scholarly journals Adoption of Blockchain Based Smart Application in Machine Learning

2021 ◽  
Vol 9 (11) ◽  
pp. 600-605
Author(s):  
Vijaya Ravindra Wankhade
Keyword(s):  
Machine Learning ◽  
Data Security ◽  
Data Analytics ◽  
Security And Privacy ◽  
Support Vector ◽  
Grid Data ◽  
Security Issues ◽  
Blockchain Technology ◽  
Area Unit ◽  
Accuracy Of Results

Abstract: In recent years, the emergence of blockchain technology (BT) has become a novel, most disruptive, and trending technology. The redistributed database in BT emphasizes data security and privacy. Also, the consensus mechanism makes positive that data is secured and bonafide. Still, it raises new security issues like majority attacks and double-spending. To handle the said problems, data analytics is required on blockchain-based secure knowledge. Analytics on these data raises the importance of arising technology Machine Learning (ML). ml involves the rational quantity of data to create precise selections. data reliability and its sharing are terribly crucial in ml to enhance the accuracy of results. the combination of those two technologies (ML and BT) provide give highly precise results. in this paper, present gift a detailed study on ml adoption we BTbased present applications additional resilient against attacks. There area unit varied ancient ML techniques, for example, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms like Convolutional Neural Network (CNN) and Long STM (LSTM) are often used to analyze the attacks on a blockchain-based network. Further, we tend to embody however each the technologies are often applied in many sensible applications like unmanned Aerial Vehicle (UAV), sensible Grid (SG), healthcare, and sensible cities. Then, future analysis problems and challenges are explored. At last, a case study is presented with a conclusion. Keywords: Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

scholarly journals A Novel Framework of an IOT-Blockchain-Based Intelligent System

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Aliaa M. Alabdali
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Security ◽  
Intelligent System ◽  
Security And Privacy ◽  
Self Monitoring ◽  
Smart Systems ◽  
Blockchain Technology ◽  
Iot Devices ◽  
Smart Mobile

With the growing need of technology into varied fields, dependency is getting directly proportional to ease of user-friendly smart systems. The advent of artificial intelligence in these smart systems has made our lives easier. Several Internet of Things- (IoT-) based smart refrigerator systems are emerging which support self-monitoring of contents, but the systems lack to achieve the optimized run time and data security. Therefore, in this research, a novel design is implemented with the hardware level of integration of equipment with a more sophisticated software design. It was attempted to design a new smart refrigerator system, which has the capability of automatic self-checking and self-purchasing, by integrating smart mobile device applications and IoT technology with minimal human intervention carried through Blynk application on a mobile phone. The proposed system automatically makes periodic checks and then waits for the owner’s decision to either allow the system to repurchase these products via Ethernet or reject the purchase option. The paper also discussed the machine level integration with artificial intelligence by considering several features and implemented state-of-the-art machine learning classifiers to give automatic decisions. The blockchain technology is cohesively combined to store and propagate data for the sake of data security and privacy concerns. In combination with IoT devices, machine learning, and blockchain technology, the proposed model of the paper can provide a more comprehensive and valuable feedback-driven system. The experiments have been performed and evaluated using several information retrieval metrics using visualization tools. Therefore, our proposed intelligent system will save effort, time, and money which helps us to have an easier, faster, and healthier lifestyle.

Study and Survey on Blockchain Privacy and Security Issues

2021 ◽  
pp. 169-191
Author(s):  
Sourav Banerjee ◽  
Debashis Das ◽  
Manju Biswas ◽  
Utpal Biswas
Keyword(s):  
Data Storage ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy And Security ◽  
Unauthorized Access ◽  
Security Issues ◽  
Blockchain Technology ◽  
Targeted Attacks ◽  
Wide Range ◽  
Financial Transactions

Blockchain-based technology is becoming increasingly popular and is now used to solve a wide range of tasks. And it's not all about cryptocurrencies. Even though it's based on secure technology, a blockchain needs protection as well. The risks of exploits, targeted attacks, or unauthorized access can be mitigated by the instant incident response and system recovery. Blockchain technology relies on a ledger to keep track of all financial transactions. Ordinarily, this kind of master ledger would be a glaring point of vulnerability. Another tenet of security is the chain itself. Configuration flaws, as well as insecure data storage and transfers, may cause leaks of sensitive information. This is even more dangerous when there are centralized components within the platform. In this chapter, the authors will demonstrate where the disadvantages of security and privacy in blockchain are currently and discuss how blockchain technology can improve these disadvantages and outlines the requirements for future solution.

scholarly journals Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions

2018 ◽  
Vol 7 (2.7) ◽  
pp. 797
Author(s):  
Ravinder Korani ◽  
Dr P. Chandra Sekhar Reddy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
System Architecture ◽  
Data Security ◽  
Security And Privacy ◽  
Predictive Analysis ◽  
Detection Accuracy ◽  
Machine Learning Model ◽  
Detection Mechanisms ◽  
Networking Security

Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign scope heuristic ( ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate. 

scholarly journals A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2784 ◽  
Author(s):  
Chao Qu ◽  
Ming Tao ◽  
Ruifen Yuan
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Smart Homes ◽  
Security And Privacy ◽  
Smart Devices ◽  
Security Issues ◽  
Blockchain Technology ◽  
Security Problem ◽  
Fast Development ◽  
Privacy Issues

With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea of Blockchain (BC) theory has brought about a potential solution to the IoT security problem. The emergence of blockchain technology has brought about a change of decentralized management, providing an effective solution for the protection of network security and privacy. On the other hand, the smart devices in IoT are always lightweight and have less energy and memory. This makes the application of blockchain difficult. Against this background, this paper proposes a blockchain model based on hypergraphs. The aims of this model are to reduce the storage consumption and to solve the additional security issues. In the model, we use the hyperedge as the organization of storage nodes and convert the entire networked data storage into part network storage. We discuss the design of the model and security strategy in detail, introducing some use cases in a smart home network and evaluating the storage performance of the model through simulation experiments and an evaluation of the network.

scholarly journals Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

2018 ◽  
Vol 12 (7) ◽  
pp. 49 ◽  
Author(s):  
Osama Harfoushi ◽  
Dana Hasan ◽  
Ruba Obiedat
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Language Processing ◽  
Data Analytics ◽  
Support Vector ◽  
Social Media Analytics ◽  
Depth Analysis ◽  
Series Of Experiments ◽  
Microsoft Azure ◽  
Learning Analysis

The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

How users tweet about a cyber attack: An explorative study using machine learning and social network analysis

2020 ◽  
Vol 11 (2) ◽  
pp. 195-214 ◽  
Author(s):  
Daniel Vogler ◽  
Florian Meissner
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Cyber Attack ◽  
Ticket Sales ◽  
Security Issues ◽  
Twitter Users

Cybercrime is a growing threat for firms and customers that emerged with the digitization of business. However, research shows that even though people claim that they are concerned about their privacy online, they do not act correspondingly. This study investigates how prevalent security issues are during a cyber attack among Twitter users. The case under examination is the security breach at the US ticket sales company, Ticketfly, that compromised the information of 26 million users. Tweets related to cybersecurity are detected through the application of automated text classification based on supervised machine learning with support vector machines. Subsequently, the users that wrote security-related tweets are grouped into communities through a social network analysis. The results of this multi-method study show that users concerned about security issues are mostly part of expert communities with already superior knowledge about cybersecurity.

scholarly journals Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

2020 ◽  
pp. 522-529
Author(s):  
Sadhana Patidar ◽  
Priyanka Parihar ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Model Performance ◽  
Feature Reduction ◽  
Support Vector ◽  
Learning Tools ◽  
Security Issues ◽  
Proposed Model

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

scholarly journals Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

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