Security and privacy for IoT and fog computing paradigm

2018 ◽  
Author(s):  
Abdul Rauf ◽  
Riaz Ahmed Shaikh ◽  
Asadullah Shah
Keyword(s):  
Fog Computing ◽  
Security And Privacy ◽  
Computing Paradigm

scholarly journals A survey on fog computing: research challenges in security and privacy issues

2018 ◽  
Vol 7 (2.7) ◽  
pp. 335 ◽  
Author(s):  
T Veerraju ◽  
Dr K. Kiran Kumar
Keyword(s):  
Fog Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
Limited Resources ◽  
Privacy And Security ◽  
Computing Paradigm ◽  
Is Security ◽  
Real Time Applications ◽  
And Storage ◽  
Privacy Issues

With the rapid advancement of Internet of Things has enabled to combine the intercommunication and interconnection between seamless networks. Cloud computing provides backend solutions and one among the most prominent technologies for the users, still cannot be solved all the problems such as latency of real time applications. However, a new computing paradigm comes in to the picture. Many of the researchers focused on this exemplar known as Fog/Edge computing, which has been planned to the extension of cloud services. Fog provides the services to the edge of the networks, which makes communication, computation and storage for end users through fog devices and for servers like controllers. We analyze the study, which aims to augment low bandwidth, latency along with the privacy and security.   The major problem in the Fog computing is security due to the limited resources. In this paper, we investigated the protection issues and confrontation of Fog and also provide countermeasures on security for different attacks. We focused the future security directions and challenges to address in fog networks.

scholarly journals A Smart Water Metering Deployment Based on the Fog Computing Paradigm

2020 ◽  
Vol 10 (6) ◽  
pp. 1965 ◽  
Author(s):  
Dimitrios Amaxilatis ◽  
Ioannis Chatzigiannakis ◽  
Christos Tselios ◽  
Nikolaos Tsironis ◽  
Nikos Niakas ◽  
...  
Keyword(s):  
Smart City ◽  
Data Privacy ◽  
Data Exchange ◽  
Fog Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
Smart Water ◽  
Computing Paradigm ◽  
Computational Performance ◽  
Water Metering

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system’s design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system.

scholarly journals An Overview of Fog Computing and Edge Computing Security and Privacy Issues

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8226
Author(s):  
Ahmed M. Alwakeel
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Edge Computing ◽  
Security And Privacy ◽  
Local Network ◽  
Security Issues ◽  
Computing Paradigm ◽  
Edge And Fog Computing ◽  
Cloud Technologies ◽  
Privacy Issues

With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.

Simulation and Emulation Tools for Fog Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Simar Preet Singh ◽  
Rajesh Kumar ◽  
Anju Sharma ◽  
S. Raji Reddy ◽  
Priyanka Vashisht
Keyword(s):  
Quality Of Service ◽  
Programming Languages ◽  
Traffic Management ◽  
Fog Computing ◽  
Research Work ◽  
Research Experience ◽  
Computing Paradigm ◽  
Quality Of Service Parameters ◽  
User Friendly

Background: Fog computing paradigm has recently emerged and gained higher attention in present era of Internet of Things. The growth of large number of devices all around, leads to the situation of flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: Review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that uses different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

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

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