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To ensure optimal use of images while preserving privacy, it is necessary to partition the shared image into public and private areas, with public areas being openly accessible and private areas being shared in a controlled and privacy-preserving manner. Current works only facilitate image-level sharing and use common cryptographic algorithms. To ensure efficient, controlled, and privacy-preserving image sharing at the area level, this paper proposes an image partition security-sharing mechanism based on blockchain and chaotic encryption, which mainly includes a fine-grained access control method based on Attribute-Based Access Control (ABAC) and an image-specific chaotic encryption scheme. The proposed fine-grained access control method employs smart contracts based on the ABAC model to achieve automatic access control for private areas. It employs a Cuckoo filter-based transaction retrieval technique to enhance the efficiency of smart contracts in retrieving security attributes and policies on the blockchain. The proposed chaotic encryption scheme generates keys based on the private areas' security attributes, largely reducing the number of keys required. It also provides efficient encryption with vector operation acceleration. The security analysis and performance evaluation were conducted comprehensively. The results show that the proposed mechanism has lower time overhead than current works as the number of images increases.

Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes. Inspired by the dynamic defense approach proposed in cyberspace to address endless arm races, this article defines ensemble quantity, network structure, and smoothing parameters as variable ensemble attributes and proposes a stochastic ensemble strategy based on heterogeneous and redundant sub-models. The proposed method introduces the diversity and randomness characteristic of deep neural networks to alter the fixed correspondence gradient between input and output. The unpredictability and diversity of the gradients make it more difficult for attackers to directly implement white-box attacks, helping to address the extreme transferability and vulnerability of ensemble models under white-box attacks. Experimental comparison of ASR-vs.-distortion curves with different attack scenarios under CIFAR10 preliminarily demonstrates the effectiveness of the proposed method that even the highest-capacity attacker cannot easily outperform the attack success rate associated with the ensemble smoothed model, especially for untargeted attacks.

Data provenance is an effective approach for data security supervision. In the distributed, multi-user, and multi-layer big data system, only the provenance generation method, which leverages the information logged at both application and operating system level, has the capacity to completely obtain the provenance information required for data usage supervision. However, the current research on the conjoint analysis of multiple logs is inadequate, and it is difficult for them to effectively integrate the provenance information extracted from different logs, especially in the big data scenario. For the near real-time provenance generation based on the analysis of multiple heterogeneous logs, this paper employs a Hadoop-based big data system as the research object, and proposes a parallel log analysis method based on auxiliary data structures and multi-threading. For the efficient conjoint analysis of multiple logs, 5 auxiliary data structures are constructed as the medium for the correlation and fusion of log information, and a multi-threading method is presented to parallelize the lookup of provenance information. In order to cope with the complex log record generation rules, log analysis methods for nondeterministic records, non-instantaneous operations, and instantaneous batch operations are proposed to generate provenance information correctly. In addition, a provenance generation framework is established to implement the proposed log analysis method. The experimental results show that the log collection time overhead caused by processing files above MB level is less than 0.1%. The proposed method can analyze logs in near real time and generate provenance information correctly.

When users upload their private data to the cloud, they lose control of the data stored in the cloud server. If the cloud system cannot provide an effective security mechanism to protect the data, the consequent data leakage issue will hinder the development of cloud computing. Conventional access control and encryption technologies cannot effectively control the propagation of tenant private data in the system. The mandatory one-way information flow control model is limited by the complexity of the cloud environment, and it is difficult to effectively protect private data stored in the cloud. To solve the above problems, this article proposes a tenant-led ciphertext information flow control method for cloud virtual machines. Through the design of a decentralized information flow control security policy, a secret-domain key management scheme, and a multi-ID-based threshold encryption scheme, the information flow control strategies of taint infection, secret-level reduction, and ability propagation are realized in a ciphertext form, which can effectively prevent malicious users inside and outside the system from illegally reading private data. The feasibility of this method is verified by a security proof and an experiment.

Nowadays, big data has become a hot research topic. It gives fresh impetus to the economic and social development. However, the huge value of big data also makes it the focus of attacks. Big data security incidents occur frequently in recent years. The security supervision capacities for big data do not match its important role. Data provenance which describes the origins of data and the process by which it arrived the current state, is an effective approach for data supervision. For the full use of provenance in big data supervision, a provenance model which defines the concepts used to represent the provenance types and relations is required to be built in advance, but current provenance models do not adapt to big data scenarios well. In this paper, we comprehensively consider the characteristics of big data and the requirements of data security supervision, extend the widely used provenance model PROV-DM by subtyping and new relation definition, and propose a big data provenance model (BDPM) for data supervision. BDPM model supports the provenance representation of various data types and diverse data processing modes to represent the entire data transformation process through different components in the big data system, and defines new relations to enrich provenance analysis functions. Based on BDPM model, we introduce the constraints that should be satisfied in the construction of valid provenance graph and present the data security supervision methods via provenance graph analysis. Finally, we evaluated the satisfiability of BDPM model through a case study.

In the cloud environment, owing to the large-scale sharing of the upper application instance and the underlying virtual machine resources, the tenants' information flow boundary in the shared virtual machine is fuzzy and difficult to identify. In addition, protection of tenant information flow between processes is inadequate, resulting in the leakage of sensitive information of tenants. Therefore, a dynamic control method for tenants' sensitive information flow based on virtual boundary recognition is proposed. By analyzing the behavior and operation log of tenants, the behavior feature vectors of tenants are constructed, and an automatic recognition algorithm of tenant virtual boundary based on the dynamic spiking neural network is designed. This algorithm can realize dynamic identification of the tenant virtual security boundary when the application service demand changes dynamically. Further, combined with the concept of centralized and decentralized information flow control, a dynamic control method of sensitive information flow is established. The security label is formally defined by using the lattice structure, and the control rules of tenants' information flow and the rules of tenant label encryption-declassification are designed. Thus, the independent, dynamic and secure control of tenants' information flow inside and outside the tenant virtual boundary. Finally, the detailed design of a dynamic security control application system for cloud tenants' sensitive information flow is provided. Experiments confirm that the proposed algorithm can identify the security boundary of tenants more accurately and efficiently than the traditional spiking neural network classification methods. Further, the security and effectiveness of the method is verified by the intransitive noninterference theory and the experiment of information flow control.

Adversarial examples in an image classification task cause neural networks to predict incorrect class labels with high confidence. Many applications related to image classification, such as self-driving and facial recognition, have been seriously threatened by adversarial attacks. One class of the existing defense methods is the preprocessing-based defense which transforms the inputs before feeding them to the system. These methods are independent of the classification models and have excellent defensive effects under oblivious attacks. An image filtering method is often used to evaluate the robustness of adversarial examples. However, filtering induces the loss of valuable features that reduce the classification accuracy and weakens the adversarial perturbation. Furthermore, the fixed filtering parameters cannot effectively defend against the adversarial attack. This paper proposes a novel defense method based on different filter parameters and randomly rotated filtered images. The output classification probabilities are statistically averaged, which keeps the classification accuracy while removing the perturbation. Experimental results show that the proposed method improves the defense capability of various models against diverse kinds of oblivious adversarial attacks. Under the adaptive attack, the transferability of the adversarial examples among different models is significantly reduced.

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.

In the attribute-based access control (ABAC) model, attributes are the basis for controlling access to data resources. The existing attribute extraction methods that are based on manual management are time consuming and have a high cost, variable accuracy, and poor scalability when dealing with massive unstructured text from big data resources. This paper proposes a multidimensional hybrid feature generation method for text resource attributes. The method comprehensively calculates the characteristics of attributes themselves, the relationships between attributes, and the relationship between attributes and resources. It can fully and accurately characterize the attributes. It converts attribute features into grayscale images in order to translate attribute mining problems into image recognition problems. We propose an attribute mining method based on a convolutional neural network (CNN). We use neural networks to automatically correlate features. It means there is no need to manually consider the importance of features and their relationships. This avoids the need for security experts to manually label the attributes of massive resources and facilitates the automatic and intelligent mining of ABAC resource attributes. The experimental results show that compared with the benchmark algorithm, the proposed method has improved accuracy and recall rate and can provide attribute support for ABAC of big data resources.

Security issues of large-scale local area network are becoming more prominent and the anomaly detection for the network traffic is the key means to solve this problem. On the other hand, it is a challenge to extract effective and accurate traffic features for anomaly detection. In order to resolve this challenge, multi-types of network flow features are designed and analyzed in the present study. These features include sequence packet features, general statistical features and environmental features, which can profile the characteristics of network flows accurately. Moreover, a method based on the hybrid neural network is proposed to detect anomaly by analyzing these features. One-dimensional convolutional network is implemented to analyze the sequence features in the hybrid neural network, while deep neural networks are utilized to learn the characteristics of high-dimension feature vectors including general statistical features and environmental features. The method can make comprehensive analysis for network anomaly detection. Two datasets of ISCX-IDS-2012 and CIC-IDS-2017 are carried out to evaluate the performance of the proposed method and other similar algorithms. The present study shows that the comprehensive performances of the proposed method are better than that for others algorithms. It is concluded that the proposed method can be applied for the anomaly detection applications with reasonable performance.

Reconfigurable security protocols, with dynamic protocol configuration and flexible resource allocation, have become a state-of-the-art technology to guarantee the security of space-ground integrated network. However, reconfiguration decision-making for reconfigurable security protocols remains a major challenge in order to adapt to diverse secure service requirements and deploy higher security level but more complicated security strategies in nodes with limited resources and computing abilities. To handle this problem commendably, a hierarchically collaborative ant colony–based reconfiguration decision-making model called HiCoACR is proposed. This model, inspired by the ideas of hierarchical reinforcement learning and population collaboration, decomposes the reconfiguration decision-making problem into two sub-problems by introducing a two-level hierarchy ant colony consisting of the Explorer and the Worker. The Explorer controls directions of protocol reconfiguration and generates abstract scheduling sub-goals which are conveyed from the Worker. While the Worker schedules most suitable cryptogram resources for each sub-goal received and produces the optimal reconfiguration solution which is verified and re-optimized by a Lévy process–based stochastic gradient descent algorithm. Both the Explorer and the Worker adopt a modified version of ant colony algorithm to fulfill its targets, where a hierarchical pheromone is defined to reinforce positive behaviors of each ant colony. Experiment results suggest that HiCoACR outperforms baseline algorithms and possesses well model transferability.

Decentralizing multi-authority attribute-based encryption (ABE) has been adopted for solving problems arising from sharing confidential corporate data in cloud computing. For decentralizing multiauthority ABE systems that do not rely on a central authority, collusion resistance can be achieved using a global identifier. Therefore, identity needs to be managed globally, which results in the crucial problems of privacy and security. A scheme is developed that does not use a central authority to manage users and keys, and only simple trust relations need to be formed by sharing the public key between each attribute authority (AA). User identities are unique by combining a user's identity with the identity of the AA where the user is located. Once a key request needs to be made to an authority outside the domain, the request needs to be performed by the authority in the current domain rather than by the users, so, user identities remain private to the AA outside the domain, which will enhance privacy and security. In addition, the key issuing protocol between AA is simple as the result of the trust relationship of AA. Moreover, extensibility for authorities is also supported by the scheme presented in this paper. The scheme is based on composite order bilinear groups. A proof of security is presented that uses the dual system encryption methodology.

Space–ground integrated network, a strategic, driving, and irreplaceable infrastructure, guarantees the development of economic and national security. However, its natures of limited resources, frequent handovers, and intermittently connected links significantly reduce the quality of service. To address this issue, a quality-of-service-aware dynamic evolution model is proposed based on complex network theory. On one hand, a quality-of-service-aware strategy is adopted in the model. During evolution phases of growth and handovers, links are established or deleted according to the quality-of-service-aware preferential attachment following the rule of better quality of service getting richer and worse quality of service getting poor or to die. On the other hand, dynamic handover of nodes and intermittent connection of links are taken into account and introduced into the model. Meanwhile, node heterogeneity is analyzed and heterogeneous nodes are endowed with discriminate interactions. Theoretical analysis and simulations are utilized to explore the degree distribution and its characteristics. Results reveal that this model is a scale-free model with drift power-law distribution, fat-tail and small-world effect, and drift character of degree distribution results from dynamic handover. Furthermore, this model exerts well fault tolerance and attack resistance compared to signal-strength-based strategy. In addition, node heterogeneity and quality-of-service-aware strategy improve the attack resistance and overall quality of service of space–ground integrated network.

Access control has always been one of the effective methods to protect data security. However, in new computing environments such as big data, data resources have the characteristics of distributed cross-domain sharing, massive and dynamic. Traditional access control mechanisms are difficult to meet the security needs. This paper proposes CACM-MMSR to solve distributed cross-domain access control problem for massive resources. The method uses blockchain and smart contracts as a link between different security domains. A permission decision model migration method based on access control logs is designed. It can realize the migration of historical policy to solve the problems of access control heterogeneity among different security domains and the updating of the old and new policies in the same security domain. Meanwhile, a semantic reasoning-based permission decision method for unstructured text data is designed. It can achieve a flexible permission decision by similarity thresholding. Experimental results show that the proposed method can reduce the decision time cost of distributed access control to less than 28.7% of a single node. The permission decision model migration method has a high decision accuracy of 97.4%. The semantic reasoning-based permission decision method is optimal to other reference methods in vectorization and index time cost. [ABSTRACT FROM AUTHOR]

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy management efficiency and difficulty in accurately describing the access control policy. To overcome these problems, this paper proposes a big data access control mechanism based on a two-layer permission decision structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are introduced in the ABAC model as business constraints between entities. The proposed mechanism implements a two-layer permission decision structure composed of the inherent attributes of access control entities and the business attributes, which constitute the general permission decision algorithm based on logical calculation and the business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neural network, respectively. The general permission decision algorithm is used to implement accurate policy decisions, while the business permission decision algorithm implements fuzzy decisions based on the business constraints. The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent, adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure, the complex and diverse big data access control management requirements can be satisfied by considering the security and availability of resources. Experimental results show that the proposed mechanism is effective and reliable. In summary, it can efficiently support the secure sharing of big data resources. [ABSTRACT FROM AUTHOR]

Deep learning-based image processing algorithms have developed rapidly in the past decade and have shown significant improvements to extract image features when both sufficient computing power and big data are accessible. Thus, rapid advances in applications such as facial recognition and autonomous driving have been one of the implementation areas. On the other hand, edges as a low-level prevalence feature in images with independent semantics are practically adapted to attain better outcomes. However, neural network-based image feature extraction focusing on texture rather than shape leads to insufficient accuracy. To address this issue, an edge feature extraction method utilizing both conventional operators such as HDE and Sobel and a deep learning-based method is proposed to classify and retrieve images with better accuracy outcomes. By doing so, a large amount of data needed to conduct deep learning-based methods is decreased, the transferability of the model is achieved, classification and retrieval accuracies are enhanced, and the data is compressed. All these better results are attained with benchmark data sets. As a result, all these are achieved by proposing a novel method. [ABSTRACT FROM AUTHOR]

Doping inorganic acid ions represents a promising pathway to improving the photocatalytic activity of TiO2, and oxygen vacancy has been regarded as the determinant factor for photocatalytic activity. A series of samples doped with Cl−, NO3−, and SO42− was prepared via a simple sol–gel method. Two different oxygen vacancies in the crystal layer of NO3−/TiO2 and Cl−/TiO2 were found, and those are [Ti3+]-V0-[Ti3+] and [Ti3+]-Cl, respectively. The photocurrent of NO3−/TiO2 with [Ti3+]-V0-[Ti3+] is significantly greater than that of Cl−/TiO2 with [Ti3+]-Cl. The least oxygen vacancy is in the gel layer of SO42−/TiO2, and the negligible photocurrent is due to difficulty in forming a stable sol. Furthermore, the process conditions for the application of TiO2 were investigated in this work. The optimal process parameters are to adjust the solution to pH = 3 during sol–gel preparation, to adopt 550 °C as the calcination temperature, and to use an alkaline electrolyte, while the rest of the preparation conditions remain unchanged. This work reveals a new avenue for designing efficient photocatalysts for air pollutant degradation.

Abstract Background According to research, the fatty liver index (FLI) is associated with diabetes. However, few studies have been conducted to investigate the relationship between FLI and diabetes risk from various perspectives. This study comprehensively investigated the relationship between FLI and incident diabetes in a large Japanese population. Methods This retrospective cohort study included 14,280 participants from Murakami Memorial Hospital in Japan from 2004 to 2015. The independent and dependent variables are FLI and risk of type 2 diabetes mellitus (T2DM), respectively. To examine the link between FLI and incident T2DM, Cox proportional-hazards regression was employed. In addition, we performed a number of sensitivity studies to guarantee the validity of the results. Moreover, we conducted subgroup analyses. Results After adjusting covariates, the results showed that FLI was positively associated with the risk of T2DM (HR = 1.019, 95%CI: 1.012, 1.025). Additionally, the sensitivity analysis showed how reliable the outcomes were. And a stronger association between FLI and incident T2DM was observed in the regular exercisers (HR = 1.036, 95%CI: 1.019–1.053, P

Androgen receptor splice variant 7 (AR-v7), a constitutively active transcription factor, plays a crucial role in the progression of castration-resistant prostate cancer (CRPC). Here, we found that the cleavage and polyadenylation specificity factor 1 (CPSF1) (the largest subunit of the multi-protein cleavage and polyadenylation specificity complex), regulated by the E3 ubiquitin ligases SIAH1, promoted AR-v7 expression. The data from microarray-based analysis and clinical specimen-based analysis showed that SIAH1 expression was decreased in PCa and was negatively correlated with aggressive phenotypes of PCa. SIAH1 repressed PCa cell growth and invasion under castrate conditions. SIAH1 directly interacted with CPSF1 and promoted ubiquitination and degradation of CPSF1. CPSF1 expression was negatively correlated with SIAH1 expression, but positively with PCa progression. CPSF1 overexpression switched the AR splicing pattern and facilitated the generation of the oncogenic isoform (AR-v7) by binding to the AAUAAA polyadenylation signal contained in AR-cryptic exon CE3. Functionally, SIAH1 acted as a tumor suppressor in PCa pathogenesis by repressing CPSF1-mediated AR-v7 generation. Finally, we demonstrated that m6A methylation was concerned with the repression of SIAH1 in PCa. Our results define SIAH1/CPSF1/AR-v7 axis as a regulatory factor of PCa progression, providing a promising target for treating PCa.

IntroductionFermented egg-milk peptides (FEMPs) could enhance the colon-intestinal barrier and upgrade the expression of zonula occludens-1 and mucin 2. Besides, the underlying biological mechanism and the targets FEMPs could regulate were analyzed in our study.MethodsHerein, the immunofluorescence technique and western blot were utilized to evaluate the repair of the intestinal barrier. Network pharmacology analysis and bioinformatics methods were performed to investigate the targets and pathways affected by FEMPs.Results and discussionAnimal experiments showed that FEMPs could restore intestinal damage and enhance the expression of two key proteins. The pharmacological results revealed that FEMPs could regulate targets related to kinase activity, such as AKT, CASP, RAF, and GSK. The above targets could interact with each other. GO analysis indicated that the targets regulated by FEMPs could participate in the kinase activity of the metabolic process. KEGG enrichment revealed that the core targets were enriched in pathways related to cell apoptosis and other important procedures. Molecular docking demonstrated that FEMPs could bind to the key target AKT via hydrogen bond interactions. Our study combined the experiment in vivo with the method in silico and investigated the interaction between peptides and targets in a pattern of multi-targets and multi-pathways, which offered a new perspective on the functional validation and potential application of bioactive peptides.