An Unstructured to Structured Data Conversion using Machine Learning Algorithm in Internet of Things (IoT)

2020 ◽  
Author(s):  
Saurav Verma ◽  
Khushboo Jain ◽  
Chetana Prakash
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Structured Data ◽  
Data Conversion ◽  
Machine Learning Algorithm

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Tree Health ◽  
Environmental Feature ◽  
Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

scholarly journals The Abnormal Detection for Network Traffic of Power IoT Based on Device Portrait

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiaxuan Fei ◽  
Qigui Yao ◽  
Mingliang Chen ◽  
Xiangqun Wang ◽  
Jie Fan
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Traffic Data ◽  
Network Environment ◽  
Security Measures ◽  
Privacy Leakage ◽  
Traffic Characteristics

The construction of power Internet of things is an important development direction for power grid enterprises. Although power Internet of things is a kind of network, it is denser than the ordinary Internet of things points and more complex equipment types, so it has higher requirements for network security protection. At the same time, due to the special information perception and transmission mode in the Internet of things, the information transmitted in the network is easy to be stolen and resold, and traditional security measures can no longer meet the security protection requirements of the new Internet of things devices. To solve the privacy leakage and security attack caused by the illegal intrusion in the network, this paper proposes to construct a device portrait for terminal devices in the power Internet of things and detect abnormal traffic in the network based on device portrait. By collecting traffic data in the network environment, various network traffic characteristics are extracted, and abnormal traffic is analyzed and identified by the machine learning algorithm. By collecting the traffic data in the network environment, the features are extracted from the physical layer, network layer, and application layer of the message, and the device portrait is generated by a machine learning algorithm. According to the established attack mode, the corresponding traffic characteristics are analyzed, and the detection of abnormal traffic is achieved by comparing the attack traffic characteristics with the device portrait. The experimental results show that the accuracy of this method is more than 90%.

scholarly journals Routing Attacs pada Internet Of Things Berbasis Smart Intrution Detecion System

2020 ◽  
Vol 7 (2) ◽  
pp. 329
Author(s):  
Eka Lailatus Sofa ◽  
Subiyanto Subiyanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Routing Attacks

<p class="Abstrak"><em>Internet of Things</em> (IoT) telah memasuki berbagai aspek kehidupan manusia, diantaranya <em>smart city, smart home, smart street, </em>dan<em> smart industry </em>yang memanfaatkan internet untuk memantau informasi yang dibutuhkan<em>.</em> Meskipun sudah dienkripsi dan diautentikasi, protokol jaringan <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) yang dapat menghubungkan benda-benda yang terbatas sumber daya di IoT masih belum dapat diandalkan. Hal ini dikarenakan benda-benda tersebut masih dapat terpapar oleh <em>routing attacks</em> yang berasal dari jaringan 6LoWPAN dan internet. Makalah ini menyajikan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> untuk menganalisis model <em>routing attacks</em> lainnya pada jaringan IoT. IDS menggunakan <em>compression header</em> 6LoWPAN sebagai fitur untuk <em>machine learning algorithm</em> dalam mempelajari jenis <em>routing attacks</em>. Skenario simulasi dikembangkan untuk mendeteksi <em>routing attacks</em> berupa <em>selective forwarding attack</em> dan <em>sinkhole attack</em>. Pengujian dilakukan menggunakan <em>feature selection</em> dan <em>machine learning algorithm</em>. <em>Feature selection</em> digunakan untuk menentukan fitur signifikan yang dapat membedakan antara aktivitas normal dan abnormal. Sementara <em>machine learning algorithm</em> digunakan untuk mengklasifikasikan <em>routing attacks</em> pada jaringan IoT. Ada tujuh <em>machine learning algorithm</em> yang digunakan dalam klasifikasi antara lain <em>Random Forest, Random Tree, J48, Bayes Net, JRip, SMO,</em> dan <em>Naive Bayes</em>. Hasil percobaan disajikan untuk menunjukkan kinerja <em>Smart Intrusion Detection System</em> berdasarkan <em>Compression Header Analyzer</em> dalam menganalisis <em>routing attacks</em>. Hasil evaluasi menunjukkan bahwa IDS ini dapat mendeteksi antara serangan dan <em>non-</em>serangan.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Internet of Things (IoT) has entered various aspects of human life including smart city, smart home, smart street, and smart industries that use the internet to get the information they need. Even though it's encrypted and authenticated, Internet protocol  <a title="IPv6" href="https://en.wikipedia.org/wiki/IPv6">IPv6</a> over Low-Power Wireless <a title="Personal area network" href="https://en.wikipedia.org/wiki/Personal_area_network">Personal Area Networks</a> (6LoWPAN) networks that can connect limited resources to IoT are still unreliable. This is because these objects can still be exposed to attacks from 6LoWPAN and the internet. This paper presents the performance of an Smart Intrusion Detection System based on Compression Header Analyzer to analyze other routing attack models on IoT networks. IDS uses a 6LoWPAN compression header as a feature for machine learning algorithms in learning the types of routing attacks. Simulation scenario was developed to detect routing attacks in the form of selective forwarding and sinkhole. Testing is done using the feature selection and machine learning algorithm. Feature selection is used to determine significant features that can distinguish between normal and abnormal activities. While machine learning algorithm is used to classify attacks on IoT networks. There were seven machine learning algorithms used in the classification including Random Forests, Random Trees, J48, Bayes Net, JRip, SMO, and Naive Bayes. Experiment Results to show the results of the Smart Intrusion Detection System based on Compression Header Analyzer in analyzing routing attacks. The evaluation results show that this IDS can protect between attacks and non-attacks.</em><strong></strong></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Trust Management Approach for Detection of Malicious Devices in SIoT

2021 ◽  
Vol 15 (1) ◽  
pp. 43-50
Author(s):  
Priyanka Hankare ◽  
Sachin Babar ◽  
Parikshit Mahalle
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Social Networking ◽  
Trust Management ◽  
Learning Algorithm ◽  
Management Approach ◽  
Heterogeneous Environments ◽  
Machine Learning Algorithm ◽  
Building Relationships ◽  
Internet Of Thing

Internet of Things (IoT) is an innovative era of interrelated devices to provide services to other devices or users. In Social Internet of Thing (SIoT), social networking aspect is used for building relationships between devices. For providing or utilizing services, devices need to trust each other in complex and heterogeneous environments. Separating benign and malicious devices in SIoT is a prime security objective. In literature, several works proposed trust computation models based on trust features. But these models fail to identify malicious devices. This paper focuses on detection of malicious devices. In this paper, basic fundamentals, properties, models and attacks of trust in SIoT are discussed. Up-to-date research distributions on trust management and trust attacks are reviewed and idea of Trust Management using Machine Learning Algorithm (TM-MLA) is proposed for identification of malicious devices.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery
Sign in / Sign up

Export Citation Format

Share Document