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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration 3

A Dynamic Security Assessment Tool for Modern Power Systems ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Therapontos, Phivos Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources' (IBRs') protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded Cyprus Power System. © 2024 IEEE.

关键词： Dynamic security assessment frequency security IBR modeling low-inertia grids system strength

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

引用

9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Minimum DC Link Capacitance for a Family of Three-Phase Three-Level Grid-Connected Converters Operating with Unity Power Factor 17

Minimum DC Link Capacitance for a Family of Three-Phase Thre...

引用

17th IEEE International Conference on Compatibility, Power electronics and Power engineering, CPE-POWERENG 2023

作者： Siton, Yarden Abramovitz, Alexander Mellincovsky, Martin Sitbon, Moshe Lineykin, Simon Kuperman, Alon Ben-Gurion University of the Negev School of Electrical and Computer Engineering Beer-Sheva Israel Shamoon College of Engineering Dept. of Electrical and Electronics Engineering Ashdod Israel Ariel University of Samaria Dept. of Electrical Engineering and Electronics Ariel Israel

ISBN: (纸本)9798350300048

The paper presents a method for derivation of the minimum split DC link capacitance in three-phase three-level DC-AC converters operating with unity power factor with neither active balancing circuits nor AC zero sequence injection. It is shown that due to the fact that partial DC link voltages and AC-side phase voltages obtain extremum values at different instants, it is possible to reduce the minimum value of the former below maximum value of the latter while still ensuring correct operation of the power stage. Minimum attainable split DC link capacitance values are therefore obtained according to boundary case of tangency between the above-mentioned voltages. Analytical derivations are accurately verified by simulations and experiments, demonstrating close agreement. © 2023 IEEE.

关键词： Capacitance

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Next-Gen SDN Security: Quantum Keying Meets AI Analytics

Next-Gen SDN Security: Quantum Keying Meets AI Analytics

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Virushabadoss, S. Anithaashri, T.P. Nivedha, M. Krishnan, S. Hari Karuppannan, Anand Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of Computer Science and Engineering Chennai India Arasu Engineering College Department of Electronics and Communication and Engineering Kumbakonam India Sanskrithi School of Engineering Department of Electronics and Communication and Engineering Puttaparthi India Gnanamani College of Technology Department of Electronics and Communication and Engineering Namakkal India

ISBN: (纸本)9798350375442

This paper suggests a complete security architecture that blends quantum key distribution (QKD) with cutting-edge machine learning methods to improve the Software-Defined Networking (SDN) control plane. By creating secure channels of communication through QKD, the framework ensures integrity and secrecy of control messages between the SDN controller and network devices. Furthermore, network traffic is continuously monitored and analyzed using machine learning techniques, enabling the detection of unusual behaviors that might be warning signs of upcoming security problems. Tests utilizing Mininet as the network interface and Open Daylight as the SDN controller proved the efficacy of the suggested method. The outcomes revealed a minimal computational overhead, exceptional detection accuracy up to 98.1%, and low false positive rates, substantiating the system's ability to effectively manage varied network conditions and traffic volumes. These results underscore the potential of integrating QKD with robust machine learning strategies to secure the SDN control plane against sophisticated threats. © 2024 IEEE.

关键词： Quantum cryptography

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement 3

Comparative Study of Implementation of Very Deep Super Resol...

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3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

作者： Ganesan, P. Ravichandran, M. Sathish, B.S. Joseph, L.M.I.Leo Sajiv, G. Murugesan, R. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Sri Venkateshwara College of Engineering Dept. of Computer Science Engineering Ai Bengaluru India Sr University Dept. of Electronics and Communication Engineering Telangana Warangal India Saveetha School of Engineering Simats Chennai India Narshima Reddy Engineering College Dept. of Electronics and Communication Engineering Secunderabad India

ISBN: (纸本)9798350358742

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results. © 2023 IEEE.

关键词： Medical imaging

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Hardware Implementation to Detect Brain Tumor using AI ML based Convolution Neural Networks CNN in the Field of Bio Medical engineering 15

Hardware Implementation to Detect Brain Tumor using AI ML ba...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ravindranath, C. Gopalaiah Adithya, T.G. Pavithra, G. Manjunath, T.C. Department of Civil Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Electronics & Instrumentation Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Dept. of Computer Science & Engineering PES University Hoskerehalli 100 Feet Ring Road Karnataka Bangalore560085 India Dept. of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331300579

A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. We are using a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) Transfer learning for detecting brain tumors. The performance of the model is predicting image tumor is present or not in image. If the tumor is present, it returns yes otherwise return no. Here the location of the tumor is identified by K-means clustering using Raspberry pi. The Arduino controller is used for the movement of the robotic wheels to move in the exact location and determines the position of the tumor. © Grenze Scientific Society, 2024.

关键词： Convolution

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Identify Initial Nodes for Spreader in A New Diffusion Model Based on Topology (DBT) in Social Networks 9

Identify Initial Nodes for Spreader in A New Diffusion Model...

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9th International Conference on Web Research, ICWR 2023

作者： Khosravi, Ehsan Yazdanifar, Fatemeh Salimi, Nikoo Beni, Hamid Ahmadi Sharif University of Technology Dept. of Computer Engineering Tehran Iran Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Engineering and Naval Architecture University of Genoa Dept. of the Electrical Electronics Electronics and Telecommunication Genoa Italy Azarbaijan Shahid Madani University Dept. of Software Engineering Tabriz Iran

ISBN: (纸本)9798350399691

With the development of science and technology through social networks, the process of diffusion has become a controversial subject. In social networks, the process of diffusion is transmitted from one individual to another. The influence maximization problem, in which at least k nodes are chosen as the initial nodes of the spreader in the smallest amount of time, is one of the most critical aspects of the diffusion process. Furthermore, the initial nodes in the social network should generate the most influence on the other nodes in the network. However, in the algorithms proposed to solve the problem of influence maximization, the real-world factors which affect the influence probability in diffusion models are ignored for example, the influence probability is determined randomly. This problem causes influence maximization algorithms based on diffusion models to face the challenge of time-consuming Monte Carlo simulation. So, the RIM algorithm presented in this article which this method selects the initial nodes of the spreader according on their local reachability. In addition, Due to privacy in social networks, complete information about people such as gender, education, etc. is not available. For this reason, in the RIM algorithm, using the graph structure in social networks, the influence probability is calculated in the new DBT diffusion model. In addition, the RIM algorithm exhibits stable performance in the selection of the spreader's initial nodes in terms of influence spread, and execution time. © 2023 IEEE.

关键词： Topology

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New Beamforming Approach Based EGWO for Beyond 5G Communication

New Beamforming Approach Based EGWO for Beyond 5G Communicat...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Farghaly, Samar I. Fouda, Mostafa M. Tanta University Faculty of Engineering Electronics and Electrical Communications Engineering Dept. Tanta31527 Egypt Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798350351408

Beamforming has attracted the attention of many researchers in recent years. It significantly enhances the performance of many communication systems, especially beyond 5G communication, in terms of spectral and energy efficiency. In addition, beamforming is done in the analog domain, so it adds no complexity to the existing system. Consequently, in this paper, the new beamforming approach is based on a hybrid combination between GWO and L2-norm called enhanced garay wolf optimizer (EGWO). This approach is used to reduce the side lobe level (SLL) and the number of antenna elements. Hence, the signal-to-interference noise ratio (SINR) is maximized, and complexity is suppressed via the reduction of elements. The simulation results show the proposed algorithm outperforms other algorithms in comparison. © 2024 IEEE.

关键词： Signal to noise ratio

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Developing and Implementing An Effective Big Data Classification System 2

Developing and Implementing An Effective Big Data Classifica...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Managuli, Manjunath Pujar, Sagar Managuli, Sangamesh C Krishnamurthy, K.T. Nandalike, Rajesh Kunchur, Pavan Kls Gogte Institute of Technology Dept. of Electronics & Communication Belagavi India Kls Gogte Institute of Technology Dept. of Computer Science Belagavi India Pes University Dept. of Mechanical Engineering Bangalore India B M S College of Engineering Dept. of Electronics & Instrumentation Bangalore India Nitte Meenakshi Institute of Technology Dept. of Electronics & Communication Bangalore India

ISBN: (纸本)9798350317558

Relevant data Technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency. © 2024 IEEE.

关键词： Bayesian Network deep learning feature classification Feature selection

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A New Proposed Data Security Model in Cloud Computing Environment

A New Proposed Data Security Model in Cloud Computing Enviro...

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2023 International Conference on computer, electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Banerjee, Pallab Prasad, Vishal Thakur, Kanika Banerjee, Probal Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India Govt. Polytechnic Ranchi Dept. of Electronics and Communication Engineering Jharkhand India

ISBN: (纸本)9798350338003

Advancement in the environment of cloud computing has rapidly increases over the past years. In cloud computing protection of data utilizing centralized resources has arisen because of a growing number of businesses storing their data int the cloud helps them easily to access their data easily. The major issues faced by users nowadays, the safety and fetching of user cloud data is arisen rapidly. In this proposed algorithm, the user's data is encrypted using the advanced encryption standards algorithm, and the encrypted data is then performed with some xor operations in cypher block chaining mode using a secret key known only to the data owner to increase confidentiality and security level. If the user does not provide a secret key, the system can generate one on its own. © 2023 IEEE.

关键词： Cryptography

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