The tight cyber–physical coupling in industrial control system (ICS) makes it vulnerable to attacks, where attackers can exploit system vulnerabilities to cross domain boundaries, causing significant losses. A novel quantitative risk assessment framework for ICS is proposed, integrating both the cyber domain and the physical domain to address potential risk assessment issues. First, an entropy-weighted technique for order preference by similarity to ideal solution method introducing triangular fuzzy numbers is proposed to solve the vulnerability index for multiattribute decision making to obtain the a prior probability. Second, the risk propagation mechanism of complex network topology and device interaction under ICS was studied, and the posterior probability model based on susceptible-exposed-infectious-recovered was designed to dynamically capture the propagation characteristics of risks in the network. Finally, the safety loss level is defined based on standard criteria to achieve quantitative risk assessment of ICS. The proposed risk assessment model was experimentally validated on the SWaT testbed, with results confirming its feasibility and effectiveness.

Self-supervised representation learning (SSL) typically suffers from inadequate data utilization and feature-specificity due to the suboptimal sampling strategy and the monotonous optimization method. Existing contrastive-based methods alleviate these issues through exceedingly long training time and large batch size, resulting in non-negligible computational consumption and memory usage. In this paper, we present an efficient self-supervised framework, called GLNet. The key insights of this work are the novel sampling and ensemble learning strategies embedded in the self-supervised framework. We first propose a location-based sampling strategy to integrate the complementary advantages of semantic and spatial characteristics. Whereafter, a Siamese network with momentum update is introduced to generate representative vectors, which are used to optimize the feature extractor. Finally, we particularly embed global contrastive and local location tasks in the framework, which aims to leverage the complementarity between the high-level semantic features and low-level texture features. Such complementarity is significant for mitigating the feature-specificity and improving the generalizability, thus effectively improving the performance of downstream tasks. Extensive experiments on representative benchmark datasets demonstrate that GLNet performs favorably against the state-of-the-art SSL methods. Specifically, GLNet improves MoCo-v3 by 2.4% accuracy on ImageNet dataset, while improves 2% accuracy and consumes only 75% training time on the ImageNet-100 dataset. In addition, GLNet is appealing in its compatibility with popular SSL frameworks. Code is available at GLNet.

The dissemination of digital disinformation in online social networks (OSNs) has been the subject of extensive research, although many challenges remain, including the analysis and control of disinformation dissemination across different platforms (i.e., cross-platform). In this article, we investigate and analyze the spreading patterns and regularities of disinformation both within a single platform and across platforms. To explore the complex relationship between user propagation desire and behaviour within the same group, a user propagation desire inference model based on propagation characteristics (behaviour characteristics and time characteristics) and a bidirectional backpropagation (B-BP) deep neural network are constructed. Then, to avoid overfitting due to the interaction of users’ propagation behaviour and the correlation among propagation characteristics, a novel adaptive weighted particle swarm optimization evolutionary algorithm is utilized to further optimize the B-BP deep neural network. We design and conduct a series of evaluation experiments on the current global hot topics including but not limited to novel coronavirus-19 pandemic (COVID-19), food safety, medical and health, and environmental protection. By using a real-world social platform and its social situation metadata analysis, the experimental results show that the proposed method not only accurately predicts the level of user propagation desire under multiple behaviour interactions but also facilitates social platform managers in handling disinformation disseminators. Our findings reveal that the intensity of social users’ desires to spread disinformation is related to the topics and groups that users are interested in, while the propagation motivation of social users is not strong under topics that users are not interested in. Our studies also demonstrate that social users with propagation desires tend to utilize their familiar social platforms and local circles for communication, and the behaviour and desire to spread disinformation to the cross-platform are not strong. We posit that these findings can help inform online and, fine-grained governance and mitigation strategies other than “one size fits all” approaches (e.g., “account prohibition and deletion”), and hopefully minimize disinformation dissemination.

With the increasing demand for high speed and low latency services on the Internet of Vehicles, researches on wireless networks in intelligent connected vehicles (ICVs) with communication and caching capability have attracted much attention. Content retrieving in ICVs is subject to performance degradation as a result of channel fading and intermittent network connectivity. The emerging fifth-generation (5G) networks are promising in supporting the needs of data transmission and alleviating the communication problems in ICVs. Specifically, to improve the users' quality of experience (QoE) and reduce the access delay of content retrieval, it helps to leverage in-network caching in on-board units and small cell base stations (SBSs). In this paper, we propose a cooperative caching scheme based on content popularity and transmission power restriction for inhomogeneous ICV, which pre-caches content files at SBSs to significantly reduce content retrieval delay. In specific, we model the proposed system as a cache management problem and attain optimal QoE by allocating proper transmission power for each content file. Using extensive simulations, we demonstrate that the proposed solution can effectively provide service for ICVs with high QoE in different scenarios.

Recommender systems have been recognized as an effective way to deal with the information overload problem, which can recommend accurate and positive items to users from a large volume of choices. Due to its capability and simplicity, collaborative filtering (CF) is one of the most popular techniques for recommender systems. However, CF suffers from three issues which are user cold-start, item cold-start and data sparsity problems. These issues severely degrade the performance of CF. To address these issues, a hybrid collaborative filtering recommendation model, termed TrustCF, is proposed in this paper, based on user-item ratings and trust relations among users. TrustCF integrates ratings from trusted friends and similar users, ratings of similar items, item reputation and user history ratings. In particular, we modify the similarity calculation formula, considering the effect of the number of co-ratings. Trust relations among users are used to make predictions. In this way, TrustCF can alleviate the data sparsity problem and improve the recommendation performance. Experimental results on two real-world datasets verify the effectiveness of the proposed TrustCF model and show TrustCF has better recommendation accuracy than other five counterparts.

Modbus over TCP/IP is one of the most popular industrial network protocol that are widely used in critical infrastructures. However, vulnerability of Modbus TCP protocol has attracted widely concern in the public. The traditional intrusion detection methods can identify some intrusion behaviors, but there are still some problems. In this paper, we present an innovative approach, SD-IDS (Stereo Depth IDS), which is designed for perform real-time deep inspection for Modbus TCP traffic. SD-IDS algorithm is composed of two parts: rule extraction and deep inspection. The rule extraction module not only analyzes the characteristics of industrial traffic, but also explores the semantic relationship among the key field in the Modbus TCP protocol. The deep inspection module is based on rule-based anomaly intrusion detection. Furthermore, we use the online test to evaluate the performance of our SD-IDS system. Our approach get a low rate of false positive and false negative.

Large holes are unavoidably generated in depth image based rendering (DIBR) using a single color image and its associated depth map. Such holes are mainly caused by disocclusion, which occurs around the sharp depth discontinuities in the depth map. We propose a divide-and-conquer hole-filling method which refines the background depth pixels around the sharp depth discontinuities to address the disocclusion problem. Firstly, the disocclusion region is detected according to the degree of depth discontinuity, and the target area is marked as a binary mask. Then, the depth pixels located in the target area are modified by a linear interpolation process, whose pixel values decrease from the foreground depth value to the background depth value. Finally, in order to remove the isolated depth pixels, median filtering is adopted to refine the depth map. In these ways, disocclusion regions in the synthesized view are divided into several small holes after DIBR, and are easily filled by image inpainting. Experimental results demonstrate that the proposed method can effectively improve the quality of the synthesized view subjectively and objectively.

With the rapid development of the cloud computing service, utilizing traditional access control models was difficult to meet the complex requirements of data protection in cloud environment. In cloud environment, the definition of data and its protection are gradually varied when contents shifting from one virtual machine to another; in these new scenarios, the multi-tenancy pattern has been taken as a core attribute. For this reason, many users need to change their roles according to different situations; certifications has impact much more complicated challenges in cloud environment while access control was suitable for the static status but no longer for the changing situation. In this paper, a new usage control protocol model—multi-UCON (MUCON) based on usage control (UCON), combined with encryption technology and the digital watermarking technology, is proposed with the characteristics of flexible accrediting, feature binding, and off-line controlling. The analysis and simulation experiments indicate that the proposed protocol model is secure, reliable, and easy to be implemented, which can be deployed in cloud computing environments for data protection.

Nowadays multimedia social networks are fueled by sensational coverage of sex, violence and crime. In this paper, we provide a normalized cross entropy metric to determine whether a headline is a sensational headline or not by the literal consistency between the headline and its corresponding document. Experiments on a Chinese data set show that the traditional relevancy measurements—vector cosine, relative entropy, likelihood and cross entropy—suffer from strong dependence on text length and are unable to effectively identify sensational headline. The experimental results on both Chinese data sets and English data sets show that our metric can cover the positive effects of high-frequency words and overcome the negative effects of the lengths of the title and the document.

A digital certificate scheme in content protection system for high definition digital interfaces is proposed. Before transferring the encryption content, the device interface is firstly confirmed whether it is passed the certification. After the certification, then transfers the content. The interface of the receiver detects whether the recognition management unit has passed the certification. The digital content is ensured by the recognition management unit, which can prevent the invalid capturing the information. In the period of certification, the characteristics of the certificate are fully made use of. The application shows the system is high secure, which can be used in the high definition digital interface.

Rapid development of mobile communications business leads to greater focus on effective mobile DRM (digital right management) for providing improved content protection. To be able to guarantee DRM policies enforcement, the trusted mobile working environment based on a tamper-resistant hardware module is needed. In this paper, firstly, a construction of the trusted mobile computing based on TPM/TPCM is introduced. Thereafter, an example of DRM authentication scheme in user domain integrated with trusted mobile platform is discussed. Based on the new characters provided by trusted computing platform, the authentication scheme can be simplified, which is safe enough to increase the security of latest mobile DRM framework and promote its interoperability and compatibility.

Digital Rights Management ecosystem is composed of various participants, which adopt different security policies to meet their own security requirements, with a goal to achieve individual optimal benefits. However, from the perspective of the whole DRM-enabling contents industry, a simple adoption of several increasingly enhanced security policies does not necessarily implement an optimal benefit balance among participants. A game-theoretic analysis of adoptions of security policies was emphasized based on a proposed General DRM value chain ecosystem without the loss of generality. First, we formalized security policies and fundamental properties that include internal relativity and external one, together with multiparty game on adoptions of security policies. Also, a cooperative game among digital Contents Provider, Rights/Service Provider and digital Devices Provider, as well as a non-cooperative game between Providers and Consumers were presented. Final, a stable core allocation of benefits and Nash Equilibriums were found out, respectively. It is clearly concluded that the cooperative game has important super-addivitity and convexity, thus simultaneous adoptions of security policies with external relativity being helpful to achieve Pareto Optimality by using a pre-established cooperative relation; and that Pareto Optimality also exists between Providers and Consumer with the increase of users' purchase transactions when both have a repeated game.