2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)最新文献_第4页

IoT based Post Crash Assistance System 基于物联网的事故后援助系统

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142894

Neel Desai, P. Kulkarni, L. Joy, Prachi Raut

引用次数: 0

Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier 基于KNN和ANN分类器的认知无线网络主用户仿真攻击(PUEA)精确检测

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143015

Mohammad Azharuddin Inamdar, H. V. Kumaraswamy

{"title":"Accurate Primary User Emulation Attack (PUEA) Detection in Cognitive Radio Network using KNN and ANN Classifier","authors":"Mohammad Azharuddin Inamdar, H. V. Kumaraswamy","doi":"10.1109/ICOEI48184.2020.9143015","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9143015","url":null,"abstract":"Performance of a cognitive radio network (CRN) can be degraded by a primary user emulation attack (PUEA). Cognitive Radio (CR) is a potential answer for radio spectrum inefficiency issue. Primary user emulation (PUE) assault is a genuine risk to cognitive radio systems. This problem can be eliminated by disconnecting malicious user from base station after classification process. In this work, K nearest neighbor classifier (KNN) is used to classify the malicious users. KNN is trained by using parameters such data rate, distance, power, frequency of request etc. Also, proposed work is compared with artificial neural network (ANN) which is trained by the same parameters used for KNN training. Security of the network is improved by using Elliptical Curve Cryptography (ECC) as data encryption. Trained classifier can detect the emulating users with high accuracy due to significant parameter selection. To validate the performance, accuracy and sensitivity analysis are carried out, simulation results show that the proposed work performs better in terms of accuracy as compared to that of conventional PUEA classification techniques.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Machine Learning based anomaly detection for IoT Network: (Anomaly detection in IoT Network) 基于机器学习的物联网网络异常检测:(物联网网络异常检测)

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142921

N. K. Sahu, I. Mukherjee

{"title":"Machine Learning based anomaly detection for IoT Network: (Anomaly detection in IoT Network)","authors":"N. K. Sahu, I. Mukherjee","doi":"10.1109/ICOEI48184.2020.9142921","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142921","url":null,"abstract":"As the world is leading towards having everything smart, like smart home, smart grid smart irrigation, there is the major concern of attack and anomaly detection in the Internet of Things (IoT) domain. There is an exponential increase in the use of IoT infrastructure in every field leads to an increase in threats and attacks too. There can be many types of possible attacks and anomaly that can affect the IoT system which can lead to failure of the IoT system. In this paper, different anomalies are predicted based on a different feature in the data set. Two machine learning classification models are used and comparisons between the performance of these used models are shown. Logistic regression and artificial neural network classification algorithms are applied. Since there are more than 3.5 lakh data set, two different approaches are experimented. In the first case, the classification algorithm stated above is applied on the whole 3.5 lakh dataset, and in the second case, all the classification algorithms are applied after omitting the feature “value” having data as 0 and 1. Data is divided into two sets, training and test set where the training set is 75% of total data available and the rest are test set, 99.4% accuracy is obtained for ANN for the first case while 99.99% accuracy is obtained for the algorithm stated above for the second case. This work can be used for identifying threats and anomaly occurring in a smart device and IoT solutions and prevent attacks.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Real-Time Garbage, Potholes and Manholes Monitoring System using Deep Learning Techniques 使用深度学习技术的实时垃圾、坑洞和人孔监测系统

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143030

S. Sayyad, Shaily Parmar, Mrinalini Jadhav, Karan Khadayate

{"title":"Real-Time Garbage, Potholes and Manholes Monitoring System using Deep Learning Techniques","authors":"S. Sayyad, Shaily Parmar, Mrinalini Jadhav, Karan Khadayate","doi":"10.1109/ICOEI48184.2020.9143030","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9143030","url":null,"abstract":"A major challenge in urban cities is waste management, as the pace of urbanization is growing rapidly sustainable urban development strategies are therefore required. One of the main concerns with our environment has been improper garbage containment, unrepaired potholes, open manholes, and stagnant water which are harmful to the well-being of the residents. Since the idea of smart cities is very trendy these days and without a smart waste management system, smart cities can't be complete. Traditional manual monitoring is a cumbersome process and utilizes more effort, time, and cost which can easily be assisted with present technologies. Monitoring such tasks using IoT is one such solution, but incorporating IoT deals with the use of sensitive electronic devices which are difficult to maintain and hence add on some additional costs. Hence this paper describes the development of a system using deep learning techniques which can cut down huge costs in effectively monitoring the surroundings and lend a helping hand for a better, safer present and future.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122600578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

IoT and Image Processing based Forest Monitoring and Counteracting System 基于物联网和图像处理的森林监测与抵消系统

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142996

S. Pareek, S. Shrivastava, Sonal Jhala, J. Siddiqui, Savitanandan Patidar

引用次数: 6

Optimizing a New Intrusion Detection System Using Ensemble Methods and Deep Neural Network 基于集成方法和深度神经网络的新型入侵检测系统优化

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143028

A. Rai

引用次数: 8

An IoT based Smart Monitoring System for Vehicles 基于物联网的车辆智能监控系统

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142936

Christy Mary Jacob, Nikhil George, Amul Lal, Roshan Jacob George, Merin Antony, Jineeth Joseph

{"title":"An IoT based Smart Monitoring System for Vehicles","authors":"Christy Mary Jacob, Nikhil George, Amul Lal, Roshan Jacob George, Merin Antony, Jineeth Joseph","doi":"10.1109/ICOEI48184.2020.9142936","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9142936","url":null,"abstract":"There is increased adoption of penalty and fine for traffic rule violators in the public sector but there is a tendency for people to evade from those imposed fines and restrictions for their own safety. Our system will completely monitor all the traffic violations namely over speeding, rash driving, drunken driving, driving without a seat belt, and so on right from the starting of the car. There is an increasing demand to develop a system to check passengers without coming out of the vehicle. A new system for the police force to check the vehicle's details with a smart device placed in the vehicle. The device is equipped with speed monitoring, Alcohol detection, Seat belt checking, etc. If any violation is detected the controller sends an emergency data to the cloud, thus the vehicle is in continuous monitoring mode, and RTO will get updates about the vehicles which are violating rules. Alcoholic breath sensor will continuously monitor the driver's breath, speed sensor will be connected with the speedometer and checks for over speeding, Seat belt sensor will warn the driver if he/she is not using the seat belt, vehicle details including license, pollution details, insurance, etc. will be uploaded to the server or cloud. If any of the above things are violated, automatically defaulter will be imposed fines and the details will be sent to the Motor vehicle department.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121770660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Automatic Skimming of Web Pages on a Single click Efficiently 自动浏览网页在一个单一的点击有效

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143003

Arnab Dey, Sudhanshu Jain

引用次数: 3

Ensemble learning-based approach for crack detection using CNN 基于集成学习的CNN裂纹检测方法

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9143035

Vibhu Kailkhura, S. Aravindh, S. Jha, N. Jayanthi

{"title":"Ensemble learning-based approach for crack detection using CNN","authors":"Vibhu Kailkhura, S. Aravindh, S. Jha, N. Jayanthi","doi":"10.1109/ICOEI48184.2020.9143035","DOIUrl":"https://doi.org/10.1109/ICOEI48184.2020.9143035","url":null,"abstract":"Crack detection is of pivotal importance in civil engineering and other related applications. Traditional methods of human inspection are tedious and severely limited. Automated crack detection by conventional image processing techniques is challenging due to their inability to discriminate crack features from background noise. Inhomogeneous lighting, shadows, and surface finish hinder the performance of digital image processing methods. The use of convolutional neural networks has helped achieve remarkably better results in the field of computer vision. Ensemble learning is an approach to aggregate the results of a number of individual models for classification or regression. Ensemble learning for crack detection has been implemented using deep convolutional neural networks (DCNN) in this paper. The models are evaluated on a number of performance metrics, namely-(i) accuracy, (ii) precision, (iii) recall (iv) Matthews correlation coefficient (MCC), (v) AUROC, and (vi) F1 score. Experimental results show the robustness of the ensembling method and offer promising scope in crack detection. They outperform the current best performance on open source concrete crack dataset. The ensemble models achieved much better performance than their individual counterparts with the best ensemble achieving a validation accuracy of 99.67%.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"22 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131903628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Performance Evaluation of 2X2 MIMO Reconfigurable Testbed Communication System 2X2 MIMO可重构试验台通信系统性能评价

2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) Pub Date : 2020-06-01 DOI: 10.1109/ICOEI48184.2020.9142969

Khushi Meherwan Asudaria, B. Venkateshulu

引用次数: 1