3 and VGG-16. The results show that our FedWeg algorithm outperforms baselines in terms of communication costs and test accuracy.

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.

Community search over bipartite graphs has attracted significant interest recently. In many applications such as the user–item bipartite graph in e-commerce and customer–movie bipartite graph in movie rating website, nodes tend to have attributes. However, the previous community search algorithms on bipartite graphs ignore attributes, thus making them to return results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex [Formula: see text], we aim to find the attributed [Formula: see text]-communities of [Formula: see text], where the structure cohesiveness of the community is described by the [Formula: see text]-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonicity property of attributed bipartite graphs, then we adopt different generating methods and verify the order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

The covert communication technology has emerged as a novel method for network authentication, copyright protection, and providing the evidence of cybercrimes. However, how to design the covert communication scheme in the physical layer of wireless networks and how to optimize the data rate for the covert communication channels are very challenging. In this article, we propose a wireless covert communication system (CCS), where the transmit antennas are selected and coded to generate a covert codebook. According to the covert codebook, the antennas can be dynamically combined to transmit different covert messages. In addition, we adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the covert messages, where the precoding method is designed to deviate the constellations for covert information bits from those for the public information bits. Furthermore, we derive the closed-form expressions of capacity and bit error ratio (BER) for the proposed CCS. To maximize the covert data rate of the CCS, we formulate an optimization problem of the covert data rate and solve the problem to find the optimal precoding matrix. To reduce the covert information leakage, artificial noise is introduced to the system to jam the communication between the transmitting and watching nodes. We design a beamforming scheme to maximize the secure rate for the CCS, where the leakage of covert information can be minimized while the covert communication is not influenced. Simulation results show that the proposed CCS can significantly improve the covert data rate and reduce the covert BER in comparison with the traditional CCSs.

In covert communication systems, covert messages can be transmitted without being noticed by the monitors or adversaries. Therefore, the covert communication technology has emerged as a novel method for network authentication, copyright protection, and the evidence of cybercrimes. However, how to design the covert communication in the physical layer of wireless networks and how to improve the channel capacity for the covert communication systems are very challenging. In this paper, we propose a wireless covert communication system, where data streams from the antennas of the transmitter are coded according to a code book to transmit covert and public messages. We adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the messages, where the constellation of covert information bits deviates from its normal coordinates. Moreover, the covert receiver can detect the covert information bits according to the constellation departure. Simulation results show that proposed covert communication system can significantly improve the covert data rate and reduce the covert bit error rate, in comparison with the traditional covert communication systems.

In this article, we investigate the anti-jamming problem with joint channel and power allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding both mutual interference among UAVs and external malicious jamming to maximize the system Quality of Experience (QoE) relevant to the power consumption. To simultaneously capture the competition and coordination among UAVs, we first model the problem as a local interaction Markov game and then prove it as an exact potential game with at least one Nash equilibrium. Next, we propose a collaborative multiagent layered Q learning (MALQL)-based anti-jamming communication algorithm to reduce the high dimensionality of the action space and analyze the asymptotic convergence of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm, which outperforms the traditional multiagent$Q$learning algorithm when suffering from different jamming strategies.

Network slicing is treated as a key technology of the rapidly developing 5G system. Nevertheless, the environment of the users is extremely complex, leading to a great challenge for allocating the slices in an optimal manner. In this paper, we propose a dynamic slice allocation scheme with two- tier paradigm in consideration of the quality of experience (QoE). In the first tier, called local tier, we employ linear programming aided by a penalty function to allocate the radio resources in the slices to services for user equipments aiming at the best QoE. In the second tier, called edge tier, we design a deep reinforcement learning algorithm to dynamically allocate the computing resources to the edge networks, to achieve the best QoE and highest resource utilization rate. Simulation results demonstrate that the proposed paradigm can achieve better throughput and QoE in comparison with the traditional network slicing paradigms.

Unmanned aerial vehicles (UAVs) are envisaged to play a new and important role in future cellular networks. In this paper, we analyze the uplink performance of heterogeneous networks with UAVs in terms of coverage probability and area spectral efficiency (ASE). To be more specific, we first investigate the system performance under a general channel model, with practical considerations such as (1) line-of-sight and non-line-of-sight components, (2) antenna height difference L between the UAVs and the base stations (BSs), and (3) idle mode capabilities (IMCs) at the BSs to mitigate inter-cell interference. Thereafter, we study the system performance under the latest UAV path loss model defined by the 3rd Generation Partnership Project. Under this special case, we provide a more detailed analysis of the coverage probability as well as the ASE, and explore the impacts of difference system parameters on the system performance. Numerical results validate the analytical expressions and show that (1) the IMC can improve the coverage probability and the ASE, especially when the network is dense, (2) the overall system performance degrades when L increases, and (3) the fractional power control factor has a negligible impact on the UAVs performance when L is large enough.

The hybrid wireless and power line communication (HWPLC) networks address the problem that mobile wireless sensors and power line communication (PLC) sensors cannot communicate with each other within an Internet of Things (IoT) network. In this paper, we design a relay equipped with a dual wireless and PLC interface, which connects both the PLC and wireless sensors into an IoT network. Furthermore, the dual-interface relay forwards messages by adaptively selecting a interface according to the channel state. A general mathematical probability model of the dual-interface relaying system is presented. The probability density function of the output signal-to-noise ratio (SNR) is developed, which is based on explicit closed-form expressions derived from the statistics character of the PLC and wireless channel. Furthermore, the average capacity, bit-error rate (BER) expressions, and the outage probability formulas are derived. Numerical results show that the HPLWC relaying system with the dual-interface can significantly improve the performance of capacity, BER, and outage probability by adaptively selecting the interface with the optimal received SNR.

To offer communication capability in an easy and simple deployment, power line communications (PLCs) have recently attracted interest from the smart grid. The effective sub‐channel assignment can increase throughput of In‐Home (IH) PLC networks with orthogonal frequency division multiplexing scheme distributed over low‐voltage areas. Given a logical topology of an IH PLC network, the authors present a formulation to optimise the sub‐channel assignment problem as a linear programming with an objective function of maximising the network throughput. It takes into account constraints of the network topology, interference and traffic fairness. According to the solution of the optimising sub‐channel assignment problem, the scheduling algorithm of links to obtain channels is developed for every time slot. Evaluation demonstrates that the proposed approach performs much better in improving overall network throughput and ensuring the traffic fairness of network users than conventional ones.

Abstract In this paper, a novel covert channel, called covert behavior channel, is proposed according to behaviors of network users to solve the security and efficiency problem of the traditional covert channel. In the proposed channel, operation sequences of the network protocols are used as carriers of covert information. An encryption‐based information embedding scheme is designed to improve security of the covert information. With the help of Markov model, the capacity of the proposed covert channel with time‐varying noise is derived. The formulation for analyzing the covert behavior channel is presented against the channel noise aroused by discarding packets. By introducing corrected entropy‐based algorithm to detect the covert behavior channel, the security of the channel is verified. Numerical results show that the proposed covert behavior channel is more secure than covert storage channels and achieves a better bit rate and robustness than that of covert timing channels. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we investigate a two-way relaying power line communication (PLC) network with analog network coding. We focus on the analysis of the system outage probability, symbol error rate, and average capacity. Specifically, we first derive the probability density function (PDF) of the received signal-to-noise ratio (SNR) with a closed form, by exploiting the statistical properties of the PLC channel. Then with the help of this PDF, we develop the outage probability, symbol error rate, and average capacity with closed forms, based on the Hermite polynomial. Simulations show that the derived analytical results are consistent with those by Monte Carlo simulation.

This paper proposes a unique discovery signal as an enabler of peer-to-peer (P2P) communication which overlays a cellular network and shares its resources. Applying P2P communication to cellular network has two key issues: 1. Conventional ad hoc P2P connections may be unstable since stringent resource and interference coordination is usually difficult to achieve for ad hoc P2P communications; 2. The large overhead required by P2P communication may offset its gain. We solve these two issues by using a special discovery signal to aid cellular network-supervised resource sharing and interference management between cellular and P2P connections. The discovery signal, which facilitates efficient neighbor discovery in a cellular system, consists of un-modulated tones transmitted on a sequence of OFDM symbols. This discovery signal not only possesses the properties of high power efficiency, high interference tolerance, and freedom from near-far effects, but also has minimal overhead. A practical discovery-signal-based P2P in an OFDMA cellular system is also proposed. Numerical results are presented which show the potential of improving local service and edge device performance in a cellular network.