scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

Machine learning algorithms for smart data analysis in the Internet of things: an overview

2021 ◽  
pp. 307-327
Author(s):  
Mohammed H. Alsharif ◽  
Anabi Hilary Kelechi ◽  
Imran Khan ◽  
Mahmoud A. Albreem ◽  
Abu Jahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Smart Data ◽  
The Internet Of Things

scholarly journals Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 444 ◽  
Author(s):  
Valerio Morfino ◽  
Salvatore Rampone
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Apache Spark ◽  
Identification Accuracy ◽  
Supervised Machine Learning ◽  
Iot Devices

In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

scholarly journals Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Farman Pirzado ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja Dhomeja ◽  
Awais Ahmed
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Mobile Banking ◽  
Alternative Technique ◽  
Keystroke Dynamics ◽  
Data Set ◽  
Proposed Model

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.

scholarly journals Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1677
Author(s):  
Ersin Elbasi ◽  
Ahmet E. Topcu ◽  
Shinu Mathew
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Bayes Classifier ◽  
Social Distancing ◽  
Public Areas ◽  
Iot Devices

COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.

scholarly journals Not All Attributes are Created Equal: dX -Private Mechanisms for Linear Queries

2020 ◽  
Vol 2020 (1) ◽  
pp. 103-125
Author(s):  
Parameswaran Kamalaruban ◽  
Victor Perrier ◽  
Hassan Jameel Asghar ◽  
Mohamed Ali Kaafar
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Differential Privacy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Trade Off ◽  
Systematic Procedure ◽  
Privacy Budget ◽  
Sensitivity Vector

AbstractDifferential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider dX -privacy, an instantiation of the privacy notion introduced in [6], which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its dX -private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several dX -private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed dX -private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.

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