As intellectual property rights, the copyright protection of deep models is becoming increasingly important. Existing work has made many attempts at model watermarking and fingerprinting, but they have ignored homologous models trained with similar structures or training datasets. We highlight challenges in efficiently querying black-box piracy models to protect model copyrights without misidentifying homologous models. To address these challenges, we propose a novel method called DeepCore, which discovers that the classification confidence of the model is positively correlated with the distance of the predicted sample from the model decision boundary and piracy models behave more similarly at high-confidence classified sample points. Then DeepCore constructs core points far away from the decision boundary by optimizing the predicted confidence of a few sample points and leverages behavioral discrepancies between piracy and homologous models to identify piracy models. Finally, we design different model identification methods, including two similarity-based methods and a clustering-based method, to identify piracy models using the models’ predictions of core points. Extensive experiments show the effectiveness of DeepCore in identifying various piracy models, achieving lower missed and false identification rates, and outperforming state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

With the development of edge computing and sharing economy, more services and contents are decentralized to the edge of the network. At present, most existing studies combine the content caching with service offloading mechanisms from the perspective of edge services to optimize network performance. However, few studies focus on what strategies the content providers (CPs) can implement to maximize their utilities. In order to motivate content providers to be more willing to share their contents, it is necessary to study the incentive mechanisms in the edge content market so that content audiences (CAs) can pay reasonable prices for high-quality contents. In this article, we first characterize the content supply and demand model, the CPs' cost and utilities in edge content market by considering both the time sensitivity of edge content and the heterogeneity of CAs. Furthermore, according to the edge content market environment characteristics, we divide the edge content market into a monopoly environment, where the content is only provided by a certain CP, and an open environment, where content services are provided by multiple CPs. In the monopoly environment, we establish a two-stage Stackelberg game to design the incentive mechanism. In the open environment, also we formulate the competitive behavior among CPs as a stochastic game. Since the CPs are not aware of each other's strategies and environmental uncertainty, the reinforcement learning-based algorithm (RLIMO) is used to derive the pricing strategy of CP. Finally, numerical results show that the proposed incentive mechanisms are reliable and effective.

The proliferation of portable embedded cameras makes it easy for mobile users to collaboratively collect environmental sensing videos in network-underdeveloped or disaster-affected zones. Compared with in other scenes, the video upload in disaster areas is more challenging due to intermittent wireless connections and moving transmission destinations. Deduplication (redundancy elimination) and cooperative video transmission are two effective ways to ensure timely video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video transmission routing. To solve such problems, this paper proposes a collaborative upload method of sensing videos, which performs the local video deduplication without excessive comparisons and feature exchanges. Also, we combine the two-round deduplication with the content-aware dynamic routing to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and achieve load balance. We formulate the deduplication-supported collaborative video upload as a multi-stage decision problem. To tackle the time-varying destinations and the local deduplication during transmission in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVUA) to route videos and remove duplicates. Extensive numerical validations are conducted to compare MAVUA with the existing algorithms. The numerical results demonstrate that MAVUA can save roughly 11% transmission time and achieve 80% load balancing improvement.

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

In recent years, the rapid development of urbanization has posed enormous challenges to transportation, security management, quality of life, and so on, which makes the research and development of smart city important. As an information-driven project, strong communication infrastructures are required for connecting smart objects, people, and sensors together. As a consequence, the optimization of the wireless network is the primary premise to support and improve the quality of smart services. The challenge lies in the difficulty to achieve the quantitative description of networks due to the complexity and variability of wireless environments. It is too coarse-grained to express characteristics of networks simply by the strength of received signals through mobile devices. To extract more fine-grained network characteristics, we dig into the PHY layer collecting CSI for network descriptions and extract three signal characteristics, i.e., Rician-K, delay spread and spectral width, from real-world wireless channels. The K-means clustering algorithm is implemented in this article for good performance of network partitioning, and experiments are conducted based on the data sets collected from real scenarios. Simulation results verify the feasibility of our scheme. © 1986-2012 IEEE.

Accurate objects detection in remote sensing images is very important, because security, transportation, and rescue applications in military and civilian fields require fully analyzing and using these images. To address the problem that many small-sized objects in remote sensing images are difficult to detect, this paper proposes an improved S(2)ANET-SR model based on S(2)A-NET network. In this paper, the original and reduced image are fed to the detection network at the same time, and then a super-resolution enhancement module for the reduced image is designed to enhance the feature extraction of small objects, after that, the perceptual loss and texture matching loss is proposed as supervision. Extensional experiments are conducted to evaluate the performance on the general remote sensing dataset DOTA, and the results show that our proposed method can achieve 74.47% mAP, which is 0.79% better than the accuracy of S(2)A-NET. (C) 2021 Elsevier B.V. All rights reserved.

随着LoRa、NB-IoT以及5G等通信技术的发展,物联网设备数量与日俱增,物联网安全也日益成为物联网应用关注的热点。物联网设备识别是物联网设备安全评估、防护和升级的必备前提,设备识别的目标是确定设备的类型、品牌、型号和固件版本等属性信息,尤其是细粒度的设备型号和固件版本信息,与设备漏洞直接关联,能够更准确的反映出设备的安全状态。然而面对物联网设备数量庞大、品牌类型繁多以及服务协议混杂等现实存在的问题,物联网设备的识别在识别精度、识别粒度、特征空间以及识别时效性等方面受到诸多挑战。本文采用主动设备识别技术,从标语、字段以及Web管理平台等多个方面开展研究,利用多协议融合、重传机制、跨层协议以及弱口令漏洞等各种技术和策略,实现了对设备型号和固件版本的细粒度识别。本文的创新性工作和主要贡献如下:（1）针对多协议标语识别的时间开销和识别精度平衡难题,本文提出了一种多协议探测优化调度机制来实现基于多协议标语的设备识别。利用强化学习思想,将物联网设备多协议探测报文的调度问题建模为马尔可夫决策过程;通过统计每种协议标语中所含设备属性信息的概率,构建基于标语设备识别过程的马尔可夫状态转移矩阵,改进了现有的价值迭代算法,生成最优协议探测序列;实验验证结果表明所提出的方法显著提高了设备识别的准确率和时间效率,并在路由器和打印机类设备上进一步验证了该算法的可扩展性。（2）针对TCP协议字段特征设备差异性不足的问题,本文设计了一种基于重传TCP报文字段特征的物联网设备识别方法。通过改进TCP三次握手机制,设计了一种无连接的重传TCP报文探测规则,高效获取报头字段来增加设备识别的指纹粒度,并通过量化各类设备字段特征的一致性和差异性筛选出不同的特征字段组合,利用Bagging集成分类器实现动态的物联网设备识别机制,并通过实验验证了该识别方法的高效性和准确性。（3）针对单协议字段特征设备差异性不足的问题,本文利用HTTP和TCP协议在物联网场景下的通用性优势,提出了一种基于跨层协议字段特征的大规模细粒度设备识别方法。基于TCP三次握手过程设计了一组跨层报文探测策略,高效获得了5种跨层响应报文;通过设计字段特征的一致性和差异性度量标准,筛选出HTTP和TCP跨层协议的特征字段,并利用CNN+LSTM+Soft Max神经网络模型实现了跨层设备识别的原型系统,通过实验验证了跨层协议在设备型号识别准确率和召回率上的有效性。（4）针对固件源码分析困难的现实挑战,本文另辟蹊径,通过对物联网设备Web管理页面的内容分析,提出了一种基于弱口令的大规模细粒度设备固件识别方法。利用物联网设备普遍存在的弱口令漏洞获取在线物联网设备的Web管理页面内容,并通过设计自动化的登录页面特征聚类方法和网页内容分块分析算法,获取固件版本所在页面,利用正则表达式实现固件版本的识别。实验结果也验证了该方法在设备固件识别中的有效性。

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm. To motivate data owners to contribute towards FL, research on FL incentive mechanisms is gaining great interest. Existing monetary incentive mechanisms generally share the same FL model with all participants regardless of their contributions. Such an assumption can be unfair towards participants who contributed more and promote undesirable free-riding, especially when the final model is of great utility value to participants. In this paper, we propose a Fairness-Aware Incentive Mechanism for federated learning (FedFAIM) to address such problem. It satisfies two types of fairness notion: 1) aggregation fairness, which determines aggregation results according to data quality; 2) reward fairness, which assigns each participant a unique model with performance reflecting his contribution. Aggregation fairness is achieved through efficient gradient aggregation which examines local gradient quality and aggregates them based on data quality. Reward fairness is achieved through an efficient Shapley value-based contribution assessment method and a novel reward allocation method based on reputation and distribution of local and global gradients. We further prove reward fairness is theoretically guaranteed. Extensive experiments show that FedFAIM provides stronger incentives than similar non-monetary FL incentive mechanisms while achieving a high level of fairness.

With the rapid development of information technologies, industrial Internet has become more open, and security issues have become more challenging. The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge. However, endogenous security lacks a scientific and formal definition in industrial Internet. Therefore, firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis. Secondly, the endogenous security innovation mechanism is clearly defined. Thirdly, an improved clone selection algorithm based on federated learning is proposed. Then, we analyze the threat model of the industrial Internet identity authentication scenario, and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof. We conduct identity authentication experiments based on two types of blockchains and compare their experimental results. Based on the experimental analysis, Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet. Internet of Things Application (IOTA) public blockchain can be used for data aggregation analysis of Internet of Things (IoT) edge nodes. Finally, we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

The widespread use of the Internet of Things (IoTs) and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings. Within such systems, all participants related to commercial and industrial systems must communicate and generate data. However, due to the small storage capacities of IoT devices, they are required to store and transfer the generated data to third-party entity called "cloud", which creates one single point to store their data. However, as the number of participants increases, the size of generated data also increases. Therefore, such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security, privacy, and performance. To address these challenges, Federated Learning (FL) has been proposed as a reasonable decentralizing approach, in which clients no longer need to transfer and store real data in the central server. Instead, they only share updated training models that are trained over their private datasets. At the same time, FL enables clients in distributed systems to share their machine learning models collaboratively without their training data, thus reducing data privacy and security challeges. However, slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system. Furthermore, these unnecessary communication rounds make the system vulnerable to security and privacy issues, because irrelevant model updates are sent between clients and servers. Thus, in this work, we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song (CKKS) to encrypt model parameters for their local information privacy-preserving function. The proposed solution uses the impetus term to speed up model convergence during the model training process. Furthermore, it establishes a secure communication channel between IoT devices and the server. We also use a lightweight secure transport protocol to mitigate the communication overhead, thereby improving communication security and efficiency with low communication latency between client and server.