Knowledge-based systems for blockchain-based cognitive cloud computing model for security purposes

2021 ◽  
Author(s):  
Honglei Zhang ◽  
Zhenbo Zang ◽  
BalaAnand Muthu
Keyword(s):  
Cloud Computing ◽  
Transmission Rate ◽  
Digital Revolution ◽  
Cost Rate ◽  
Sensory Data ◽  
Knowledge Based ◽  
Blockchain Technology ◽  
Physical Environments ◽  
Cognitive Information ◽  
Iot Devices

Today, artificial intelligence (AI) can use the most powerful edge computing systems in the Internet of Things (IoT) for finding the information extracted from vast sensory data such as cyber effects or models in physical environments for classification, identification, and prediction. Heterogeneous IoT devices produce isolated and dispersed information parts, and knowledge sharing and exchange in IoT intelligent applications with several selfish nodes are necessary for complex tasks. In both academia and business, IoT is driving a digital revolution. However, protection and IoT privacy problems are challenged. It offers comfort for everyday lives. Blockchain, a shared cryptographic database, is a promising IoT encryption solution for several manufacturing, finance, and trade sectors. The IoT-based blockchain architecture is an interesting contrast to the conventional, centralized paradigm that struggles to fulfill specific IoT requirements. New concepts for applying data and resources management protection procedures in distributed networks and cloud computing are introduced. Cloud management services can be linked to the application through blockchain technology and distributed leader, a stable cognitive information system that facilitates management operations and securing data. This document provides many ideas for applying personal and behavioral characteristics to security and cryptography protocols, blockchain based on the cognitive cloud computing (BC-CCC) pattern. The simulation result shows that the proposed strategy can significantly enhance data transmission rate (96.2%), security ratio (94.5%), throughput ratio (92.4%), scalability ratio (91.5%), trust rate (93.8%), data trading ratio (96.2%), and reduce storage cost rate (25.1%) compared to other existing methods.

scholarly journals Blockchain Technology for Intelligent Environments

2019 ◽  
Vol 11 (10) ◽  
pp. 213 ◽  
Author(s):  
Spyros Voulgaris ◽  
Nikos Fotiou ◽  
Vasilios A. Siris ◽  
George C. Polyzos ◽  
Mikael Jaatinen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Third Party ◽  
Intelligent Environments ◽  
Digital World ◽  
Blockchain Technology ◽  
Physical Environments ◽  
Fast Pace ◽  
Iot Devices ◽  
First Time ◽  
The Internet Of Things

In the last few years, we have been witnessing the convergence of the physical with the digital world. The Internet of Things (IoT) is progressing at a fast pace, and IoT devices are becoming pervasive in our physical environments, bringing the vision of Intelligent Environments closer to reality. At the same time, the newly-introduced blockchain technology is offering for the first time ever cryptographically proven trust based on a set of mutually untrusted nodes. Blockchain technology thus has the potential to become a key component of many IoT systems, offering them an unprecedented level of accountability, transparency, and reliability. This paper first lays out the principles on which blockchain systems are operating, along with descriptions of the most noteworthy blockchain implementations. It then presents a number of systems through which blockchains may interact with external systems and third-party data sources. Finally, it provides a survey of the state-of-the-art blockchain-based systems targeting IoT applications.

scholarly journals The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Palmyra Repette ◽  
Jamile Sabatini-Marques ◽  
Tan Yigitcanlar ◽  
Denilson Sell ◽  
Eduardo Costa
Keyword(s):  
Urban Development ◽  
Social Economic ◽  
Local Governance ◽  
Open Data ◽  
Journal Articles ◽  
Collective Knowledge ◽  
Digital Revolution ◽  
Knowledge Based ◽  
The Government ◽  
The City

Since the advent of the second digital revolution, the exponential advancement of technology is shaping a world with new social, economic, political, technological, and legal circumstances. The consequential disruptions force governments and societies to seek ways for their cities to become more humane, ethical, inclusive, intelligent, and sustainable. In recent years, the concept of City-as-a-Platform was coined with the hope of providing an innovative approach for addressing the aforementioned disruptions. Today, this concept is rapidly gaining popularity, as more and more platform thinking applications become available to the city context—so-called platform urbanism. These platforms used for identifying and addressing various urbanization problems with the assistance of open data, participatory innovation opportunity, and collective knowledge. With these developments in mind, this study aims to tackle the question of “How can platform urbanism support local governance efforts in the development of smarter cities?” Through an integrative review of journal articles published during the last decade, the evolution of City-as-a-Platform was analyzed. The findings revealed the prospects and constraints for the realization of transformative and disruptive impacts on the government and society through the platform urbanism, along with disclosing the opportunities and challenges for smarter urban development governance with collective knowledge through platform urbanism.

scholarly journals Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4798
Author(s):  
Fangni Chen ◽  
Anding Wang ◽  
Yu Zhang ◽  
Zhengwei Ni ◽  
Jingyu Hua
Keyword(s):  
Energy Consumption ◽  
Transmission Rate ◽  
Edge Computing ◽  
Energy Harvest ◽  
Total System ◽  
Mobile Edge Computing ◽  
Weight Factor ◽  
Iot Devices ◽  
System Variables ◽  
The Relationship

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals Towards Secure Network Computing Services for Lightweight Clients Using Blockchain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Xu ◽  
Guojun Wang ◽  
Jidian Yang ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Service Providers ◽  
Security Analysis ◽  
Service Provisioning ◽  
Network Computing ◽  
Security Risks ◽  
Blockchain Technology ◽  
Computing Services ◽  
Viable Solution ◽  
Secure Network ◽  
Iot Devices

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.

scholarly journals Device-Based Security to Improve User Privacy in the Internet of Things †

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2664 ◽  
Author(s):  
Luis Belem Pacheco ◽  
Eduardo Pelinson Alchieri ◽  
Priscila Mendez Barreto
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Single Point ◽  
Data Access ◽  
Cloud Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Privacy And Security ◽  
Iot Devices ◽  
And Control

The use of Internet of Things (IoT) is rapidly growing and a huge amount of data is being generated by IoT devices. Cloud computing is a natural candidate to handle this data since it has enough power and capacity to process, store and control data access. Moreover, this approach brings several benefits to the IoT, such as the aggregation of all IoT data in a common place and the use of cloud services to consume this data and provide useful applications. However, enforcing user privacy when sending sensitive information to the cloud is a challenge. This work presents and evaluates an architecture to provide privacy in the integration of IoT and cloud computing. The proposed architecture, called PROTeCt—Privacy aRquitecture for integratiOn of internet of Things and Cloud computing, improves user privacy by implementing privacy enforcement at the IoT devices instead of at the gateway, as is usually done. Consequently, the proposed approach improves both system security and fault tolerance, since it removes the single point of failure (gateway). The proposed architecture is evaluated through an analytical analysis and simulations with severely constrained devices, where delay and energy consumption are evaluated and compared to other architectures. The obtained results show the practical feasibility of the proposed solutions and demonstrate that the overheads introduced in the IoT devices are worthwhile considering the increased level of privacy and security.

scholarly journals DDoS Detection on Internet of Things using Unsupervised Algorithms

2021 ◽  
Vol 297 ◽  
pp. 01005
Author(s):  
Hailyie Tekleselassie
Keyword(s):  
Learning Algorithms ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Accuracy Score ◽  
Ddos Attacks ◽  
Knowledge Based ◽  
Ddos Detection ◽  
Iot Devices ◽  
Unsupervised Algorithms

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

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