Cryptocurrency adoption: current stage, opportunities, and open challenges

Cryptocurrency is attracting the attention of academic and non- academic researchers as an alternative architecture of currency. Because of the growing of cryptocurrency research, it is essential to value the existing research of cryptocurrency and identify potential future research areas. This paper provides an up to date review of Information system (IS) research on cryptocurrency adoption. In this paper, we conduct a systematic literature review to gather the previous research related to cryptocurrency adoption. The goal of this research is to identify the current research stage and open challenges for future studies in cryptocurrency adoption. Moreover, the paper presents a systematic literature review (SLR) of 25 research articles published on the adoption of cryptocurrency from 2014 to 2017. The results demonstrate that cryptocurrency adoption research has grown significantly throughout this period, and remains a fertile area for academic research. The results show that the cryptocurrency adoption literature can be classified according to three main classifications: qualitative research, quantitative research and others. The results of the SLR reveal that there is a lack of study focusing on the factors that are significantly influenced on the acceptance of cryptocurrency. Furthermore, there is also a lack of technology acceptance models used in addressing the issues. Research gap found is presented and discussed

A secure edge computing model using machine learning and IDS to detect and isolate intruders.

The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. [Abstract copyright: © 2024 The Author(s).

Analyzing Threats and Attacks in Edge Data Analytics within IoT Environments

Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics

Evaluation of postgraduate academic performance using artificial intelligence models

10.1016/j.aej.2022.03.021ALEXANDRIA ENGINEERING JOURNAL61129867-987