2019 15th International Conference on Electronics, Computer and Computation (ICECCO)最新文献_第10页

Enhanced Decision Tree-J48 With SMOTE Machine Learning Algorithm for Effective Botnet Detection in Imbalance Dataset 基于SMOTE机器学习算法的增强决策树- j48在不平衡数据集中的有效僵尸网络检测

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043233

Ilyas Adeleke Jimoh, I. Ismaila, M. Olalere

{"title":"Enhanced Decision Tree-J48 With SMOTE Machine Learning Algorithm for Effective Botnet Detection in Imbalance Dataset","authors":"Ilyas Adeleke Jimoh, I. Ismaila, M. Olalere","doi":"10.1109/ICECCO48375.2019.9043233","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043233","url":null,"abstract":"Botnet is one of the major security threats in the field of information technology today (IT). The increase in the rate of attack on industrial IT infrastructures, theft of personal data and attacks on financial information is becoming critical. Majority of available dataset for botnet detection are very old and may not be able to stand the present reality in this research area. One of the latest dataset from Canadian Institute of Cyber Security labeled “CICIDS2017” was noted as an imbalance data distribution ratio of 99% to 1%. This distribution represents majority to minority class ratio. This may pose a challenge of over-fitting in majority class to the research and create a bias in the analysis of results. This research work has adopted J48 decision tree machine learning algorithm with application of SMOTE technique in solving the problem of imbalance dataset, thereby leading to an improved detection of botnets. The accuracy of the highest scenario was 99.95%. This is a significant improvement in detection rate compare to the previous research work.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132702679","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: 8

A Fuel Cell technology approach to generate electricity using air-conditioner water 一种利用空调水发电的燃料电池技术

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043268

E. A. Aja, Sadiq Thomas, Oluwatomisin E. Aina, I. D. Inuwa, B. Saka, Y. S. Mohammed

引用次数: 0

Deep Learning for Image-based Cervical Cancer Detection and Diagnosis — A Survey 基于图像的深度学习宫颈癌检测与诊断研究综述

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043220

Oluwatomisin E. Aina, Steve A. Adeshina, A. Aibinu

引用次数: 9

Mitigating Advanced Persistent Threats Using A Combined Static-Rule And Machine Learning-Based Technique 使用组合静态规则和基于机器学习的技术减轻高级持续性威胁

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043278

Oluwasegun Adelaiye, A. Ajibola

引用次数: 1

Classification of handwritten names of cities using various deep learning models 使用各种深度学习模型对手写城市名称进行分类

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043266

Nurseitov Daniyar, B. Kairat, Kanatov Maksat, Alimova Anel

{"title":"Classification of handwritten names of cities using various deep learning models","authors":"Nurseitov Daniyar, B. Kairat, Kanatov Maksat, Alimova Anel","doi":"10.1109/ICECCO48375.2019.9043266","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043266","url":null,"abstract":"The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123893400","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: 5

Perspectives of Integration QR Codes and RFID readers in large-scale events controlled by HRM systems 在人力资源管理系统控制的大型事件中整合QR码和RFID阅读器的观点

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043285

N. Ibragimov, Alina Amangeldiyeva

引用次数: 1

ANDROID APPLICATIONS MALWARE DETECTION: A Comparative Analysis of some Classification Algorithms ANDROID应用恶意软件检测:几种分类算法的比较分析

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043284

Oluwaseyi Olorunshola, Ayanfeoluwa Oluyomi

{"title":"ANDROID APPLICATIONS MALWARE DETECTION: A Comparative Analysis of some Classification Algorithms","authors":"Oluwaseyi Olorunshola, Ayanfeoluwa Oluyomi","doi":"10.1109/ICECCO48375.2019.9043284","DOIUrl":"https://doi.org/10.1109/ICECCO48375.2019.9043284","url":null,"abstract":"The usage of the Android Operating System (OS) has surpassed all other operating systems and as a result, it has become the primary target of attackers. Many attacks can be geared towards Android phones mainly using application installation. These third-party applications first seek permission from the user before installation. Some of the permissions can be elusive evading the users’ attention. With the type of harm that can be done which include illegal extraction and transfer of the users’ data, spying on the users and so on there is a need to have a heuristic approach in the detection of malware. In this research work, some classification algorithms were tested to determine the best performing algorithm when it comes to the detection of android malware detection. An android application dataset was obtained from figshare and used in the Waikato Environment for Knowledge Analysis (WEKA) for training and testing, it was measured under accuracy, false-positive rate, precision, recall, f-measure, Receiver Operating Curve (ROC) and Root Mean Square Error (RMSE). It was discovered that multi-layer perceptron performs best with an accuracy of 99.4%.","PeriodicalId":166322,"journal":{"name":"2019 15th International Conference on Electronics, Computer and Computation (ICECCO)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121343085","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

Improvement Of Gravity Light Generation Modeling Using Bond Graph Method 基于键合图法的重力光生成建模改进

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043217

Sadiq M. Bammami, E. Okafor, S. Hussein, M. Bammami, Sadiq Thomas, Omotayo Oshiga

引用次数: 0

Developing a Digital Interactive Course Material on Automated Management System Moodle for Integral Equations Course “积分方程”课程自动化管理系统模型之数位互动教材开发

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043240

Isa Muslu, Moussa Mahamat Boukar, O. Gurbuz

引用次数: 0

An Investigation into Peer-to-Peer Network Security Using Wireshark 基于Wireshark的点对点网络安全研究

2019 15th International Conference on Electronics, Computer and Computation (ICECCO) Pub Date : 2019-12-01 DOI: 10.1109/ICECCO48375.2019.9043236

A. Musa, A. Abubakar, Usman Abdul Gimba, Rasheed Abubakar Rasheed

引用次数: 2