With the increasing global communication demands and the development of Internet of Things (IoT), extending the connectivity to rural and remote areas has become imperative for future networks. The sixth-generation (6G) network is expected to provide heterogeneous services and seamless network coverage for everyone and everything. Combining the advantages of both satellite and terrestrial networks, the integrated satellite-terrestrial network architecture is promising to provide global broadband access for all types of users, which has drawn much attention from both the academia and industry. In this article, we present a comprehensive survey of the state-of-the-art of integrated satellite-terrestrial networks toward 6G. First, an executive classification and summary of the integration architecture is presented from network design to performance optimization. Then, typical applications of the integrated satellite-terrestrial network are discussed based on the architecture. By considering the unique characteristics of the two networks, main challenges are pointed out when performing integration, such as the long propagation delay, complex link conditions, and high dynamics of the network topology. Finally, some promising future techniques are explored from the perspective of the integrated architecture. A detailed survey of the potential integration architectures is of great importance to enable more flexible network design and construction in future 6G networks. This article will provide a valuable guideline on future research and development of integrated satellite-terrestrial networks. 

Integrated Satellite-Terrestrial Networks Toward 6G: Architectures, Applications, and Challenges

Cyber-attacks cyber–physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks. Machine learning algorithms have been proposed for hindering cyber-attacks on CPSs, but the absence of labeled data from novel attacks makes their detection quite challenging. In this context, generative adversarial networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system. However, the detection of cyber-attacks on CPSs has strict latency requirements, since the attacks need to be stopped before the system is compromised. In this article, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs. The IDS is proposed for a fog architecture, which brings computation resources closer to the end nodes and thus contributes to meeting low-latency requirements. In order to achieve higher detection rates, the proposed architecture computes a reconstruction loss based on the reconstruction of data samples mapped to the latent space. Other works that follow a similar approach struggle with the time required to compute the reconstruction loss, which renders them impractical for latency constrained applications. We address this problem by training an encoder that accelerates the reconstruction loss computation. Experiments show that the proposed solution achieves higher detection rates and is at least 5.5 times faster than a baseline approach in the three studied data sets.

Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment

Cloud computing has become increasingly popular for data owners to outsourcing their data in public cloud and also permits other users to fetch this data With the advantage of flexibility and economic savings motivates both individuals and organizations to outsource their private complex data management system into the cloud However, in reality cloud does not support only one or two user instead they supports millions of users and hence privacy issues of data is incurred We are analyzing the schemes to deal with Privacy preserving Ranked Multi-keyword Search in a Multi-owner model (PRMSM) According to our analysis this scheme perform secure search without knowing the actual data of both keywords and trapdoors For that we are going to develop secure search protocol In this paper we are proposing a novel Additive Order and Privacy Preserving Function family to protect the legal data from the attackers Furthermore, our proposed PRMSM supports efficient data user revocation

/pdf/privacy-preserving-ranked-multi-keyword-search-for-multiple-511c8pupma.pdf

Privacy Preserving Ranked Multi-Keyword Search for Multiple Data Owners in Cloud Computing

Attribute-based keyword search (ABKS) has a broad developing prospect in providing search service for users and realizing fine-grained access control over ciphertext in the background of cloud computing. However, two open problems prevent further development and application of ABKS. Firstly, most of ABKS schemes suffer from inside keyword guessing attack (KGA) inherently, which is a serious security threat. Secondly, the existing ABKS schemes only focus on single or conjunctive keyword search, these inflexible retrieval modes may lead to efficiency loss caused by inaccurate positioning of user's interest and greatly reduce user search experience. In this paper, we introduce a semi-trusted server and build a dual server model. Based on the dual server model and our proposed techniques, we are the first to put forward an attribute-based multi-keyword ranked search scheme against inside keyword guessing attack (ABKRS-KGA) to solve the mentioned two problems simultaneously. We provide strict security definitions for two types of adversaries and we are the first to prove that the construction is adaptively secure against both chosen-keyword attack (CKA) and KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the simulation results show that the efficiency of our scheme is acceptable.

Practical Attribute-based Multi-Keyword Ranked Search Scheme in Cloud Computing

With the significant rise in the demand for infinite storage resource and high-quality retrieval service, cloud computing is deemed to be a highly remarkable technology. For privacy concerns, several research works on ranked multi-keyword search over the encrypted cloud data under multiple data owner model have been developed. However, most of these schemes are vulnerable to keyword guessing attacks and equivalence test attack. Besides, they cannot accurately return the top - $k$ ranked search results from the encrypted data of different data owners to the data users. Obviously, these drawbacks may incur leakage of privacy-sensitive keyword information and the inaccuracy of returned search results. Therefore, in this article, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, which empowers the cloud server to perform multi-keyword search over the cloud data and then, return the top - $k$ ranked search results to data users without leaking any keyword and trapdoor information. Additionally, we show through rigorous security analysis that our scheme is secure against the attacks launched by inside attackers and outside attackers. Finally, the performance evaluation indicates that our scheme has more satisfactory features than the existing ranked multi-keyword search schemes.

Efficient Ranked Multi-Keyword Retrieval With Privacy Protection for Multiple Data Owners in Cloud Computing

Due to a variety of advantages of data outsourcing, some Location Based Services (LBS) providers are motivated to outsource the geographic data and query service to commercial cloud. However, for protecting data confidentiality, the valuable data should be encrypted before outsourcing, which obstructs the utilization like geographic information query. To address this problem, some previous works regarding to secure search on encrypted database could be applied in outsourced LBS scenario directly, but none of them is tailor-made for linear region search (LRS). The LRS is a kind of LBS that widely used in navigation system, it finds the nearby points of interest (POI) for a query segment. In this paper, for the first time, we explore and solve the challenging problem of privacy-preserving linear region search. Specifically, we choose the quadtree structure to build index for POI database, then the results of LRS can be efficiently obtained by finding out the rectangular regions that query segment passes through. In order to preserve the privacy of both LBS providers and users, according to computational geometry and Asymmetric Scalar-product Preserving Encryption (ASPE) approach, we design a novel algorithm for accurately determining whether a segment intersects with a rectangle on ciphertext. Moreover, this algorithm also provides a new idea to solve other computational problems in encrypted 2-dimensional geometry space. Based on different privacy requirements of two threat models, we propose two privacy-preserving LRS schemes and corresponding dynamic update operations. Security analysis and experiments on real-world dataset show that our schemes are secure and efficient.

Privacy-Preserving Linear Region Search Service

With the rapid development of the Internet of things (IoT), more and more IoT devices are connected and communicate frequently. In this background, the traditional centralized security architecture of IoT will be limited in terms of data storage space, data reliability, scalability, operating costs and liability judgment. In this paper, we propose an new key information storage framework based on a small distributed database generated by blockchain technology and cloud storage. Specifically, all encrypted key communication data will be upload to public could server for enough storage, but the abstracts of these data (called "communication logs") will be recorded in "IoT ledger" (i.e., an distributed database) that maintained by all IoT devices according to the blockchain generation approach, which could solve the problem of data reliability, scalability and liability judgment. Besides, in order to efficiently search communication logs and not reveal any sensitive information of communication data, we design the secure search scheme for our "IoT ledger", which exploits the Asymmetric Scalar-product Preserving Encryption (ASPE) approach to guarantee the data security, and exploits the 2-layers index which is tailor-made for blockchain database to improve the search efficiency. Security analysis and experiments on synthetic dataset show that our schemes are secure and efficient.

Secure and Efficiently Searchable IoT Communication Data Management Model: Using Blockchain as a new tool

The recent outbreak of industrial cyberattacks indicates that the current industrial network security architecture is under serious challenges. As one of the critical industrial networks, the heterogeneous and real-time substation network lacks compatibility with the conventional cryptography architecture represented by secure sockets layer/transport layer security (SSL/TLS) and public key infrastructure (PKI). To enhance the security of smart substations under the premise of low latency, in this article, we present a novel encryption-as-a-service architecture based on fog computing in this article. The architecture offloads encryption to dedicated devices and makes certificate and key management available through unified web services on the fog and cloud layers. Based on this architecture, we propose MX-SORTS, maximizing security on real-time communication of different services, an algorithm for adaptive configuration of encrypting and signing substation network traffic. By the contrast experiments with the conventional cryptography architecture, we prove that the encryption-as-a-service architecture can significantly improve the real-time and security performance of substation networks.

An Adaptive Encryption-as-a-Service Architecture Based on Fog Computing for Real-Time Substation Communications

Secure multi-keyword ranked search over encrypted cloud data for multiple data owners

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Privacy-Preserving Linear Region Search Service

Secure and Efficiently Searchable IoT Communication Data Management Model: Using Blockchain as a new tool

An Adaptive Encryption-as-a-Service Architecture Based on Fog Computing for Real-Time Substation Communications

Secure multi-keyword ranked search over encrypted cloud data for multiple data owners