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Enhancing Network Intrusion Detection in a Limited Labeled Data Scenario: An Active Learning Approach

Enhancing Network Intrusion Detection in a Limited Labeled D...

引用

2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hossain, Md Sharafat Hossain, Md Al Amin Sikdar, Md Shahin Islam, Md Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh Selcuk University Dept. of Information Technology Engineering Konya Turkey Prime University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The Internet was originally developed to facilitate easier communication through digital devices. It has made our lives easier by providing means for communication and delivering digital resources anywhere and at any time in the world. However, the internet also exposes users to the risk of criminals exploiting various vulnerabilities in the internet system, launching sophisticated attacks to gain valuable information and hamper users' lives. Henceforth, an effective intrusion detection system (IDS) is imperative for safeguarding the data and resources of users in the cyber world. This research aims to enhance semi-supervised IDS models by leveraging limited labeled data and unlabeled data and exploring innovative approaches to improve adaptability and accuracy in detecting known and emerging threats, particularly in scenarios with limited labeled data available. Our study uses the UNSW-NB15 dataset, and the results show that our suggested active learning-based semi-supervised model, which was created with 2000 labeled data, is better than the supervised model that uses the same number of labeled data. The proposed model achieved an accuracy score of 97.67% while existing studies achieved an accuracy score of 94.61%. © 2024 IEEE.

关键词： Network intrusion

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

引用

2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Adesokan, Adedamola Rahman, Aisha B Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms' trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers' monetary incentives, profit, and the platforms' trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms' monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms' reputation. Simulation results demonstrate TOPMG's operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies. © 2024 IEEE.

关键词： Reinforcement learning

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Fuzzy C-Means Clustering Approach for Green IoT in Smart Cities 3

Fuzzy C-Means Clustering Approach for Green IoT in Smart Cit...

引用

3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Hussein, Aya Ayad Abdulrazzak, Hazem N. Al-Iraqia University Department of Network Engineering Faculty of Engineering Baghdad Iraq Al-Rafidain University College Computer Communication Engineering Dept. Baghdad Iraq

ISBN: (纸本)9798350349207

Cluster-based routing or Hierarchical is a widely recognized approach with particular benefits in terms of scalability and communication efficiency. In Wireless Sensor Networks supported IoT, the utilization of clustered routing is a cost-effective method of data collection and transmission, where nodes with the most energy remaining can be utilized to collect and transmit data. This paper proposed a soft Fuzzy C-Means Dynamic Clustering (FCM-DC) Approach. A new Cluster Head (CH) selection technique was presented also. The proposed model was compared with an existing cluster based routing protocol, LEACH protocol, and LEACH-C protocol. The proposed FCM-DC model reduce the packet delay about 16% in 50 nodes scenario and 86% in 100 nodes scenario compared with LEACH. © 2024 IEEE.

关键词： Smart city

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Penafiel, Arianna Santamaria Diamanti, Maria Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The efficient operation of the unified Integrated Sensing and communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

关键词： Mobile edge computing

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Deep Learning-Based Flood Detection system Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection 24

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Odeh, Suhail Samara, Anas Zreineh, Nicola Obeid, Seif Ibrieghieth, Maen Obaid, Mahmoud Bethlehem University Software Engineering Dept. Palestine Arab American University Computer System Engineering Jenin Palestine

ISBN: (纸本)9798350384307

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care. © 2023 IEEE.

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Cross-Layer Design for Enhanced QoS in Video Streaming Over Mobile Wireless Networks in Weibull Fading Conditions 12

Cross-Layer Design for Enhanced QoS in Video Streaming Over ...

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12th International Conference on Internet of Everything, Microwave, Embedded, communication and Networks, IEMECON 2024

作者： Upadhyay, Deepak Gupta, Mridul Giri, Sagar Sharma, Rashmi Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350387315

This research paper proposes a novel cross-layer design framework of adaptive mechanisms and QoS strategies to improve the QoS of mobile video streaming in the face of Weibull fading harsh conditions. The unique component of the design is the application of adaptive mechanisms across application, MAC, and PHY layers. The robust optimization plan based on dynamic bitrate control, advanced error correction methods, and responsive real-time-aware resource allocation to urban-Weibull-mobility strengthen video streaming robustness. The SES results presented several improvements in terms of latency reduction and throughput enhancement, as well as the minimization of the error rate. These results depict high performance particularly in the most challenging conditions of high fading variability and realistic mobility distributions for urban mobile networks. In summary, a novel cross-layer adaptive video streaming mechanism for urban environments has been proposed, demonstrating remarkable performance enhancements. Finally, the study has proved that the cross-layer design is an optimal and efficient way of overcoming conventional and harsh challenging situations, adding a foundation for future direction of network architecture that can assure far more user-friendly and reliable mobile video services. © 2024 IEEE.

关键词： Video streaming

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

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6th IEEE International Conference on Recent Advances in Intelligent Computational systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-dept. overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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A systematic Study on Ransomware Attack: Types, Phases and Recent Variants 5

A Systematic Study on Ransomware Attack: Types, Phases and R...

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5th International Conference on Intelligent communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Krishna Prasad, D.S. Prasanna Kumar, H.R. Dept. of Computer & Communication Engineering. Karnataka574110 India Visvesvaraya Technological University Belagavi590018 India Pes Institute of Technology and Management Dept. of Information Science and Engineering Shivamogga India

ISBN: (纸本)9798350385649

The rise of ransomware attacks presents a major cybersecurity threat, leading to considerable financial losses and disrupting crucial operations across diverse sectors. In-dept. research on the field of ransomware assaults is presented in this study, with particular attention to the various kinds and variations that have appeared throughout time. This research includes a thorough examination of the strategies, processes, and methods that ransomware perpetrators use to carry out their operations. The study outlines the many stages in the lifecycle of ransomware attacks, including the introduction of infection vectors, the use of encryption tools, and the last step of extortion. The research also explores the terrain of ransomware variations, recognizing the evolution and diversity of these threats throughout time. With a taxonomy that categorizes variations depending on their dissemination methods, encryption algorithms, and evasion approaches. © 2024 IEEE.

关键词： Cybersecurity

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