Trust Management in Fog Computing

2019 ◽  
pp. 34-48
Author(s):  
Sunilkumar S. Manvi ◽  
Naveen Chandra Gowda
Keyword(s):  
Cloud Computing ◽  
Computational Model ◽  
Trust Management ◽  
Fog Computing ◽  
New Challenges ◽  
Distributed Cloud

Fog computing is an encouraging computational model that extends distributed cloud computing to the edge of systems. It varies to cloud computing with some of the attributes. Fog computing has new challenges while building and maintaining the trust among the fog nodes and with edge devices. The solutions applied for the various cloud challenges cannot be directly applied for fog computing. This chapter gives an overview of these difficulties and relates solutions in a concise way. It also highlights the open challenges that still exist in fog computing.

scholarly journals Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing

Energies ◽  
2014 ◽  
Vol 7 (8) ◽  
pp. 5151-5176 ◽  
Author(s):  
Xiaolong Cui ◽  
Bryan Mills ◽  
Taieb Znati ◽  
Rami Melhem
Keyword(s):  
Cloud Computing ◽  
Computational Model ◽  
Fault Tolerant ◽  
Energy Aware ◽  
Green Cloud Computing ◽  
Green Cloud

Using Distributed Cloud Computing to Solve Resource-Intensive Tasks

2021 ◽  
Vol 12 (5) ◽  
pp. 233-254
Author(s):  
D. Yu. Bulgakov ◽  
Keyword(s):  
Cloud Computing ◽  
Point Of View ◽  
Free Software ◽  
Production Environment ◽  
Learning Models ◽  
Python Programming Language ◽  
Task Processing ◽  
Python Programming ◽  
Distributed Cloud ◽  
Machine Learning Models

A method for solving resource-intensive tasks that actively use the CPU, when the computing resources of one server become insufficient, is proposed. The need to solve this class of problems arises when using various machine learning models in a production environment, as well as in scientific research. Cloud computing allows you to organize distributed task processing on virtual servers that are easy to create, maintain, and replicate. An approach based on the use of free software implemented in the Python programming language is justified and proposed. The resulting solution is considered from the point of view of the theory of queuing. The effect of the proposed approach in solving problems of face recognition and analysis of biomedical signals is described.

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

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.

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