Security and Privacy Implications on Database Systems in Big Data Era: A Survey

Samaraweera, G. Dumindu; Chang, J. Morris

doi:10.1109/tkde.2019.2929794

Cited by 40 publications

(16 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in addition to semantic feature learning, improving the computational environment with advanced feature selection, resampling, and classification is a must. Moreover, assessing the suitability of the aforesaid methods also needed to be tested with dynamic distributed data with interoperable heterogeneous query elements [51][52][53]. These key factors can be considered the key driving forces behind this study.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Dynamic Data Infrastructure Security for Interoperable e‐Healthcare Systems: A Semantic Feature‐Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

View full text Add to dashboard Cite

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Dynamic Data Infrastructure Security for Interoperable e‐Healthcare Systems: A Semantic Feature‐Driven NoSQL Intrusion Attack Detection Model

Sreejith

Senthil

2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…The bottom-up approach first designs the most basic data structure at the bottom of the system, and then gradually merges the basic data structures according to the logical relationships within the system, from the bottom up until the overall data structure of the system is obtained. The one-by-one expansion method first defines and designs the core data structure inside the system, and then gradually expands outward from the core data according to the internal logical relationship of the system, until the design of all data structures inside the system is complete [ 40 ].…”

Section: System Designmentioning

confidence: 99%

A Financial Management Platform Based on the Integration of Blockchain and Supply Chain

Yang

Xiong

et al. 2023

Sensors

View full text Add to dashboard Cite

Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.

show abstract

“…Communication issues between PLCs and the engineering stations or field devices concerning security must be confronted. Modern database systems use communication systems to deploy as cloud-based solutions [66]. Different DBs support various security technologies, though most non-relational solutions overlook modern Big Data applications.…”

Section: Communication Protocolsmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

View full text Add to dashboard Cite

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

show abstract

Security and Privacy Implications on Database Systems in Big Data Era: A Survey

Cited by 40 publications

References 62 publications

[Retracted] Dynamic Data Infrastructure Security for Interoperable e‐Healthcare Systems: A Semantic Feature‐Driven NoSQL Intrusion Attack Detection Model

[Retracted] Dynamic Data Infrastructure Security for Interoperable e‐Healthcare Systems: A Semantic Feature‐Driven NoSQL Intrusion Attack Detection Model

A Financial Management Platform Based on the Integration of Blockchain and Supply Chain

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Contact Info

Product

Resources

About