scholarly journals The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1788 ◽  
Author(s):  
Noshina Tariq ◽  
Muhammad Asim ◽  
Feras Al-Obeidat ◽  
Muhammad Zubair Farooqi ◽  
Thar Baker ◽  
...  
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Critical Infrastructure ◽  
Fog Computing ◽  
Security Requirements ◽  
Security Challenges ◽  
Depth Analysis ◽  
Comprehensive Comparison ◽  
Attack Surface ◽  
Criminal Groups

The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

scholarly journals A Survey of Protocol-Level Challenges and Solutions for Distributed Energy Resource Cyber-Physical Security

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2360 ◽  
Author(s):  
Aditya Sundararajan ◽  
Aniket Chavan ◽  
Danish Saleem ◽  
Arif Sarwat
Keyword(s):  
Energy Resource ◽  
Security Requirements ◽  
Future Research ◽  
Clear Understanding ◽  
Distributed Energy ◽  
Proper Understanding ◽  
Distributed Energy Resource ◽  
Starting Point ◽  
Security Challenges ◽  
Attack Surface

The increasing proliferation of distributed energy resources (DERs) on the smart grid has made distributed solar and wind two key contributors to the expanding attack surface of the network; however, there is a lack of proper understanding and enforcement of DER communications security requirements. With vendors employing proprietary methods to mitigate hosts of attacks, the literature currently lacks a clear organization of the protocol-level vulnerabilities, attacks, and solutions mapped to each layer of the logical model such as the OSI stack. To bridge this gap and pave the way for future research by the authors in determining key DER security requirements, this paper conducts a comprehensive review of the key vulnerabilities, attacks, and potential solutions for solar and wind DERs at the protocol level. In doing so, this paper serves as a starting point for utilities, vendors, aggregators, and other industry stakeholders to develop a clear understanding of the DER security challenges and solutions, which are key precursors to comprehending security requirements.

scholarly journals Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiuqing Chen ◽  
Hong Zhu ◽  
Deqin Geng ◽  
Wei Liu ◽  
Rui Yang ◽  
...  
Keyword(s):  
Big Data ◽  
Hybrid Systems ◽  
Data Privacy ◽  
Health Care Services ◽  
Fog Computing ◽  
Physiological Signals ◽  
Medical Decision ◽  
Implantable Medical Devices ◽  
Physiological Signal ◽  
Medical Decision Support

The proliferation of physiological signals acquisition and monitoring system, has led to an explosion in physiological signals data. Additionally, RFID systems, blockchain technologies, and the fog computing mechanisms have significantly increased the availability of physiological signal information through big data research. The driver for the development of hybrid systems is the continuing effort in making health-care services more efficient and sustainable. Implantable medical devices (IMD) are therapeutic devices that are surgically implanted into patients’ body to continuously monitor their physiological parameters. Patients treat cardiac arrhythmia due to IMD therapeutic and life-saving benefits. We focus on hybrid systems developed for patient physiological signals for collection, storage protection, and monitoring in critical care and clinical practice. In order to provide medical data privacy protection and medical decision support, the hybrid systems are presented, and RFID, blockchain, and big data technologies are used to analyse physiological signals.

scholarly journals Research on Design of Fog Computing Optimization Model for Medical Big Data

2021 ◽  
Author(s):  
Baoling Qin
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Deep Learning ◽  
Optimization Model ◽  
Data Privacy ◽  
Fog Computing ◽  
Information Service ◽  
Heterogeneous Data ◽  
Data Redundancy ◽  
Medical Big Data

Targeted at the current issues of communication delay, data congestion, and data redundancy in cloud computing for medical big data, a fog computing optimization model is designed, namely an intelligent front-end architecture of fog computing. It uses the network structure characteristics of fog computing and “decentralized and local” mind-sets to tackle the current medical IoT network’s narrow bandwidth, information congestion, heavy computing burden on cloud services, insufficient storage space, and poor data security and confidentiality. The model is composed of fog computing, deep learning, and big data technology. By full use of the advantages of WiFi and user mobile devices in the medical area, it can optimize the internal technology of the model, with the help of classification methods based on big data mining and deep learning algorithms based on artificial intelligence, and automatically process case diagnosis, multi-source heterogeneous data mining, and medical records. It will also improve the accuracy of medical diagnosis and the efficiency of multi-source heterogeneous data processing while reducing network delay and power consumption, ensuring patient data privacy and safety, reducing data redundancy, and reducing cloud overload. The response speed and network bandwidth of the system have been greatly optimized in the process, which improves the quality of medical information service.

scholarly journals Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Lixin Zheng ◽  
Shangguang Wang ◽  
Chao Huang ◽  
...  
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fog Computing ◽  
Full Dataset ◽  
Security Challenges ◽  
Protection Technology ◽  
Decision Tree Method

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

scholarly journals Fog computing security and privacy issues, open challenges, and blockchain solution: An overview

2021 ◽  
Vol 11 (6) ◽  
pp. 5081
Author(s):  
Yehia Ibrahim Alzoubi ◽  
Ahmad Al-Ahmad ◽  
Ashraf Jaradat
Keyword(s):  
Fog Computing ◽  
Security Requirements ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Security Vulnerabilities ◽  
Blockchain Technology ◽  
Security Challenges ◽  
Iot Devices ◽  
The Impact ◽  
Privacy Issues

<span lang="EN-US">Due to the expansion growth of the IoT devices, Fog computing was proposed to enhance the low latency IoT applications and meet the distribution nature of these devices. However, Fog computing was criticized for several privacy and security vulnerabilities. This paper aims to identify and discuss the security challenges for Fog computing. It also discusses blockchain technology as a complementary mechanism associated with Fog computing to mitigate the impact of these issues. The findings of this paper reveal that blockchain can meet the privacy and security requirements of fog computing; however, there are several limitations of blockchain that should be further investigated in the context of Fog computing.</span>

Security Challenges in Big Data

2018 ◽  
Vol 6 (7) ◽  
pp. 981-985
Author(s):  
Dinesh Singh ◽  
Dayanand . ◽  
Arushi Arya
Keyword(s):  
Big Data ◽  
Security Challenges

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

Sign in / Sign up

Export Citation Format

Share Document