An Innovative Encryption Method for Agriculture Intelligent Information System based on Cloud Computing Platform

Tan, Wen Xue; Zhao, Chun Jiang; Wang, Hua; Wang, Xi Ping

doi:10.4304/jsw.9.1.1-10

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this purpose, the data are encrypted before transmission and then decoded at the destination. Wen Xue et al [97] presented an encryption method for agricultural information systems. This method builds secure communication among users, farmers, and cloud servers.…”

Section: Security and Privacymentioning

confidence: 99%

A Platform Approach to Smart Farm Information Processing

et al. 2022

View full text Add to dashboard Cite

With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance productivity in farming practices. In a smart farming scenario, large amounts of data are collected from diverse sources such as wireless sensor networks, network-connected weather stations, monitoring cameras, and smartphones. These data are valuable resources to be used in data-driven services and decision support systems (DSS) in farming applications. However, one of the major challenges with these large amounts of agriculture data is their immense diversity in terms of format and meaning. Moreover, the different services and technologies in a smart farming ecosystem have limited capability to work together due to the lack of standardized practices for data and system integration. These issues create a significant challenge in cooperative service provision, data and technology integration, and data-sharing practices. To address these issues, in this paper, we propose the platform approach, a design approach intended to guide building effective, reliable, and robust smart farming systems. The proposed platform approach considers six requirements for seamless integration, processing, and use of farm data. These requirements in a smart farming platform include interoperability, reliability, scalability, real-time data processing, end-to-end security and privacy, and standardized regulations and policies. A smart farming platform that considers these requirements leads to increased productivity, profitability, and performance of connected smart farms. In this paper, we aim at introducing the platform approach concept for smart farming and reviewing the requirements for this approach.

show abstract

Section: Security and Privacymentioning

confidence: 99%

A Platform Approach to Smart Farm Information Processing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a new computing paradigm that is based on the Internet and improves the utilization of hardware resources [2]. Currently, the mainstream IT companies implement the cloud computing in different ways.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Anomaly Detection Framework for Cloud Computing Environment

Lin¹,

Chen²

2015

JCP

View full text Add to dashboard Cite

Infrastructure as a Service (IaaS) is an important service type provided by cloud computing. Infrastructure resources are encapsulated into services and they are provided to users over the Internet in the form of virtual machines. A malicious user can upload malicious software into the virtual machine allocated by a cloud computing service provider and launch the side channel attacks to other virtual machines located in the same physical node by operating his own virtual machine. In order to address the above problem, this paper proposes an efficient anomaly detection framework for cloud computing environment to detect the virtual machines that present abnormal behaviors. A new feature extraction algorithm is designed to reduce the dimensionality of the collected data and a new anomaly detection algorithm is also designed to detect the abnormal virtual machines. A series of experiments are conducted on a cloud computing environment that is deployed using the open source project OpenStack to evaluate the proposed framework. Experimental results show that the proposed framework is better than other anomaly detection methods designed for cloud computing environment in terms of precision, recall, false alarm rate, and runtime.

show abstract