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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Ansh Bhavsar Sezan Agvan Fenil Ramoliya Mohammad S. Obaidiat Rajesh Gupta Sudeep Tanwar Kuei-Fang Hsiao Department of Computer Science and Engineering Institute of Technology Nirma University India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan King Abdullah II School of Information Technology The University of Jordan Amman Jordan

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

Power systems must be secured against malicious activity and unauthorized access with the help of intrusion detection systems (IDS). Unfortunately, it is often difficult for conventional IDS techniques to correctly identify robust and dynamic threats. The security of the power grid is seriously threatened by the growing frequency of attacks. In this paper an ensemble learning framework is proposed for power system intrusion detection in order to overcome this difficulty. This framework achieving high accuracy by combining the Random Forest, Decision Tree, and Logistic Regression algorithms. The proposed ensemble model is able to achieve an accuracy of 98%, which can be used to improve IDS outcomes [1]. Additionally, this method produces a low false alarm rate, that ensures precise and accurate intrusion detection. The system precisely detects intrusions by continuously observing real-time data for anomalies. Ensemble learning can incorporate data compression methods such as feature selection which reduce the overall memory footprint. This model's evaluation depicts that it has the potential to detect attacks on power systems, making it a useful tool for boosting overall security.

关键词： Support vector machines Logistic regression Accuracy Scalability Intrusion detection Real-time systems Power systems

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Complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks 2

Complex Event Processing in Web Streams with Ontology-Based ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishna, M Hari Manjunatha Kumar, Rakesh Singh, Navdeep Kumar, Ashwani Ftaiet, Adnan Allawi Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Smart cities embody the integration of information technology, urban infrastructure, and citizen engagement to optimize city functions and drive economic growth. A critical component in achieving this integration is the ability to process complex events in real-time, particularly from diverse web streams. This paper introduces a novel framework for Complex Event Processing (CEP) in web streams by employing ontology-based abstraction layers, aiming to enhance the interpretability and interoperability of heterogeneous data sources within smart city frameworks. The proposed approach uses a layered architecture where the bottom layer deals with raw data streams from various sources, including IoT devices, social media, and sensor networks. The middle layer employs an ontology-based model to abstract and enrich the raw data, providing a unified and semantic representation. This representation facilitates the identification and correlation of complex events, which are pertinent to city management and planning. The top layer involves the application of advanced data mining techniques to predict, detect, and respond to urban events in a timely and efficient manner. Through this methodology, the framework addresses the challenges of scalability, semantic heterogeneity, and real-time processing needs inherent in smart city applications. Empirical evaluations demonstrate the efficacy of the approach in various urban scenarios, showing promising improvements over existing methods. This work lays a foundational architecture for future research and development in smart city technologies, aiming to make urban areas more livable, sustainable, and efficient. © 2024 IEEE.

关键词： Complex Event Processing Data Mining Ontology-Based Abstraction Smart Cities Web Streams

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Cloud Forensics Analysis Framework for Secure and Efficient Data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Revathi, V. Aishwarya, B.K. Tripathi, Manju Singh, Navdeep Alabdely, Haider Kumar, Ashwani New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering M. P. Bhopal India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350373783

This study's Cloud Forensics Analysis Framework seeks secure and effective data recovery methods. The framework's five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure Data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics Analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation. © 2024 IEEE.

关键词： Cloud security

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Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

arXiv

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arXiv 2022年

作者： Damer, Naser Fontanillo López, César Augusto Fang, Meiling Spiller, Noemie Pham, Minh Vu Boutros, Fadi Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Centre for IT & IP Law KU Leuven Leuven Belgium

The main question this work aims at answering is: "can morphing attack detection (MAD) solutions be successfully developed based on synthetic data?". Towards that, this work introduces the first synthetic-based MAD development dataset, namely the Synthetic Morphing Attack Detection Development dataset (SMDD). This dataset is utilized successfully to train three MAD backbones where it proved to lead to high MAD performance, even on completely unknown attack types. Additionally, an essential aspect of this work is the detailed legal analyses of the challenges of using and sharing real biometric data, rendering our proposed SMDD dataset extremely essential. The SMDD dataset, consisting of 30,000 attack and 50,000 bona fide samples, is publicly available for research purposes. Copyright © 2022, The Authors. All rights reserved.

关键词： Biometrics

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Multi-Layered Ontology Models for Dynamic Knowledge Representation in Social Network Analysis 2

Multi-Layered Ontology Models for Dynamic Knowledge Represen...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Reddy, Y Manohar Raj, Vijilius Helena Bansal, Saloni Thethi, H Pal Kalra, Ravi Khalid, Mohammed Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement. © 2024 IEEE.

关键词： Latent semantic analysis

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Quantum Transport in Open Spin Chains using Neural-Network Quantum States

arXiv

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arXiv 2022年

作者： Mellak, Johannes Arrigoni, Enrico Pock, Thomas von der Linden, Wolfgang Institute of Theoretical and Computational Physics Graz University of Technology Petersgasse 16/II GrazA-8010 Austria Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II GrazA-8010 Austria

In this work we study the treatment of asymmetric open quantum systems with neural-networks based on the restricted Boltzmann machine. In particular, we are interested in the non-equilibrium steady state current in the boundary-driven (anisotropic) Heisenberg spin chain. We address previously published difficulties in treating asymmetric dissipative systems with neural-network quantum states and Monte-Carlo sampling and present an optimization method and a sampling technique that can be used to obtain high-fidelity steady state approximations of such systems. We point out some inherent symmetries of the Lindblad operator under consideration and exploit them during sampling. We show that local observables are not always a good indicator of the quality of the approximation and finally present results for the spin current that are in agreement with known results of simple open Heisenberg chains. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Embryonic heart development as an immersive experience: Unveiling learning effects and influential factors in virtual learning environments

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computers in Biology and Medicine 2025年 187卷 109638-109638页

作者： Kunz, Matthias Schott, Danny Wunderling, Tom Halloul, Martin Hansen, Christian Albrecht, Anne Braun-Dullaeus, Rüdiger C. Otto von Guericke University Medical Faculty Department of Internal Medicine/Cardiology and Angiology Magdeburg Germany Otto von Guericke University Faculty of Computer Science Department of Simulation and Graphics Magdeburg Germany Otto von Guericke University Medical Faculty Institute of Anatomy Magdeburg Germany

As the quality and availability of Virtual Reality (VR) technologies improve, their potential applications in medical education, particularly VR Learning Environments (VRLEs), are increasingly explored. VRLEs offer a dynamic platform where educators and students can interact, access materials, and engage in learning beyond traditional classrooms. However, questions remain about their long-term learning effects and potential confounding factors. This study investigates these aspects through a VR application designed for teaching heart embryology. For this reason we conducted a user study with medical students in their early years of training (N = 143). Our findings reveal significant short-term and sustained learning outcomes two to four weeks following a single VR session. Importantly, these outcomes appear largely independent of users’ technical affinity and are minimally influenced by their immersion tendencies. Instead, the quality of the VRLE and its user experience emerge as critical factors. These results underscore the efficacy of well-designed VRLEs in higher education and highlight key areas for future development. © 2025 The Authors

关键词： Virtual environments

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UAV-LiDAR and Terrestrial Laser Scanning for Automatic Extraction of Forest Inventory Parameters

UAV-LiDAR and Terrestrial Laser Scanning for Automatic Extra...

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Virtual International Conference on Soft Computing, Optimization Theory and Applications, SCOTA 2021

作者： Meghraoui, Khadija Lfalah, Hamza Sebari, Imane Kellouch, Souhail Fadil, Sanaa Ait El Kadi, Kenza Bensiali, Saloua Research Unit of Geospatial Technologies for A Smart Decision Hassan II Institute of Agronomy and Veterinary Medicine Rabat Morocco Cartography–Photogrammetry Department School of Geomatics and Surveying Engineering Hassan II Institute of Agronomy and Veterinary Medicine Rabat Morocco AXIGEO Marrakech4040 Morocco Department of Water and Forests Rabat Morocco Department of Applied Statistics and Computer Science Hassan II Institute of Agronomy and Veterinary Medicine Rabat Morocco

ISBN: (纸本)9789811964053

The determination of the Dendrometric parameters of forest stands has a silvicultural and ecological interest for the forester, in particular for the evaluation of the dynamics of growth and productivity, and the evaluation of indicators of good ecological status. Currently, UAV-LiDAR (Unmanned Aerial Vehicle-Light Detection and Ranging) has become the new trend for measurement professionals, offering very high-resolution data collection at considerably lower survey costs. In addition, this technology has started to prove its utility in forest inventory applications namely to extract dendrometric parameters, where direct and conventional measurements are sometimes difficult. As for the TLS (Terrestrial Laser Scanning) technology, it has made it possible to obtain several abundant and refined structural information under the forest canopy. In the context of extraction of forest inventory parameters, the precision of extracting tree height for example using TLS alone, is insufficient. Hence the contribution of the combination of ALS (Aerial Laser Scanning) with TLS data to fill any information gaps that may exist. The main goal of this study is to present an approach to the automatic extraction of dendrometric parameters from UAV-LiDAR and TLS data. The proposed methodology is based on performing a TLS survey at a plot level and an ALS scan of the entire area. Our methodology is essentially made up of two steps: automatic crown delineation and automatic extraction of dendrometric parameters (position, Diameter at breast height, height, stem curve, concave and convex hull). For the first step, we compared the segmentation of the point cloud by the Watershed algorithm and by the SEGMA pipeline. Whereas the extraction of the dendrometric parameters was carried out using a set of algorithms namely RHT (Random Hough Transform) and LSR (Least Square Regression). The study focused on UAV-ALS and TLS datasets from different regions and with different densities (the Mediterran

关键词： Antennas

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Construction of Efficient Authenticated Key Agreement Protocol for Intelligent Transportation System 2

Construction of Efficient Authenticated Key Agreement Protoc...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Nongthombam, Kiranbala Rana, Saurabh Obaidat, Mohammad S. Chhikara, Deepak Mishra, Dheerendra Chandigarh University Department of Mathematics Punjab Mohali140413 India King Abdullah Ii School of Information Technology University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Maulana Azad National Institute of Technology Department of Mathematics / Bioinformatics and Computer Applications Bhopal462003 India Computer Science Department and Director of Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States

ISBN: (纸本)9781665464017

To enable intelligent transportation systems, the Internet of Vehicles (IoV) uses wireless communications between roadside units, onboard units), phones, and other gadgets within a vehicle to deliver a new convenient, comfortable, and safe driving experience. IoV is now more exposed and frequently targeted because of its greatly increased reliance on wireless communication, control, and computer technology, To put it another way, an adversary may target such an open channel to intercept, change, inject malicious messages, or erase loaded data. This could lead to replay, impersonation, middle attack, privileged insider, and other related attacks. An authentication protocol should strive to accomplish security, anonymity, and untraceability among other critical aspects. In this research, we suggest a brand-new key agreement and mutual authentication methodology for IoV powered Intelligent transportation system (ITS). We demonstrate that our suggested system is secure against several known threats in IoV- the enabled ITS environment. Furthermore, a thorough comparison of our proposed system to eight other competing ones reveals that it has the lowest communication and computational costs. In addition, it is more secure and provides more functionality characteristics. © 2022 IEEE.

关键词： Authentication

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Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

Privacy-friendly Synthetic Data for the Development of Face ...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Naser Damer Cé sar Augusto Fontanillo Ló pez Meiling Fang Noé mie Spiller Minh Vu Pham Fadi Boutros Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Centre for IT &#x0026 IP Law KU Leuven Leuven Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

关键词： Training Data privacy computer vision Law Face recognition Conferences Training data

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