scholarly journals Detection of Suspicious or Un-Trusted Users in Crypto-Currency Financial Trading Applications

2021 ◽  
Vol 13 (1) ◽  
pp. 79-93
Author(s):  
Ruchi Mittal ◽  
M. P. S. Bhatia
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Smart Devices ◽  
Banking Services ◽  
Financial Trading ◽  
Learning Techniques ◽  
Reputation Score ◽  
Transaction Services

In this age, where cryptocurrencies are slowly creeping into the banking services and making a name for them, it is becoming crucially essential to figure out the security concerns when users make transactions. This paper investigates the untrusted users of cryptocurrency transaction services, which are connected using smartphones and computers. However, as technology is increasing, transaction frauds are growing, and there is a need to detect vulnerabilities in systems. A methodology is proposed to identify suspicious users based on their reputation score by collaborating centrality measures and machine learning techniques. The results are validated on two cryptocurrencies network datasets, Bitcoin-OTC, and Bitcoin-Alpha, which contain information of the system formed by the users and the user's trust score. Results found that the proposed approach provides improved and accurate results. Hence, the fusion of machine learning with centrality measures provides a highly robust system and can be adapted to prevent smart devices' financial services.

scholarly journals Machine Learning Based Indoor Localisation Using Wi-Fi And Smartphone

10.31645/06 ◽  
2020 ◽  
Author(s):  
Zulqarnain Khokhar ◽  
◽  
Murtaza Ahmed Siddiqi ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Gradient Boosting ◽  
Learning Techniques ◽  
Indoor Localisation

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

scholarly journals Predicting Spread, Recovery and Death Due to COVID-19 using a Time-Series Model (Prophet)

2021 ◽  
Vol 20 (1) ◽  
pp. 71-76
Author(s):  
Sk. Golam Mahmud ◽  
Mahbub C. Mishu ◽  
Dip Nandi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Financial Services ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Effective Vaccine ◽  
Python Language ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
The World

The world is facing its biggest challenge since 1920 due to spread of COVID-19 virus. Identified in China in December 2019, the virus has spread more than 200 countries in the world. Scientists have named the virus as Novel Corona Virus (belongs to SARS group virus). The virus has caused severe disruption to our world. Educational institutions, financial Services, government services and many other sectors are badly affected by this virus. More importantly, the virus has causeda massive amount of human deaths around the world and still its infecting people every day. Scientist around the world are trying to find a solution to stop the COVID-19. Their solutions include identifying possible effective vaccine, computeraided modelling to see the pattern of spread etc. Using Machine Learning techniques, it is possible to forecast the spread, death, and recovery due to COVID-19. In this article, we have shown a machine learning model named as Prophet Time Series Analysis to forecast the spread, death, and recovery in different countries. We train the model using the available historical data on COVID-19 from John Hopkins University’s COVID-19 site. Then we forecast spread, death, and recovery for seven days using a well known forecasting model called Prophet. This interval can be increased to see the effect of COVID-19. We chose 145 days of historical data to train the model then we predict effect for seven days (15 June 2020 to 22 June 2020). To verify out result, we compare the predicted value with actual value of spread, death and recovery. The model provides accuracy over 92% in all the cases. Our model can be used to identify the effect of COVID-19 in any countries in the world. The system is developed using Python language and visualization is also possible interactively. By using our system, it will be possible to observe the effect of spread, death and recovery for any countries for any period of time. 

Smart IoT Systems

2021 ◽  
pp. 100-119
Author(s):  
Ritu Chauhan ◽  
Sandhya Avasthi ◽  
Bhavya Alankar ◽  
Harleen Kaur
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Due Diligence ◽  
Everyday Objects ◽  
Learning Techniques ◽  
Smart Home Environment ◽  
The Internet Of Things

The IoT or the internet of things started as a technology to connect everyday objects over the internet, which has evolved into something big and invaded into every single aspect of our lives. As technology is gaining momentum, IoT-based smart devices usage among users is expanding, which generates massive data at our disposal across various domains. The authors have systematically studied the taxonomy of data analytics and the benefits of using advanced machine learning techniques in converting data into valuable assets. In the studies, they have identified and did due diligence on different smart home systems, their features, and configuration. During this course of study, they have also identified the vulnerability of such a system and threats associated with these vulnerabilities in a secure smart home environment.

Machine Learning Techniques to Mitigate Security Attacks in IoT

2020 ◽  
pp. 65-93
Author(s):  
Kavi Priya S. ◽  
Vignesh Saravanan K. ◽  
Vijayalakshmi K.
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Information Exchange ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Sensitive Data ◽  
Iot Security ◽  
Security Issues ◽  
Learning Techniques

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.

scholarly journals Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1627 ◽  
Author(s):  
Francesco Salamone ◽  
Alice Bellazzi ◽  
Lorenzo Belussi ◽  
Gianfranco Damato ◽  
Ludovico Danza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Visual Stimuli ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Learning Approaches ◽  
Biometric Data ◽  
Target Value ◽  
Learning Techniques ◽  
Comfort Perception

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.

scholarly journals Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 13-28
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Supervised Machine Learning ◽  
Security Requirements ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Prime Field ◽  
Learning Techniques ◽  
The Internet Of Things

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

scholarly journals AndroAnalyzer: android malicious software detection based on deep learning

2021 ◽  
Vol 7 ◽  
pp. e533
Author(s):  
Recep Sinan Arslan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Smart Devices ◽  
Android Malware ◽  
Original Dataset ◽  
Learning Techniques ◽  
Technological Developments

Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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