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An Ensemble Model to Detect Awareness of Malicious Software by Analyzing Users' Mobile Usage Patterns in the Context of Bangladesh 15

An Ensemble Model to Detect Awareness of Malicious Software ...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Trisha, Rabaya Akhter Sajjad, Md Saad, Izazul Haque Tahmiduzzaman, Kbm Rahman, Md. Sadekur Bangladesh University of professionals Department of Information and Communication Technology Dhaka Bangladesh Western Sydney University Department of Mict Sydney Australia University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

The aim of this research was to discover how well-informed the people of Bangladesh are when it comes to malicious software and how much of an awareness level they have of the problem. The system is able to examine usage patterns and trends in awareness by applying supervised machine learning technologies. The research used a comprehensive dataset obtained using a Google Forms survey to evaluate the level of knowledge held by a sample that was typical of the total population. Despite most participants showing signs of competence, the results indicated a large gap. What this means is that there has been broad success in raising awareness about cybersecurity. However, these initiatives have clearly failed because a sizable segment of the population remains uneducated. We used SMOTE, or the Synthetic Minority Oversampling Technique, to balance out the distribution of data among respondents. Many distinct categorization models were created, including LR, DT, SVC, RF, and K-Nearest Neighbors (KNN), among others. Ensemble models that combined KNearest Neighbors (KNN) with Decision Trees (DT) or Random Forest (RF) performed superiorly than the separate classifiers. The research found that using SMOTE improved the accuracy of the ensemble model, which shows how effective ensemble approaches are and how important it is to fix class imbalance in predictive modeling. © 2024 IEEE.

关键词： Class Imbalance Cybersecurity Awareness Ensemble Methods Machine Learning Predictive Modeling Supervised Learning

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ChulaVerse: University Metaverse Service Application Using Open Innovation with Industry Partners

ChulaVerse: University Metaverse Service Application Using O...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Kruachottiku, P. Phanomchoeng, G. Cooharojananone, N. Kovitanggoon, K. Tea-Makorn, P. Chulalongkorn University Graduate Affairs Faculty of Medicine Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Wireless Communication Ecosystem Research Unit Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Sasin School of Management Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9798350323153

ChulaVerse is the metaverse service application for Chulalongkorn University's community and consists of lifelong educational and commercial use cases. It aims to facilitate an online economy and society, enabling users and industry partners to engage in commerce, access medical services, engage in lifelong learning, and organize events as well as user-generated content. Using an open innovation concept, the platform aims to co-create with the community of users, developers, and industry partners to create a socio-economic impact. This paper discusses metaverse opportunities in education and presents strategic plans for ChulaVerse development and its first version of the Minimum Viable Product. Building a university metaverse is crucial in the digital age, offering immersive academic and student-life experiences. Partnering with the industry and private sector accelerates the development by leveraging resources and expertise. This collaboration fosters innovation, aligns the metaverse with market demands, provides financial and technical support, and equips students, faculty, and researchers with the skills needed to thrive in the digital world. © 2023 IEEE.

关键词： digital twins immersive learning Metaverse open innovation university-industry collaboration virtual reality

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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TDA-SSNN: Efficient Hybrid Top-Down Attention Siamese Sparrow Neural Network for Automatic Drone Navigation 4

TDA-SSNN: Efficient Hybrid Top-Down Attention Siamese Sparro...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Aamer, Najmuddin Bhadula, Shuchi Mahashree, P. Patil, Harshal Gurpur, Shashikala Verma, Apury Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248002 India School of Electronics and Communication Engineering Rev A University Karnataka Bangalore560064 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Viman Nagar Maharashtra Pune411014 India Parul University Faculty of Management studies P.O.Limda Ta.Waghodia Gujarat Vadodara391760 India

ISBN: (纸本)9798331540364

Unmanned aerial vehicles (UAVs) are increasingly employed in diverse applications such as military operations, delivery services, traffic and architectural monitoring, and aerial surveillance. These drones often encounter challenges in navigating complex and dynamic environments, including irregular object shapes, occlusions, and varying contrasts with backgrounds. To overcome these obstacles, this study presents a novel hybrid approach that integrates Top-Down Attention with a Siamese Sparrow Neural Network (TDA-SSNN) for efficient and data-driven drone navigation. The methodology begins with sourcing input images from publicly available datasets, including those from Udacity's project. To enhance image quality, the Robust Generalized Maximum Blake-Zisserman Total Correntropy (RMB-ZTC) adaptive filter is employed for preprocessing. Feature selection is performed using a novel Gorilla Troops Optimizer with Artificial Hummingbird Optimization (GTO-AMO). For automatic drone navigation, the TDA-SSNN is utilized, enhanced by an advanced Sparrow Search Algorithm to optimize the performance of the Top-Down Attention-based Siamese Neural Network. The proposed approach achieves a remarkable accuracy of 99.6%. © 2024 IEEE.

关键词： Drones

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Cyber Twin-Enabled Real-Time AI Orchestration for Advanced IoT Security and Dynamic Software Optimization 4

Cyber Twin-Enabled Real-Time AI Orchestration for Advanced I...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Sunkara, Krishna Chaitanya Barmola, Pareshwar Prasad Ravi, K.S. Shukla, Vishal Soni, Umang Sapatnekar, Amol Oracle Cloud Engineering Data Center Design & Modeling NC United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vidyavardhaka College of Engineering Department of Mechanical Engineering Mysuru India Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rapid expansion of Internet of Things (IoT) ecosystems has increased the complexity and vulnerability of software systems, making them more susceptible to security breaches and performance inefficiencies. Traditional security and optimization techniques struggle to keep up with the dynamic nature of IoT networks. This calls for the development of advanced frameworks that can adapt in real-time to evolving threats and operational requirements. This paper introduces Cyber Twins, an AI-driven framework designed for real-time security and software optimization in IoT environments. Cyber Twins leverage artificial intelligence and digital twin technology to create dynamic cyber representations of physical IoT devices and software processes. The framework integrates reinforcement learning for adaptive optimization, convolutional neural networks (CNNs) for anomaly detection, and graph neural networks (GNNs) for real-time threat analysis. By continuously synchronizing with live IoT data, Cyber Twins enable proactive threat detection and autonomous system optimization, ensuring a secure and high-performing IoT ecosystem. The proposed framework combines Federated Learning (FL) for distributed intelligence and Reinforcement Learning (RL) for adaptive decision-making. Key advancements include (1) a 35% improvement in intrusion detection accuracy compared to traditional methods, (2) a 40% reduction in false positive rates, and (3) a 25% increase in dynamic software adaptation speed. Experiments conducted on a simulated IoT environment with 1,000 devices demonstrated scalability and resilience under varying network conditions. Additionally, the framework achieved a 20% decrease in latency during AI model updates and a 30% reduction in computational overhead, ensuring efficient resource utilization. © 2024 IEEE.

关键词： Federated learning

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Network Time Synchronization as a Quantum Physical Layer Service 28

Network Time Synchronization as a Quantum Physical Layer Ser...

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28th European Wireless Conference, EW 2023

作者： Nunavath, Nikhitha Garbugli, Andrea Nande, Swaraj Shekhar Foschini, Luca Bassoli, Riccardo Fitzek, Frank H.P. The Deutsche Telekom Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Computer Science and Engineering University of Bologna Italy

ISBN: (纸本)9783800762262

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Quantum communication

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Augmentation of Motor Imagery Data for Brain-Controlled Robot-Assisted Rehabilitation

Augmentation of Motor Imagery Data for Brain-Controlled Robo...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Mouček, Roman Kodera, Jakub Mautner, Pavel Průcha, Jaroslav Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitní 8 Plzen Czech Republic Department of Information and Communication Technologies in Medicine Faculty of Biomedical Engineering Czech Technical University nám. Sítná 3105 Kladno Czech Republic

Brain-controlled robot-assisted rehabilitation is a promising approach in healthcare that can potentially and in parallel improve and partly automate the rehabilitation of motor apparatus and related brain structures responsible for movement. However, building a real-world rehabilitation system has many challenges and limitations. One of these challenges is the small size of the data that can be collected from the target group of people recovering from injured motor functions to train deep learning models recognizing motor imagery patterns. Therefore, the primary experiments with data augmentation and classification results over the collected and augmented dataset are presented. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electroencephalography

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Point Cloud Completion Method Assisted by Projected Image

Point Cloud Completion Method Assisted by Projected Image

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Shujin Li, Zhaowen Wu, Runxun Zhou, Fan U-niversity of Sun Yat-sen Faculty of Journalism and Communication Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China School of Computer Science and Engineering University of Sun Yat-sen Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Guangzhou China

ISBN: (纸本)9781665410205

Image-guided point cloud completion task aims to utilize image information to address the uncertainties in point cloud completion inference. Although acquiring 2D image data is relatively simpler than 3D data, it is still ineffective in scenarios with occlusions where image data cannot be reliably obtained as a reference. Therefore, we propose a point cloud completion model assisted by projected image data, which addresses the limitations of acquiring 2D images by constructing projected images of the point cloud. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other advanced methods. © 2024 IEEE.

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