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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Applying Deep learning to Balancing Network Intrusion Detection datasets 11

Applying Deep Learning to Balancing Network Intrusion Detect...

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11th IEEE international conference on Advanced Infocomm Technology, ICAIT 2019

作者： Chuang, Po-Jen Wu, Dong-Ye Tamkang University Department of Electrical and Computer Engineering Tamsui25137 Taiwan

ISBN: (纸本)9781728147789

In this investigation, we apply deep learning to generate a desirable data generation model which helps to balance the network intrusion detection datasets and enhance the detection performance. the basic idea is to adopt deep variational autoencoders to generate new data and adjust the imbalance of datasets into more favorable balanced datasets. We then use the generated balanced datasets to reduce the deviation of classifiers (incurred by data imbalance) in training and, as a result, to enhance the performance of intrusion detection. As experimental results exhibit, in comparison to unbalanced datasets, balanced datasets can practically lead to better classification accuracy, especially when facing unknown attacks. Balanced datasets also help to solve the over-fitting problem (due to imbalance of datasets) in intrusion detection model training, to ensure that the trained intrusion detection model will not misjudge new types of data - even if they are not in the training dataset. © 2019 IEEE.

关键词： Intrusion detection

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A Deep learning Approach for Location Independent throughput Prediction 8

A Deep Learning Approach for Location Independent Throughput...

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8th IEEE international conference on Connected Vehicles and Expo (ICCVE)

作者： Schmid, Josef Schneider, Mathias Hoess, Alfred Schuller, Bjoern Ostbayr TH Amberg Weiden D-92224 Amberg Germany Univ Augsburg ZDB Chair Embedded Intelligence Hlth Care & Wellb D-86159 Augsburg Germany

ISBN: (纸本)9781728101422

Mobile communication has become a part of everyday life and is considered to support reliability and safety in traffic use cases such as conditionally automated driving. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is still a challenging task while on the move. Whereas most approaches in this research field rely on historical data measurements, mapped to the corresponding coordinates in the area of interest, this paper proposes a throughput prediction method that focuses on a location independent approach. In order to compensate the missing positioning information, mainly used for spatial clustering, our model uses low-level mobile network parameters, improved by additional feature engineering to retrieve abstracted location information, e.g., surrounding building size and street type. thus, the major advantage of our method is the applicability to new regions without the prerequisite of conducting an extensive measurement campaign in advance. therefore, we embed analysis results for underlying temporal relations in the design of different deep neuronal network types. Finally, model performances are evaluated and compared to traditional models, such as the support vector or random forest regression, which were harnessed in previous investigations.

关键词： throughput prediction deep learning cellular network

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intelligent data Analytics from the Energy Economics of the Greek Debt Crisis

Intelligent Data Analytics from the Energy Economics of the ...

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international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Maria D. Karasimou Vasileios Antonenas Vasileios Laitsos Vasileios Polychronides Lefteris H. Tsoukalas School of Nuclear Engineering Purdue University West Lafayette IN USA-Volos Greece School of Nuclear Engineering University of Thessally West Lafayette IN USA-Volos Greece

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

intelligent tools are brought to bear on the analysis of energy data from Greece in the period 2010-20. During this period a paradoxical phenomenon is observed namely, an increase in energy productivity in the midst of unprecedented economic decline as measured by Gross Domestic Product (GDP). Preliminary results identify improvements in the overall efficiency of capital due to the collapse in demand following draconian austerity measures. Background, directions and trends are analyzed, policy alternatives examined, and, a new measure of energy utilization with possible real-time applications is proposed.

关键词： Productivity data analysis Economic indicators Energy measurement Tools Market research Real-time systems

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A Survey Report on Identifying Different Machine learning Algorithms in Detecting Domain Generation Algorithms within Enterprise Network

A Survey Report on Identifying Different Machine Learning Al...

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international conference on Computing and Networking Technology (ICCNT)

作者： Sameer Ajmera T.R Pattanshetti Computer engineering College of Engineering Pune Pune India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Anomaly detection is done to identify the abnormal patterns that deviate from the majority of data. It is also known as outlier detection. Detection of Domain Generation Algorithms is also a type of anomaly detection. In this paper, we talked about Domain Generation Algorithms (DGA). Malware uses Domain Generation Algorithms to communicate with Command and Control (CnC) servers managed by hackers. As DGAs are generated randomly, it is hard to detect them in real-time using signature-based software. DGA activates for a short time. So threat intelligent software sometimes fails to recognize if the URL is DGA or genuine. In this paper, research is done in machine learning algorithms such as random forest and deep learning techniques such as LSTM and neural networks which will help to detect patterns of DGA. Using the DGA technique malware starts communicating with this server. As this is a type of advanced persistent threat, this results in intellectual and financial losses of enterprises. the traditional techniques consist of blacklisting malware and URLs. Automatic detection of this DGAs is a crucial task. this imposes an overheard as DGA gets generated in large volumes.

关键词： Malware Servers Software algorithms Command and control systems Blacklisting Prediction algorithms Botnet

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Robust Compression Technique for YOLOv3 on Real-Time Vehicle Detection 11

Robust Compression Technique for YOLOv3 on Real-Time Vehicle...

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11th international conference on Information Technology and Electrical engineering (ICITEE)

作者： Krittayanawach, Nattanon Vateekul, Peerapon Chulalongkorn Univ Dept Comp Engn Fac Engn Big Data Analyt & IoT Ctr CUBIC Bangkok Thailand

ISBN: (纸本)9781728140551

For vehicle detection, YOLOv3 has shown promising accuracy. Since the number of parameters in this network can be more than ten million parameters, it cannot be fit into a commodity camera. In this paper, we propose a compression mechanism designed specifically for YOLOv3's network by removing unnecessary filters. Since YOLOv3 composes of two network components: backbone and pyramid networks, we propose a robust pruning mechanism to prune filters of each network separately. this can help to avoid overpruning the network in some part of the model making our model more robust. there are two main pruning criteria investigated: Average Percentage of Zero (APoZ) and Sum Magnitude Weight. the experiment was conducted on UA-DETRAC. the results show that our compression mechanism with APoZ criterion can reduce more than 90% of the network size, while the accuracy is even higher than the full model for about 2%.

关键词： Vehicle detection Deep learning Pruning Object detection

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Rule induction algorithms applied in educational data set 11

Rule induction algorithms applied in educational data set

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11th international conference on Computer Supported Education, CSEDU 2019

作者： Carvalho, Walisson Nobre, Cristiane Zarate, Luis Centro Universitario Una Belo Horizonte Brazil Pontificia Universidade Catolica de Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9789897583674

this article presents the application of three induction rules algorithm: OneR, RIPPER and PART in an educational data set aiming to explain the main factors that lead students to be succeed or failure in online course. the dataset used to develop this article was extracted from the log of activities of engineering students that enrolled in a 20 weeks course of Algorithm offered online. the students used learning Management System, Moodle. the dataset was preprocessed and then it was applied the algorithms into it. As result it was observed that students who begin earlier an assignment improve their probability of succeed. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Classification (of information)

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A Deep Transfer learning Approach for Seizure detection using RGB features of Epileptic Electroencephalogram Signals 11

A Deep Transfer Learning Approach for Seizure detection usin...

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11th IEEE Int Conf on Cloud Comp Technol and Sci (CloudCom) / 19th IEEE Int Conf Comp and Informat Technol (CIT) / 2019 Int Workshop on Resource Brokering with Blockchain (RBchain) / Asia-Pacific Serv Comp Conf (APSCC)

作者： Agrawal, Anupam Jana, Gopal Chandra Gupta, Prachi Indian Inst Informat Technol Allahabad IIITA Dept Informat Technol Interact Technol & Multimedia Res Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9781728150116

this paper demonstrates an approach based on Deep Transfer learning for the classification for Seizure and Non-seizure Electroencephalogram (EEG) signals. Recognizing seizure signals in intelligent way is quite important in clinical diagnosis of Epileptic seizure. Various traditional and deep machine learning techniques are employed for this purpose. However, the Epileptic seizure prediction and classification performance is not satisfactory over small EEG dataset using traditional approaches. the Transfer learning approach overcomes this by reusing the pre-trained networks such as googlenet, resnet101 and vgg19 trained on large Image database. this experiment has been done in two phases: (1) RGB image dataset generated for the seizure and non-seizure EEG signals data of University of Bonn using a novel preprocessing technique, (2) we configured googlenet, resnet101 and vgg19 trained networks to learn a new pattern or features from the RGB image dataset and finally, above mentioned networks have been used for the classification. the use of Vgg19 network shows greater accuracy among the three but takes comparatively more prediction time. We will mainly emphasize on the results obtained from the googlenet, since it provides effective accuracy taking less time for prediction. the proposed method achieved an accuracy of above 99% for a smaller number of epochs and maximum accuracy of 100% when we increase number of epochs. Experimental outcomes show the proposed approach using googlent achieved better performance w.r.t to many state-of-the-art classification algorithms even on the small EEG dataset. In addition, classification performance of our proposed approach has compared with different traditional machine learning techniques over the same input data.

关键词： Transfer learning Electroencephalogram Signal Epileptic Seizure Deep learning googlenet resnet101 vgg19

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Air Big data Outlier Detection Based on Infinite Gauss Bayesian and CNN 19

Air Big Data Outlier Detection Based on Infinite Gauss Bayes...

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11th international conference on Machine learning and Computing (ICMLC)

作者： Zhou, LiangQi Xu, HongZhen Wei, Li Zhang, Quan Zhou, Fei Li, ZhuoPei East China Univ Technol Engn Lab Radioact Geosci & Big Data Technol Nanchang Jiangxi Peoples R China East China Univ Technol Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781450366007

Air quality has always been a hot issue of concern to the people, the environmental protection department and the government. Among the massive air quality data, abnormal data can interfere with subsequent experiments and analysis. therefore, it is necessary to detect abnormal data to improve the accuracy of the data. However, traditional air outlier detection methods require at least one year's data to make inferences about air quality. this paper firstly analyzes the characteristics of air quality big data, and then proposes a framework based on Bayesian non-parametric clustering, namely Dirichlet Process (DP) clustering framework, to realize the outlier detection of air quality. the framework optimizes Gaussian mixture model into infinite Gaussian mixture model according to the results of data analysis, and uses neural network to cluster the data processed by infinite Gaussian mixture model, which effectively improves the clustering accuracy and avoids the need of collecting a large number of training data.

关键词： Air quality outlier detection Bayesian clustering Dirichlet process neural Network

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Research on intelligent Linkage Server Switch in Case of Power Loss in Computer Room

Research on Intelligent Linkage Server Switch in Case of Pow...

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IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Tao Zeng Shibing Wang Shuying Liu Fuyang Normal University Anhui Fuyang China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

the computer room of the information center is a centralized place for university data. Once a power failure occurs, it will have a great impact on the entire school. In this paper, an intelligent management scheme is designed for the situation of power loss and call in the computer room. Firstly, the IP address and data type correspondence of each server in the university are calculated to provide conditions for the intelligent opening of the server. then, according to the importance of the server, it is divided into three categories. When the computer room is powered off, the power supply is provided to different types of servers according to the remaining power of the UPS power supply. When the computer room comes in, the main server controls the automatic startup of the other three types of servers, the theory has proved that the scheme has a certain feasibility and can effectively avoid the loss of important data in the first time when the power is cut off.

关键词： Power supplies Switches Maintenance engineering Servers Personnel Uninterruptible power systems Software engineering

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Concept of a Machine learning supported Cross-Machine Control Loop in the Ramp-Up of Large Series Manufacturing

Concept of a Machine Learning supported Cross-Machine Contro...

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international conference on Mechanical and intelligent Manufacturing Technologies (ICMIMT)

作者： Moritz Meiners Jörg Franke Institute for Factory Automation and Production Systems (FAPS) Friedrich-Alexander-University Erlangen-Nuremberg Nuremberg Germany

ISBN: (数字)9781728153322

ISBN: (纸本)9781728153339

With the advancing digitalization of production plants, it becomes possible to use process data across machine boundaries. A machine can adapt its parameters to another machine-measured parameter to increase product quality. the present paper describes the design of an inter-machine control loop with machine learning techniques in order to improve the final quality output. the production ramp-up represents a special application case for this since at this point of time there is only limited knowledge about cause-effect relationships. For this purpose, the paper presents a method for analyzing these interrelations. On the one hand, simple linear regression is used to analyze the linear relationships; on the other hand, machine learning algorithms are used to analyze non-linear relationships. Two independent control loops form the overall control loop, which is capable of deriving holistic prognoses on upstream or downstream process effects.

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