Scalable Fair Clustering Algorithm for Internet of Things Malware Classification

2022 ◽  
pp. 271-287
Author(s):  
Zibekieni Obuzor ◽  
Adesola Anidu
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Malware Classification

scholarly journals Multi-Layer Blockchain-Based Security Architecture for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 772 ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Model Development ◽  
Base Stations ◽  
Smart Devices ◽  
Security Model ◽  
Cluster Heads ◽  
Authentication Mechanism ◽  
Proposed Model ◽  
Authentication And Authorization

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

scholarly journals Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Min Yu ◽  
Rongrong Cui
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Design System ◽  
Design Effect ◽  
Constraint Matrix ◽  
Clothing Design ◽  
Culture Design

In order to improve the design effect of minority clothing, according to the needs of minority clothing design, this paper uses data mining and Internet of Things technologies to construct an intelligent ethnic clothing design system and builds an intelligent clothing design system that meets customer needs based on the idea of human-computer interaction. In data processing, this paper uses the constraint spectrum clustering algorithm to take the Laplacian matrix and the constraint matrix as input and finally outputs a clustering indicator vector to improve the data processing effect of minority clothing design. Finally, this paper verifies the performance of the system designed in this paper through experiments. From the experimental research, it can be known that the minority clothing design system based on the Internet of Things and data mining constructed in this paper has a certain effect and can effectively improve the minority clothing design effect.

A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

2019 ◽  
Vol 9 (2) ◽  
pp. 16-30 ◽  
Author(s):  
Hind Bangui ◽  
Mouzhi Ge ◽  
Barbora Buhnova
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Future Internet ◽  
Research Challenges ◽  
Initial Stage ◽  
Big Data Technologies

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

scholarly journals Forest Fire Prevention Using WSN Assisted IOT

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1317 ◽  
Author(s):  
Vrince Vimal ◽  
Madhav Ji Nigam
Keyword(s):  
Internet Of Things ◽  
Sensor Network ◽  
Forest Fire ◽  
Clustering Algorithm ◽  
Base Station ◽  
Sensor Nodes ◽  
Fire Prevention ◽  
New Era ◽  
Data Flows ◽  
Forest Fire Prevention

Internet of Things is the mainstay of the new era since its application becomes the future of day-to-day life. This work targets the IoT network assisted by WSN to prevent forest fire. We propose two-layer architecture of sensor network assisted by IoT enabled UAVs. The data flows in the proposed architecture in bottom-up fashion i.e., data is sensed by the nodes, which are deployed in the forest area (and sense temperature continuously). This data is transmitted to upper layer consisting of UAVs, which take appropriate action (to sprinkle water to bring temperature down to prevent fire). All the UAVs are interconnected to each other as well as to base station. The sensor nodes are clustered using two-step clustering algorithm, which takes care of the isolated nodes. The scheme has been equated to another WSN assisted IoT clustering technique. The proposed scheme outperforms the existing in terms of congestion at the UAV stations, number of alive nodes and remaining energy of the network.  

scholarly journals Android Malware Classification Using K-Means Clustering Algorithm

2017 ◽  
Vol 226 ◽  
pp. 012105 ◽  
Author(s):  
Isredza Rahmi A Hamid ◽  
Nur Syafiqah Khalid ◽  
Nurul Azma Abdullah ◽  
Nurul Hidayah Ab Rahman ◽  
Chuah Chai Wen
Keyword(s):  
Clustering Algorithm ◽  
Android Malware ◽  
Malware Classification

scholarly journals An Enhanced K-Means Clustering Algorithm for Pattern Discovery in Big Data Analysis of 3-Phase Electrical Quantities

2018 ◽  
Vol 7 (4.44) ◽  
pp. 8
Author(s):  
Dikpride Despa ◽  
Gigih Forda Nama
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Power Distribution ◽  
Distribution System ◽  
Clustering Algorithm ◽  
Measurement Data ◽  
Power Distribution System ◽  
Multiple Sensors ◽  
Industry Standard

The Unila Internet of Things Research Group (UIRG) was developed online monitoring of power distribution system based on Internet of Things (IoT) technology on Department of Electrical Engineering University of Lampung (Unila), has been running for several months, this system monitored electrical quantities of 3-phase main distribution panel of H-building. The measurement system involve multiple sensors such current sensors and voltage sensors, the measurement data stored in to database server and shown the information in a real-time through a web-based application.Main objective of this research was to capture, analyze, and identified the knowledge pattern of electrical quantities data measurements, using Cross-Industry Standard Process for Data Mining (CRISP-DM) data mining framework, for helping the stake holders to continuous improvement of the quality of electricity services, the initial research limited to total 770847 electrical quantities recorded data that save on database system, since 1 September - 31 October 2018, the dataset consist of 21 attribute electrical quantities such as; voltage, current, power factor values, energy consumption, frequency, on H building 3-Phase main panel control.Rapidminer as leading application on knowledge discovery application was used to analyze the big data, K-Mean cluster algorithm implemented to identify the data pattern, the result indicated that 3-Phase load was unbalanced, and Phase-0 was the most utilized phase, based on from total 5 cluster analysis result. 

LiMCA: an optimal clustering algorithm for lifetime maximization of internet of things

2018 ◽  
Vol 25 (8) ◽  
pp. 4459-4477 ◽  
Author(s):  
Subir Halder ◽  
Amrita Ghosal ◽  
Mauro Conti
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Lifetime Maximization
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