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Cyberattack Classification in Smart Grid Distribution Substations using a Novel Ensemble Bagging Learning Technique

Cyberattack Classification in Smart Grid Distribution Substa...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Victor O. Ijeh Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Smart grid incorporates the communication networking that enables the exchange of information among the monitoring and controlling devices. Such incorporation of the communication networking into the electricity grid infrastructure poses the risk of cyberattacks that target the critical assets within such an infrastructure. Most of the existing research focuses on the detection of such cyberattacks but without identifying the type of the attacks. This can result in overlooked threats and misdirected the necessary countermeasures. Recognizing the attack’s type is essential for timely responses and strategic planning against future threats, thereby enhancing the resilience of the smart grid. In this paper, a Fine Tree Bagging-based Ensemble Learning (FTBE) technique is proposed to detect and classify the different types of cyberattacks and power quality disturbances. The salient features of the attacks’ types are highlighted, which helps in identifying the types of the attack following the detection process.

关键词： Training Accuracy Substation automation Uncertainty Scalability Smart grids Ensemble learning

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance 2

A Web Based Four-Tier Architecture using Reduced Feature Bas...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Hossen, Anwar Alamgir, Rakib Bin Alam, Arman Ul Siddika, Fatema Hossain, Shah Fahad Arman, Md. Shohel Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model. © 2021 IEEE.

关键词： Students

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Advancements in Jute Leaf Disease Detection: A Comprehensive Study Utilizing Machine Learning and Deep Learning Techniques 2

Advancements in Jute Leaf Disease Detection: A Comprehensive...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Haque, Rezaul Miah, Md Miraz Sultana, Shayma Fardin, Hasib Noman, Abdullah Al Al-Sakib, Abdullah Hasan, Md Kamrul Rafy, Al Shihabur, Rahman Md Rahman, Shafiur East West University Dept. of Cse Dhaka1212 Bangladesh Brac University Dept. of Cse Dhaka1212 Bangladesh International American University Dept. of Management Information System CA90010 United States Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University Dept. of Information Technology CA92614 United States Touro University Dept. of Cyber Security CA94592 United States Kyungdong University Dept. of Computer Engineering Gangwon-do Korea Republic of Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331517984

Detecting diseases in jute leaves is difficult due to the variability in how the diseases appear. Manually identifying these diseases is challenging because it requires expert knowledge and visual inspections take a lot of time. Machine Learning (ML) offers a promising solution to these challenges by automating the detection process. However, research in this area is limited due to the lack of specific datasets. This study aims to address this by creating a comprehensive dataset of 10,800 high-quality images of jute leaf diseases. The main goal is to develop a robust system for classifying jute leaves into three categories: Yellow Mosaic, Powdery Mildew, and Healthy. Our methodology involved extensive image preprocessing, including resizing and various augmentation techniques, to enhance the dataset's diversity and ensure model robustness. We trained various ML and Deep Learning (DL) models and conducted a comparative analysis of their performance. Additionally, we compared our approach with the state-of-the-art methods. The results showed that DL models, particularly Inception V3, achieved an outstanding accuracy of 99.98%, compared to 89.75% for Random Forest (RF). This highlights the potential of DL techniques in improving the accuracy of jute leaf disease detection. Our findings contribute to better disease management strategies and increased productivity in jute cultivation. © 2024 IEEE.

关键词： Adversarial machine learning

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A New Random-Forest-Based Approach for Cyberattack Detection in Digital Substations

A New Random-Forest-Based Approach for Cyberattack Detection...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Kripa M. Jose Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Digital substation automation systems rely heavily on information and communication technologies, which are prone to cyberattack threats. Traditionally, the focus has been geared toward the detection and classification of power quality disturbances from the normal operation. This paper presents a novel approach that uses random forests to detect and classify both cyberattacks and power quality disturbances from normal operation. The proposed approach was successful in identifying the salient features that significantly influence the detection of such cyberattacks. In order to demonstrate the effectiveness of the proposed approach, it has been tested on two different publicly available datasets. Unlike in previous work, the results have shown that the uncertainty associated with the choice of the training and testing sets has insignificant effect on the classification accuracies of the proposed approach. Furthermore, the proposed approach was successful in overcoming the issues of data imbalance and overfitting, which are common in cybersecurity problems.

关键词： Training Accuracy Sensitivity Uncertainty Substation automation Power quality Forestry

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Spatial Mini Golf: Game-Based Spatial Intelligence Testing and Training Using VR and Non-VR Platforms

Spatial Mini Golf: Game-Based Spatial Intelligence Testing a...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Patrick Oliver Andy Fong Zixuan Wang Burkhard C. Wünsche Dept. of Software Engineering University of Auckland Auckland New Zealand School of Computer Science University of Auckland Auckland New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Spatial intelligence is a key determinant of students' motivation and success in pursuing a STEM career. To improve students' participation in STEM subjects it is important to test and train spatial skills in an enjoyable and unobtrusive manner. We present Spatial Mini Golf, a game designed to test and enhance spatial intelligence, targeting four sub-skills: visual perception, mental rotation, spatial visualization, and visuospatial working memory. Two versions-VR and desktop-were compared. While VR increases engagement, it also presents control challenges that raise cognitive load and extend task completion times. Performance correlates with spatial intelligence, particularly in desktop environments, but VR reduces performance gaps among lower-ability participants. Our research suggest that both VR and desktop games are suitable for testing and training spatial reasoning skills. VR tools might be more accessible for users with low spatial skills if intuitive and easy-to-use interfaces are provided, but results might get affected by users' hand-eye coordination.

关键词： Training Visualization engineering profession Games Cognitive load Testing Visual perception

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Utilization of Online Collaborative Tools in software engineering: An Empirical Study on Review Meetings 6

Utilization of Online Collaborative Tools in Software Engine...

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Akman, Ibrahim Turhan, Cigdem Hacaloglu, Tuna Dept. of Computer Engineering Atilim University Ankara Turkey Dept of Software Engineering Atilim University Ankara Turkey Dept of Information Systems Engineering Atilim University Ankara Turkey

ISBN: (纸本)9781665429085

software development involves a significant amount of team effort where collaboration and communication of the team members are crucial. The team meetings are core activities in all stages of the software development process. Even though these meetings often are conducted face-to-face (F2F) with a lackof technology utilization, changing global conditions such as the COVID-19 pandemic require other solutions urgently without interrupting the software development schedule. For this purpose, online collaborative tools provide new opportunities for software teams to work together avoiding waste in time and resources and the relevant literature is immature. This study aims to assess the factors affecting the integration of online collaborative tools to SE practices with a special reference to review meetings. For this purpose, a sample of 73 SE sophomore and junior students who are future software professionals participated in experimental review meetings based on predefined scenarios. The findings indicate that the utilization of OCT's has positive effects on the participants' actual performance and improves the interaction between team members compared to F2F meetings. © 2021 IEEE

关键词： software design

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G

Work in Progress: Congestion Control in mmWave Fluctuating S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jorge Ignacio Sandoval Sandra Céspedes Department of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng Concordia University Montreal Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times.

关键词： Measurement Machine learning algorithms Millimeter wave technology Bandwidth Machine learning Radio links Millimeter wave propagation Millimeter wave communication Next generation networking Convergence

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An Automated Wavelet Generation Tool for Cyberattack Detection in Substation Automation Systems

An Automated Wavelet Generation Tool for Cyberattack Detecti...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Matthew Oinonen Walid G. Morsi Computer and Software Engineering Dept. Smart Grid & Electric Vehicles Research Laboratory Electrical Ontario Tech University Oshawa Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

This paper presents a novel method, the Automated Custom Wavelet Generation-Deep Learning-based classifier (ACWGDL), for cyberattack detection in smart grids. The proposed method generates custom wavelets for enhanced feature extraction of cyberattacks on substation automation systems (SASs). Physical and network data from a substation is converted into time-frequency spectrograms using the Discrete Wavelet Transform (DWT) with the custom generated wavelets. Deep learning is implemented with a convolutional neural network to classify the generated spectrograms as cyberattacks from normal cases and power quality disturbance cases. The proposed method has been tested on two publicly available datasets from two different substation automation systems. The results have shown that the use of the custom wavelets was able to enhance the detection accuracy of the cyberattacks by up to 11% compared to the existing benchmark wavelets. The proposed method is versatile and has demonstrated its effectiveness in enhancing the detection accuracy of the cyberattacks irrespective of the choice of the datasets.

关键词： Training Time-frequency analysis Substation automation Accuracy Benchmark testing Feature extraction Discrete wavelet transforms

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Using Deep Learning depth Maps to Improve Monocular SLAM

Using Deep Learning Depth Maps to Improve Monocular SLAM

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Simon Hollows Richard Green Dept. Mechatronics Engineering University of Canterbury Christchurch New Zealand Dept. Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes the use of a neural network to generate depth maps from 2D frames, offering an alternative to depth cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate depth data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between depth sensor depth maps in test datasets and the generated depth maps. The MiDaS neural network was used to generate estimated depth maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated depth maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM depth maps had a root mean square error (RMSE) or 0.24 m while the generated depth maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between depth maps causes this approach to fail in slow-moving scenes.

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Amin Avan Farnaz Kheiri Qusay H. Mahmoud Akramul Azim Masoud Makrehchi Shahryar Rahnamayan Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Dept. of Engineering Brock University St. Catharines ON Canada

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC.

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