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Database security is pertinent to every organisation with the onset of increased traffic over large networks especially the internet and increase in usage of cloud based transactions and interactions. Greater exposure of organisations to the cloud implies greater risks for the organisational as well as user data. In this paper, we propose a novel approach towards database intrusion detection systems (DIDS) based on Expectation maximization Clustering and Sequential Pattern Mining (EMSPM). This approach unlike any other does not have records and assumes a predetermined policy to be maintained in an organisational database and can operate seamlessly on databases that follow Role Based Access Control as well as on those which do not conform to any such access control and restrictions. This is achieved by focusing on pre-existing logs for the database and using the Expectation maximization clustering algorithm to allot role profiles according to the database user's activities. These clusters and patterns are then processed into an algorithm that prevents generation of unwanted rules followed by prevention of malicious transactions. Assessment into the accuracy of EMSPM over sets of synthetically generated transactions yielded propitious results with accuracies over 93%. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IoT node, this paper proposes an integrity protection method for trusted data of IoT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IoT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IoT nodes. The data of the IoT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IoT nodes, and embedded processor is added to the IoT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IoT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IoT nodes is about 3 s, and the work efficiency is high. [ABSTRACT FROM AUTHOR]

Nowadays, the application of a UAV swarm is becoming more and more widespread in the military field, and more and more attention is paid to the security of mission resource allocation. However, the relay node forwarding in the wireless transmission process brings greater risks to data leakage, and the computing power and energy of the UAV consumption is limited, so a lighter solution is required. This paper proposes a mechanism for the safe allocation of UAV swarm mission resources based on random labels. Each task has a random label to solve the problem of database security and wireless transmission security in the process of UAV task assignment. Furthermore, a lightweight stream cipher encryption scheme is illustrated to ensure the security of the UAV database. The irreversible hash function SHA-256 and the lightweight foam structure hash function SPONGENT-128 are used to generate random labels and then allocate task resources. In the case of energy consumption, it reduces the possibility of the enemy successfully obtaining private data. The simulation results show that the scheme has good performance in terms of security and has better performance than existing methods in terms of throughput and delay, without increasing too much energy consumption. [ABSTRACT FROM AUTHOR]

Currently, data production is as quick as possible; however, databases are collections of well-organized data that can be accessed, maintained, and updated quickly. Database systems are critical to your company because they convey data about sales transactions, product inventories, customer profiles, and marketing activities. To accomplish data manipulation and maintenance activities the Database Management System considered. Databases differ because their conclusions based on countless rules about what an invulnerable database constitutes. As a result, database protection seekers encounter difficulties in terms of a fantastic figure selection to maintain their database security. The main goal of this study is to identify the risk and how we can secure databases, encrypt sensitive data, modify system databases, and update database systems, as well as to evaluate some of the methods to handle these problems in security databases. However, because information plays such an important role in any organization, understanding the security risk and preventing it from occurring in any database system require a high level of knowledge. As a result, through this paper, all necessary information for any organization has been explained; in addition, also a new technological tool that plays an essential role in database security was discussed. [ABSTRACT FROM AUTHOR]

Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information, but also the expansion of areas and assets to be protected. In terms of information security, it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets. Also, it has caused difficulties in managing those issues due to reasons such as mutual interference, countless security events and logs' data, etc. Within this security environment, an organization should identify and classify assets based on the value of data and their security perspective, and then apply appropriate protection measures according to the assets' security classification for effective security management. But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets. In this paper, we propose an information classification management service based on blockchain, which presents and uses a model of the value of data and the security perspective. It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain. The proposed service reduces assets to be protected and security solutions to be applied, and provides security measures at the platform level rather than individual security solutions, by using blockchain. In the rapidly changing security environment of Industry 4.0, this proposed service enables economic security, provides a new integrated security platform, and demonstrates service value. [ABSTRACT FROM AUTHOR]

• A watermarking framework called SE-NDEND is proposed for Internet of Medical Things. • Neural network simulates various chaotic systems for encryption to enhance security. • The more comprehensive noise layer is designed to improve robustness. • Moiré distortion designed enhances watermark against screen-shooting attacks. The development of the Internet of Medical Things heavily relies on big data, and data security based on medical images has become a growing concern in society. Digital watermarking serves as a crucial technique for protecting and tracing medical image data copyright, as well as enabling forensic analysis. However, existing deep watermarking methods often neglect the protection of watermarks after extraction, leading to potential copyright disputes. To address this issue, this paper proposes SE-NDEND, a novel symmetric watermarking framework with neural network-based chaotic encryption for the Internet of Medical Things that significantly enhances the effectiveness and security of watermarking while maintaining robustness. Specifically, the SE-NDEND leverages neural networks to simulate chaotic systems and generate chaotic sequences, mitigating the complexity and high cost of implementing chaotic systems using hardware circuits. Moreover, we introduce a new noise layer with Moiré distortion that interacts with the decoder, forming a symmetric network structure that bolsters the robustness of watermarking. Parameters are jointly trained and shared during the training process to counteract potential interference from the noise layer. Experimental results validate the effectiveness of SE-NDEND in enhancing copyright protection, traceability, and forensic capabilities, surpassing existing deep learning methods in terms of visual quality (with PSNR of 45.8492 dB and SSIM of 0.9874), security, and robustness. The proposed framework can find application in protecting medical image data in the Internet of Medical Things. [ABSTRACT FROM AUTHOR]

The access to data in the databases of an organization depends on different levels of specific information security. If the security level is too high, it can cause delays or blockages in the implementation of priority tasks. The study tracked the flexibility of IT security related to the access to a network and how it improves the time/cost ratio in the targeted departments. The findings suggest that there is a strong link between the level of security concerning the access to information in databases and the performance of the employees. IT changes have saved time and reduced costs, increased productivity and performance and also technological and managerial flexibility.

With the development of science and technology, more and more operations are performed in the cardiac catheterization laboratory. During such operations, a lot of relevant imaging data need to be retained. These imaging data can be used for clinical and scientific research and teaching applications, but imaging data security has also become an increasingly important issue. This article is based on the Internet of Things cardiac catheterization laboratory information management system image data security mechanism system research. First of all, this article adopts the literature method to study the application research of the Internet of Things technology in the medical field, as well as the relevant medical imaging data security technology methods. Then, the medical image data security mechanism was designed, and the image data security model of the cardiac catheterization laboratory information management system based on the Internet of Things was established. Finally, the application of decentralized management of the Internet of Things RFID technology on medical equipment and the security of the application of this technology on medical imaging data are analyzed, and finally a conclusion is drawn. The image data security mechanism established in this article is based on the Internet of Things technology. The security rate of image information data reaches more than 95%, the information data security level reaches level 1, and the average data missing rate is only 4.7%. It is a brand-new breakthrough, hoping to further improve the efficiency of hospital information management and protect the safety of medical information. [ABSTRACT FROM AUTHOR]

Clients of outsourced databases need Query Authentication (QA) guaranteeing the integrity and authenticity of query results returned by potentially compromised providers. Prior work provides QA assurances for a limited class of queries by deploying several software-based cryptographic constructs. The constructs are often designed assuming read-only or infrequently updated databases. For dynamic datasets, the data owner is required to perform all updates on behalf of clients. Hence, for concurrent updates by multiple clients, such as for OLTP workloads, existing QA solutions are inefficient. We present ConcurDB, a concurrent QA scheme that enables simultaneous updates by multiple clients. To realize concurrent QA, we have designed several new mechanisms. First, we identify and use an important relationship between QA and memory checking to decouple query execution and verification. We allow clients to execute transactions concurrently and perform verifications in parallel using an offline memory checking based protocol. Then, to extend QA to a multi-client scenario, we design new protocols that enable clients to securely exchange a small set of authentication data even when using the untrusted provider as a communication hub. Finally, we overcome provider-side replay attacks. Using ConcurDB, we provide and evaluate concurrent QA for the full TPC-C benchmark. For updates, ConcurDB shows a 4x performance increase over existing solutions. [ABSTRACT FROM AUTHOR]

Biometric recognition approaches have overwhelmed major issues in traditional authentication systems. Privacy invasion and security are the needed features in the effective development of biometric recognition systems. Recently some biometric techniques have been suffered to hacking trails also. Hence there is a despairing need to propose a novel cancelable iris recognition scheme. The prime objective of this research is to create a transformed cancelable biometric template through an encryption function and a one-way transformation function. The random projection matrix is employed here to generate the first feature vector. Then an encrypted cancelable iris code is generated using Double Random Phase Encryption (DRPE) in the Fractional Fourier Transform (FFT) domain. The proposed framework utilizes both the left and right iris image of a person and generates a single cancelable iris template. This feature guarantees the privacy-preserving cancelable iris code generation for secure authentication. The experiment is carried on two state-of-art datasets CASIA Iris V4 and IITD iris to affirm the efficacy of the proposed methodology. The result analysis witnessed a promising accuracy of 99.59%, a recognition rate of 99.88% with a lower Equal Error Rate (ERR) of 0.46%. It has also been proved that the proposed approach is computationally efficient as it recognizes the iris code with less recognition time of 7 ms with a maximum true positive and true negative rate of 100%. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are naturally resource-constrained and have been deployed in various applications such as home monitoring, health care monitoring and military systems. These applications are connected to numerous devices capable of interacting with each other over the internet called as Internet of Things (IoT). WSN application plays a main part in the IoT environment. In WSN, sensors are randomly positioned in harsh surroundings where the communication network is facing privacy issues which is the most critical for data transmission. In this paper, we proposed a cryptographic based clustering structure for preserving data privacy using Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) that improves data privacy and energy efficient routing for the heterogeneous network which uses both clustering and multi-hop communication to reduce the energy consumption of sensor node and increases the lifetime of WSN. Based on the area, the source nodes are grouped to form a cluster in the random network. This scheme also provides high data privacy using Elliptic Curve Integrated Encryption-Key Provisioning Method (ECIES-KPM) along with small key size. Besides, data security based attacks are restricted by ECIES-KPM verification process. In this way, the sensitive data is confidentiality secured with low computational overhead. Experimental comparison shows that the evaluation of OP-MDCRP approach provides more data security than existing methods in terms of computational time, packet delivery ratio, energy consumption, average delay, network overhead and network lifetime. [ABSTRACT FROM AUTHOR]

In this paper, we consider the SQL Selection-GroupBy-Aggregation (SGA) query evaluation on an untrusted MapReduce system in which mappers and reducers may return incorrect results. We design CorrectMR, a system that supports efficient verification of result correctness for both intermediate and final results of SGA queries. CorrectMR includes the design of Pedersen Merkle R-tree (PMR-tree), a new authenticated data structure (ADS). To enable efficient verification, CorrectMR includes a distributed ADS construction mechanism that allows mappers/reducers to construct PMR-trees in parallel without a centralized party. CorrectMR provides the following verification functionality: (1) correctness verification of PMR-trees by replication; (2) correctness verification of intermediate (final, resp.) query results by constructing local (global, resp.) PMR-trees and verification objects. Our experimental results demonstrate the efficiency and effectiveness of CorrectMR. [ABSTRACT FROM AUTHOR]

In recent years, more users tend to use data mining as a service (DMaaS) provided by cloud service providers. However, while enjoying the convenient pay-per-use mode and powerful capacity of cloud computing, users are also threatened by the potential risk of privacy leakage. In this paper, we aim to efficiently perform privacy-preserving DMaaS, and focus on frequent itemset mining over encrypted database in outsourced cloud environment. Existing work apply different encryption methods to design various privacy-preserving mining solutions. Nevertheless, these approaches either cannot provide sufficient security requirements, or introduce heavy computation costs. Some of them also need users staying on-line to execute computations, which are not practical in real-world applications. In this paper, we propose a novel efficient privacy-preserving frequent itemset query (PPFIQ) scheme using two homomorphic encryptions and ciphertext packing technique. The proposed scheme protects transaction database with semantic security, preserves mining privacy and resists frequency analysis attacks. Meanwhile, efficiency is guaranteed by inherent parallel computations for packed plaintexts and users could stay off-line during the mining process. We provide formal security analysis and evaluate the performance of our scheme with extensive experiments. The experiment results demonstrate that the proposed scheme can be efficiently implemented on large databases. [ABSTRACT FROM AUTHOR]

Throughout engineering construction, large deformation disasters in soft rock tunnels are encountered increasingly frequently. Therefore, structural health monitoring not only ensures accurate construction management but also provides a basis for dynamic adjustment of the support structure. The existing monitoring technology has certain shortcomings, such as poor anti-interference ability, non-real-time operation, and great security risks. Consequently, high-precision real-time monitoring has become a key scientific issue in tunnel engineering. For this work, multisource information fusion technology was adopted, while data security reserve systems, such as cloud server (ECS) based on the fiber Bragg grating multisource sensing system, cloud database (RDS), and cloud website, were embedded into the No. 2 inclined shaft of the Muzhailing tunnel. Based on the negative Poisson's ratio (NPR) anchor cable control technology for large deformation of the soft rock in the No. 2 inclined shaft of the Muzhailing tunnel, reasonable and effective intelligent monitoring was carried out for tunnel construction. Monitoring and early warning cloud service platforms, based on the Internet of Things and cloud technology, could quickly produce query and statistic tunnel monitoring information. The monitoring system provided the collection, transmission, storage, processing, and early warning information sending of data, such as NPR anchor cable axial force, steel arch stress, deep surrounding rock displacement, surrounding rock deformation, and contact pressure between primary support with secondary lining. This monitoring system ensured construction safety and provided monitoring application case support for the related problems of similar projects. [ABSTRACT FROM AUTHOR]

Approaches developed based on the blockchain concept can provides a framework for the realization of open science. The traditional centralized way of data collection and curation is a labor-intensive work that is often not updated. The fundamental contribution of developing blockchain format of microbial databases includes: 1. Scavenging the sparse data from different strain database; 2. Tracing a specific thread of access for the purpose of evaluation or even the forensic; 3. Mapping the microbial species diversity; 4. Enrichment of the taxonomic database with the biotechnological applications of the strains and 5. Data sharing with the transparent way of precedent recognition. The plausible applications of constructing microbial databases using blockchain technology is proposed in this paper. Nevertheless, the current challenges and constraints in the development of microbial databases using the blockchain module are discussed in this paper. [ABSTRACT FROM AUTHOR]

The quantum privacy query protocol (QPQ) is faced with low query efficiency, transmission channel noise, and low transmission efficiency problems. The quantum-privacy query protocol proposed by us aims to achieve a better quantum-privacy block query effect and has the function of anti-rotation noise. To solve the collective rotation noise in the transmission channel, we propose a one-way QPQ protocol based on four particles. The four-particle states { ϕ 0 , ϕ 1 , ϕ 0 ′ , ϕ 1 ′ } can be entangled by two Bell states { Φ + , Ψ - } with anti-noise properties. In general, the unidirectional quantum privacy query has more advantages than a bidirectional quantum privacy query. Besides, the post-processing scheme Yang et al. (Quantum Inf Process 15(9):38333840, 2016) can not only protect the parity result of the random key but also guarantee the security of the protocol. In the security analysis, we prove the security of the user and database. [ABSTRACT FROM AUTHOR]

A method of classifying network security data based on multi-featured extraction is proposed to address instability of a nonlinear time series in a network security threat. Cybersecurity information is divided in line with the principle of acquiring multiple attributes. On this basis, an adaptive adaptation estimation technology is optimized in analogue. With the proposed method, a cybersecurity information classification system is constructed according to the phase interval reconstruction principle so that a dynamic and autonomous adaptation estimation of the cybersecurity threat can be completed to ensure the feasibility of cybersecurity information classification. The experimental result proves that the cybersecurity information classification technology based on multi-attribute extraction can effectively guide chaos into adjacent orbits and reasonably control the training scale. Moreover, the accuracy of the estimation is guaranteed and the cybersecurity threat is estimated because of its high-speed convergence and strong proximity. Therefore, the proposed classification technology can assist professionals and backstage managers in guaranteeing security by facilitating receipt of information in a timely manner. [ABSTRACT FROM AUTHOR]

Most of data over the Internet today is hosted on outsourced third-party servers which are not trusted. Sometimes data is to be distributed to other organizations or individuals for pre-agreed use. In both of these scenarios data is susceptible to malicious tampering so there is a need for some mechanism to verify database integrity. Moreover, the authentication process should be able to differentiate between valid updates and malicious modifications. In this paper, we present a novel semi-fragile watermarking scheme for relational database integrity verification. Besides detection and localization of database tampering, the proposed scheme allows modifications to the data that need periodic updates, without requiring re-watermarking. Watermark embedding is distortion free, as it is done by adjusting the text case of selected data values resulting in retention of semantic meaning of data. Additionally, group-based embedding ensures the localization of tampering up to group level. We implemented a proof of concept application of our watermarking technique. Theoretical analysis and experiments show that even a single value modification can be detected with very high probability besides detection of attacks like tuple insertion and tuple deletion. [ABSTRACT FROM AUTHOR]

Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and Map Reduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namely logistic regression is applied to calculate attack's conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing, and analysis, such that the data may be vulnerable to tampering or contamination by attackers. in this article, we propose a blockchain-based data security scheme for Ai applications in 6G networks. Specifically, we first introduce the 6G architecture (i.e., a space-air-ground-underwater integrated network). Then we discuss two Ai-enabled applications, indoor positioning and autonomous vehicle, in the context of 6G. Through a case study of an indoor navigation system, we demonstrate the effectiveness of blockchain in data security. The integration of Ai and blockchain is developed to evaluate and optimize the quality of intelligent service. Finally, we discuss several open issues about data security in the upcoming 6G networks. [ABSTRACT FROM AUTHOR]

In the present era, secure data storage for any Internet of Things (IoT) platform is plagued by poor performance of secure read and write operations, which limits the use of data storage security on any IoT platform. Therefore, in this paper, a data storage security method based on double secret key encryption and Hadoop suitable for any IoT platform is proposed. First, the Hadoop deep learning architecture and implementation process are analyzed, and the process of client Kerberos identity authentication in the Hadoop framework is discussed. From this, the current shortcomings of data storage security based on the Hadoop framework are analyzed. The elements of data storage security are also determined. Furthermore, a novel double secret key encryption method for data storage security and to improve the security of stored data itself is introduced. Simultaneously, hash computing is used to improve the read and write performance of data after secure storage. Experimental results clearly show that our proposed method can effectively improve read and write performance of data, and that the performance of data security operations is improved from current standard implementations. [ABSTRACT FROM AUTHOR]