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A Spatial-Temporal Features Based Fingerprinting Method for Machine tools in DNC Networks

A Spatial-Temporal Features Based Fingerprinting Method for ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zhongfeng Jin Nan Li Chao Liu Meimei Li Shaohua An Weiqing Huang School of Cyber Security No.19 yuquan road shijingshan district University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences E-park No.80 xingshikou road haidian district Institute of Information Engineering Beijing China

ISBN: (纸本)9781728176505

As the most widely used solution for intelligent manufacturing, the Distributed Numerical Control (DNC) system faces with various novel attack opportunities caused by increasing connection to industrial ethernet and external interfaces. Attackers can easily fabricate packets between machine tools and Machine data Collection (MDC) server. This may threaten not only processing tasks and production logic but also physical safety and business efficiency. Intrusion Detection System (IDS) is seen as a promising solution to these problems. However, existing device identifier used by IDS (e.g. IP and MAC) can be forged easily. In order to address this problem, we propose a fingerprinting method (Spatial-Temporal Feature Fingerprint, STFF) enabling identification of machine tools. The fingerprint is a vector composed of spatial and temporal features extracted from payload grayscale image, which is constructed with all payloads in a polling period. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network are used to extract spatial and temporal features from payload grayscale image respectively. Finally, a real-world unencrypted data set with two vendors (three models for each) of machine tools is used to verify the performance of the fingerprinting method. Our evaluation experiments show that the STFF could be a unique identifier to describe the communication relationship between machine tools and MDC server.

关键词： Production Fingerprint recognition Gray-scale Feature extraction Machine tools Safety Servers

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Employing machine learning techniques on sentiment analysis of google play store bangla reviews 22

Employing machine learning techniques on sentiment analysis ...

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22nd international conference on Computer and Information Technology, ICCIT 2019

作者： Jawad Soumik, Md Muhtasim Salvi Md Farhavi, Syed Eva, Farzana Sinha, Tonmoy Alam, Mohammad Shafiul Ahsanullah University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728158426

This article offers an in-depth insight on a number of existing methodologies to perform sentiment analysis using text classification on Bangla dataset. Although the rapidly developing machine learning algorithms are showing promising results, the viability of those methods for non-English languages such as Bangla is yet to be fully explored. This research aims to fill in some of those existing research gaps through proper implementation of machine learning techniques where words are converted into feature vectors via implementation of TF-IDF algorithm on data crawled from Google play store, the largest Android application market. Many significant algorithms staring linear algorithms like Naïve Bayes, Linear Support Vector Machine (SVM) are implemented. An in-depth comparison is also made among the results of various existing algorithms. The experimental results indicate that even the base-line algorithms, after proper pre-processing, can show promising results on our Bangla dataset. Naïve Bayes, Support Vector Machine and Logistic Regression has shown very promising results (accuracy score of 0.75 on average) even with the data limitation. An Ensemble method is also proposed with Adaptive Boosting technique showing an accuracy score of 0.7639 with five-fold applied. SVM has the best accuracy score of 0.7648 among all the algorithms when five-fold is applied and Gradient Boosting has the best accuracy score of 0.7695 when five-fold is not applied. © 2019 IEEE.

关键词： Sentiment analysis

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MTLH: Video QoE Monitoring for Encrypted Traffic via Multi-Task learning with Hierarchy

MTLH: Video QoE Monitoring for Encrypted Traffic via Multi-T...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Lei Xu Gang Xiong Gaopeng Gou Jie Chen Tianyu Cui Tianyi Shan Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China University of California San Diego

ISBN: (纸本)9781728176505

With the continuous growth of video traffic on the whole Internet, it is of vital importance for content providers to provide high-quality service. Monitoring the QoE (Quality of Experience) of video can help them to improve their service. However, most of the video traffic is encrypted traffic which has unreadable payload and less information in the transport layer that makes monitoring QoE a challenging task. QoE is measured from multiple metrics and many methods have been proposed to infer it. Traditional machine learning based methods have two main shortcomings. 1) These methods ignore the relevance of these metrics which can do a favor to the inference of QoE. 2) They need to extract the transport layer features and statistic features manually. In this paper, we propose multi-task learning with hierarchy (MTLH) model for inferring video QoE metrics. We use shared layer to extract deep features automatically from traffic tendency pictures which reveal the variation tendency of video streaming, that characterizes video QoE. In addition, we utilize the hierarchy to promote fine-grained start-up delay tasks. Finally, we use multitask learning architecture to learn the correlation between each task and use their correlation to refer QoE metrics. We evaluate our model for YouTube adaptive video streams on two datasets. The proposed approach achieved an average 9.0% higher of precision score and an average 11.5% higher of recall score than the state-of-the-art method in two datasets.

关键词： Measurement Correlation Streaming media Feature extraction Delays Quality of experience Cryptography

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Context-Aware learning for Anomaly Detection with Imbalanced Log data

Context-Aware Learning for Anomaly Detection with Imbalanced...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Peijie Sun E Yuepeng Tong Li Yulei Wu Jingguo Ge Junling You Bingzhen Wu Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

ISBN: (纸本)9781728176499;9781728176505

Logs are used to record runtime states and significant events for a software system. They are widely used for anomaly detection. Logs produced by most of the real-world systems show clear characteristics of imbalanced data because the number of samples in different classes varies sharply. The distribution of imbalanced data makes the anomaly classifier bias toward the majority class, so it is difficult for a classifier to learn to detect anomalies correctly. Most existing methods for log-based anomaly detection ignore this important problem, so they perform poorly on real-world systems. In this paper, we propose a context-aware method named AllContext for anomaly detection with imbalanced log data. AllContext transforms each log event into a vector, which contains not only the semantic information of each word, but also the semantics of the region where each word is located. Such rich semantic information enables our method to understand the imbalanced log data better and deeper. We conduct extensive experiments on multi-class and binary imbalanced log datasets. The accuracy of the proposed AllContext is more than twofold of that by a baseline state-of-the-art. To evaluate the robustness of the proposed method, we assess AllContext on the imbalanced unseen log data, where all samples in the test dataset do not exist in the training dataset, and the accuracy achieved by AllContext reaches 0.98. The experiments show that the proposed solution achieves accurate results on both imbalanced and unseen log data.

关键词： Training Context-aware services Runtime High performance computing Semantics Transforms Software systems

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Privacy-Preservation in Distributed Deep Neural Networks via Encryption of Selected Gradients

Privacy-Preservation in Distributed Deep Neural Networks via...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Antwi-Boasiako Emmanuel Shijie Zhou Yongjian Liao Qihe Liu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728176505

In this paper, privacy preservation in Distributed Deep Neural Networks (DDNN) is re-visited. We focus on the use of Homomorphic Encryption (HE) in the DDNN. Although HE provides some level of effective way to bring privacy to the DDNN, its use is usually associated with high overheads, especially communication cost between the Honest-but-Curious (HbC) cloud server and participants. To this end, we propose an alternative approach for privacy preservation in the DDNN. Ability to select and encrypt selected gradients homomorphically, is the main appeal of this paper and has proven to provide good privacy, reduce communication cost to a considerable level (ie. 0.04MB and 0.13MB when 1% and 10% of total gradients are selected, encrypted and shared respectively) whilst maintaining acceptable level of accuracy. A learning with error (LWE) based additive Homomorphic Encryption is used and comparison is made with closely related works to show how efficient our approach is. Furthermore, we propose the use of non-malleable code to protect the integrity of ciphertexts that are transmitted between participants and the HbC cloud server. This paper is the first to combine partial sharing and Homomorphic Encryption in the DDNN.

关键词： Training Privacy Additives High performance computing conferences Neural networks Encryption

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Emotional Health: A data driven approach to understand our emotions and improve our health 22

Emotional Health: A data driven approach to understand our e...

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22nd IEEE international conference on Computational Science and engineering (IEEE CSE) / 17th IEEE international conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Vuppalapati, Chandrasekar Kedari, Sharat Ilapakurti, Anitha Kedari, Santosh Shankar, Jaya Hanumayamma Innovat & Technol Inc 628 Crescent Terrace Fremont CA 94536 USA Hanumayamma Innovat & Technol Inc Hig 2Block 2Flat 7 Hyderabad 500044 TN India

ISBN: (纸本)9781728116648

Human emotions play an important role in everyone's daily life. People who have good emotional health pose to have good health. However, many things that happen in our life can disrupt our emotional health. These can lead to strong feelings of sadness, stress, or anxiety. In today's world many things that happen in our life can disrupt our emotional health. Emotions are also associated with problematic changes in the cardiovascular, hormonal and immune system that may lead to allergies, headache, migraine, Arthritis, blood pressure, diabetes and may penetrate its way to the heart which gives you heart attacks. In fact, recent studies show, for over 40 million people in the United States have been diagnosed with depression, the number of people coping (or trying to cope) with loneliness is unknown, and that feeling of not belonging is depressing all by itself. Emotions like anger, fear, Disgust triggers strong sensations in the upper body. Other emotions like depression, sadness and shame shows diminished sensation in the lower body, while some emotions combined both type of response. Though emptions are created in the brain, they can be manifest as physical feelings throughout the body. The most common way to understand human emotion is by capturing and understand signals provided by the human body. With help of IoT, NLP, Machine learning, Social Analytics and Artificial Intelligence we can capture and understand physiological signals and develop an emotion recognition system based on information provided by physiological signals.

关键词： Computer Vision Electronic Health Records EHR Mobile Sensors Voice Service APIs Amazon Alexa Apple Siri Kit Microsoft Cortana Internet of Things IoT CEP Complex Event Processing Health iOS Android IoT architecture real-time stream analytics Decision ID3 Tree Sanjeevani Electronic Health Records Machine learning Qualitative Metrics Outpatient

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Usage identification of anomaly detection in an industrial context 22

Usage identification of anomaly detection in an industrial c...

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22nd international conference on engineering Design, ICED 2019

作者： Zoghlami, Firas Kurrek, Philip Jocas, Mark Masala, Giovanni Salehi, Vahid University of Applied Sciences Munich Germany University of Plymouth United Kingdom

The use of flexible and autonomous robotics systems is the solution for the automation task of the production and intra-logistics environments. This dynamic context requires the robot to be aware of its surroundings through the whole task, also after accomplishing the gripping action. We present an anomaly detection approach based on unsupervised learning and reconstruction fidelity of image data. We design our method to enhance the dynamic environment perception of robotics systems and apply it in a palletizing robot, in order to perceive and detect changes to its surrounding and process after the gripping step. Our proposed approach achieves the performance targeted by the considered industrial requirements. © 2019 Design Society. All rights reserved.

关键词： Machine learning

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data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network 22

Data-driven Fault Diagnosis of Induction Motors Using a Stac...

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22nd international conference on Electrical Machines and Systems, ICEMS 2019

作者： Skylvik, Audun Johannessen Robbersmyr, Kjell G. Khang, Huynh Van University of Agder Department of Engineering Sciences Grimstad4879 Norway

ISBN: (纸本)9781728133980

Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch's method for spectral density estimation. Further, several deep learning features are extracted using stacked autoencoder networks. The proposed scheme can predict the faults at high accuracy while reducing expertise demand. The proposed method is validated on an in-house laboratory setup under variable speed and load conditions, showing a classification accuracy over 95%. Furthermore, multiple support vector machine and k-nearest neighbor classifiers are tested with the same data for a comparative study. Finally, the performance of di erent fault classifiers are evaluated based on accuracy, computational burden and classification consistency. © 2019 IEEE.

关键词： Induction motors

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learning Believable Player Movement Patterns from Human data in a Soccer Game

Learning Believable Player Movement Patterns from Human Data...

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international conference on Advanced Communication Technology (ICACT)

作者： Victor Khaustov Maxim Mozgovoy Active Knowledge Engineering Lab University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9791188428045

ISBN: (纸本)9781728149431

Player movement patterns are one of the behavioral traits immediately visible to an observer. Thus, a soccer AI system striving for believable (human-like) behavior must ensure the believability of player movements. We show how tracking data of real human players in soccer can be used to create a case-based reasoning AI system, able to simulate realistic player movements in a computer soccer game. Our results are confirmed with a direct comparison of actions made by AI-controlled players and professional athletes.

关键词： Games Artificial intelligence Estimation Tracking Decision making Task analysis

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22nd international conference on Business Information Systems, BIS 2019

22nd International Conference on Business Information System...

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22nd international conference on Business Information Systems, BIS 2019

ISBN: (纸本)9783030204815

The proceedings contain 67 papers. The special focus in this conference is on Business Information Systems. The topics include: Potential Benefits of New Online Marketing Approaches;using Blockchain Technology for Cross-Organizational Process Mining – Concept and Case Study;modeling the Cashflow Management of Bike Sharing Industry;predicting Material Requirements in the Automotive Industry Using data Mining;are Similar Cases Treated Similarly? A Comparison Between Process Workers;mining Labor Market Requirements Using Distributional Semantic Models and Deep learning;enhancing Supply Chain Risk Management by Applying Machine learning to Identify Risks;deep Neural Networks for Driver Identification Using Accelerometer Signals from Smartphones;dynamic Enterprise Architecture Capabilities: Conceptualization and Validation;re-engineering Higher Education learning and Teaching Business Processes for Big data Analytics;keyword-Driven Depressive Tendency Model for Social Media Posts;real-Time Age Detection Using a Convolutional Neural Network;an Inventory-Based Mobile Application for Warehouse Management to Digitize Very Small Enterprises;collaboration in Mixed Homecare – A Study of Care Actors’ Acceptance Towards Supportive Groupware;Stress-Sensitive IT-Systems at Work: Insights from an Empirical Investigation;data Quality Management Framework for Smart Grid Systems;balancing Performance Measures in Classification Using Ensemble learning Methods;machine learning for engineering Processes;exploring Interactions in Social Networks for Influence Discovery;a Literature Review on Application Areas of Social Media Analytics;personalized Cloud Service Review Ranking Approach Based on Probabilistic Ontology;influential Nodes Detection in Dynamic Social Networks;a Fuzzy Modeling Approach for Group Decision Making in Social Networks;system Modeling by Representing Information Systems as Hypergraphs.

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