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A Secure RPL Rank Computation and Distribution Mechanism for Preventing Sinkhole Attack in IoT-based systems

A Secure RPL Rank Computation and Distribution Mechanism for...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Alekha Kumar Mishra Deepak Puthal Asis Kumar Tripathy Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur India Center for Cyber-Physical Systems and Department of EECS Khalifa University Abu Dhabi UAE School of IT and Engineering VIT Vellore India

The sinkhole attack detection mechanisms that have been reported till date are distributed in nature. Defending a sinkhole attack requires protecting the act of forging the rank computation task. This paper propose a secure centralized mechanism to ensure protection of rank computation process against sinkhole nodes in the IoT-Based systems. The expectation maximization algorithm is used to optimally cluster the nodes into a group bearing the same rank. The distribution of ranks to the nodes is protected by the signature of the sink. The experimental results show that the optimal rank computation is secure against sinkhole nodes, however requires additional network communication between nodes and the sink node.

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Enabling A Network AI Gym for Autonomous Cyber Agents

Enabling A Network AI Gym for Autonomous Cyber Agents

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Li Li Jean-Pierre S. El Rami Adrian Taylor James Hailing Rao Thomas Kunz Defence Research & Development Canada Ottawa Canada Dept of Electrical and Computer Engineering Queen's University Kingston Canada Dept. of Systems & Computer Engineering Carleton University Ottawa Canada

This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL), is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S can train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E, showing full decision proficiency in the emulated “real” network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real in leveraging RL agents in real-world cyber networks.

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Applying Predictive Machine Learning Algorithms to Petroleum Refining Processes as Part of Intelligent Automation 23

Applying Predictive Machine Learning Algorithms to Petroleum...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Nekrasov, Ivan S. Tynchenko, Vadim S. Bukhtoyarov, Vladimir B. Kachaeva, Vera A. Bashmur, Kirill A. Sinitskaya, Aleksandra E. Siberian Federal Univercity Laboratory of Biofuel Composition Krasnoyarsk Russia Siberian Federal Univercity Dept. of Technological Machines and Equipment of Oil and Gas Complex Krasnoyarsk Russia Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Information-Control Systems Department Krasnoyarsk Russia

ISBN: (纸本)9781665498043

Intelligent automation is a term that can be applied to the more complex field of workflow automation, consisting of robotic workplace automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to improve efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training datasets, technical development, infrastructure, and expertise. But the potential benefits in terms of new ideas and financial development can increase significantly. The development of mechanized oil production in recent years has been accompanied by significant achievements in the field of digitalization. Machine learning, as an important element of digitalization, can successfully solve many production problems. The paper describes the application of some machine learning algorithms for solving the problem of classifying and predicting failures of hydrocracking process equipment that occur during oil refining and diesel fuel production. The application of random forest, principal component analysis and hyperparameter tuning methods is considered. The effectiveness of their application is compared. © 2022 IEEE.

关键词： Decision trees

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

Automatic Interaction and Activity Recognition from Videos o...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elena Merlo Marta Lagomarsino Edoardo Lamon Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.

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A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

A Neural Network-based System Identification Model to Predic...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba Olumide O. Oluleye Ayodele H. Ifijeh Victoria Oguntosin Olatayo M. Olaniyan Olubunmi Adewale Akinola Gbenga Afolabi Jacob O. Odetola. Abdultaofeek Abayomi Dept. of Electrical & Information Engineering Covenant University Ota Ogun State Nigeria HRA Institute for -Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal University Oye-Ekiti Ekiti State Nigereia Dept. of Information and Communication Technology Mangosuthu University of Technology Durban South Africa

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers.

关键词： Temperature measurement Renewable energy sources Voltage measurement Current measurement Weather forecasting Solar energy Predictive models

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Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Remaining Useful Life Prediction of Multi-Component System C...

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Chinese Automation Congress (CAC)

作者： Ke Li Xingle Fan Hui Shi Xiaohong Zhang Chaoli Sun Jianan Wang Dept. of Electronic and Information engineering Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and Technology Taiyuan China Dept. of Economics and Management Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and Technology Taiyuan China Dept. of Computer Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and Technology Taiyuan China

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method.

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study 28

Detecting Worker Attention Lapses in Human-Robot Interaction...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Dai, Zhuangzhuang Park, Jinha Kaszowska, Aleksandra Li, Chen School of Engineering and Applied Science Aston University Dept. of Computer Science Engineering and Physical Science BirminghamB4 7ET United Kingdom The Maersk Mc Kinney Moller Institute University of Southern Denmark Sdu Robotics Odense5230 Denmark Aalborg University Department of Electronic Systems Aalborg9220 Denmark Aalborg University The Department of Materials and Production Aalborg9220 Denmark

ISBN: (纸本)9798350335859

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness. © 2023 IEEE.

关键词： Eye tracking

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Solar Energy as a Substitute for Natural Gas in Residential Cooking

Solar Energy as a Substitute for Natural Gas in Residential ...

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International Conference for Convergence of Technology (I2CT)

作者： Mohammad Rasel Fahmida Amin Safkat Islam Shah Mohazzem Hossain Bishwajit Saha Department of High Voltage Substation Design and Quality Control PowerChina Hubei Electric Engineering Corporation Dhaka Bangladesh Department of High Voltage Substation Design and Quality Control Orient Energy Engineering Limited Merul Badda Dhaka Bangladesh Department of Electrical Engineering and Control Systems University of Grenoble Alpes Grenoble France Dept. of Electrical Electronic and Communication Engineering Military Institute of Science and Technology Bangladesh Dept. of Electrical and Electronic Engineering International University of Business Agriculture and Technology Bangladesh

Bangladesh, as a developing nation, faces persistent challenges in satisfying its energy demands, primarily due to its limited natural resource reserves, particularly natural gas. Natural gas is considered a crucial factor in the country’s socioeconomic development and serves as the main driving force for its industries and power plants. Nevertheless, its unregulated and extensive utilization poses a threat to the sustainability of all sectors, making it imperative to reduce its consumption. This study focuses on decreasing the consumption of natural gas in the domestic sector by advocating the adoption of induction cooking. In this context, the electrical energy needed for cooking will be derived from the readily accessible rooftop and floating photovoltaic (PV) installations within a particular community. This approach not only satisfies the community’s electrical energy requirements but also contributes to mitigating the country’s energy crisis to a certain extent.

关键词： Renewable energy sources Sociology Solar energy Production Surface roughness Rough surfaces Sustainable development

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Understanding Statistical Correlation of Application Security Vulnerability Data from Detection and Monitoring Tools

Understanding Statistical Correlation of Application Securit...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Santanam Kasturi Xiaolong Li John Pickard Peng Li Technology Management Indiana State University Terre Haute USA Dept. of Electronics and Computer Engineering Indiana State University Terre Haute USA Dept. of Technology Sys East Carolina University Greenville USA Dept. of Technology Systems East Carolina University Greenville USA

Vulnerability data gathered from multiple detection and monitoring capabilities at different layers of an application using a time-series analysis will provide value and insights by doing a statistical correlation with attack requests observed using a Web Application Firewall (WAF) monitoring solution. Static Analysis Security Testing, Software Composition Analysis, Dynamic Analysis Security Testing (DAST), Application Ethical Hack, Application Programming Interface (API) testing are the tests / scans that have been used to gather vulnerability data for this study. Correlations can further help track abnormal transaction paths if we follow specific ones pointed out by the statistical analysis for those requests that are not blocked by the rules and are allowed as valid transactions to pass through. This provides a narrowed down focus on the convergence of observability and security, critical to realizing a near-real time rapid action. Observations must continue for many days as a time series ensuring consistency and reliability in data collections and analysis. Multiple applications must be observed in a similar manner for ensuring validity of the process for analysis. Also, gathering sufficient data that is large enough to represent a reasonable population of web applications within an organization is a significant factor in achieving reliable correlation. Applying Pearson Correlation (or Spearman Correlation for distributions that are not normal) technique provides insight into Significance (two-tailed) as to whether a correlation is present over large number of data points. Results of analysis show evidence of correlations among specific attack requests monitored by the WAF and corresponding vulnerabilities in applications, detected using one or more methods. This is significant to looking for more insights into how these correlations can further explored into predicting attack patterns based on existing vulnerabilities.

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Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

Flight Trajectory Prediction Based on Hybrid-Recurrent Netwo...

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2021 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2021

作者： Schimpf, Nathan Knoblock, Eric J. Wang, Zhe Apaza, Rafael D. Li, Hongxiang University of Louisville Dept. of Electrical and Computer Engineering Louisville United States Nasa Glenn Research Center Communications and Intelligent Systems Division Cleveland United States

ISBN: (纸本)9781665412582

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostly rely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-recurrent design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicate the effectiveness of Long-Short Term Memory (LSTM) and GRU cells for state-of-the-art data processing (interpolation). Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value by improving predictions along a flight altitude;however, tailored products such as CIWS Echo Top remain the most holistically accurate. © 2021 IEEE.

关键词： Convolution

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