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Enhancement of Level of Security using Wireshark Through Continuous Monitoring and Detection System

Enhancement of Level of Security using Wireshark Through Con...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： C.Rohith Bhat Sulakshana Bhausaheb Mane Chandradeep Bhatt Garima Verma Sarmad Jaafar Naser Muhsin. Jweeg Department of computer science and engineering Saveetha school of engineering (saveetha institute of medical and technical sciences) Department of Computer Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Computer Science and Engineering Graphic Era Hill University Dehardun India Computer Science& EngineeringGraphic Era Deemed to be University Dehradun Uttarakhand India Collage of Nursing National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

We suggest using edge computing to install an intrusion detection system (IDS) at the network edge in order to address the problem of intrusion detection in wireless sensor networks (WSNs). This IDS functions as a proactive defence system designed specifically with WSNs in mind. Our method combines an arithmetic optimisation algorithm (AOA) with Wireshark and machine learning to create an edge intelligence framework for identifying denial-of-service (DoS) assaults in wireless sensor networks (WSNs). We use the wireshark for optimisation adjustment and a parallel strategy to promote communication between populations in order to increase model accuracy. Our model achieves $\mathbf{9 8 . 9 \%}$ accuracy (ACC) through simulated studies utilising the IDS dataset in Matlab2018b. The effectiveness and practical significance of our suggested intrusion detection approach are highlighted by these experimental results.

关键词： Wireless sensor networks Accuracy Image edge detection Sociology Intrusion detection Mathematical models Security

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How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild 31

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

引用

31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhang, Heng Huang, Shaoyuan Xu, Mengwei Guo, Deke Wang, Xiaofei Leung, Victor C.M. Wang, Wenyu College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada Co. Ltd Shanghai China

ISBN: (纸本)9798350399738

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds. © 2023 IEEE.

关键词： Edge computing

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WIP: A Systematic Approach for Outcome-Based Assessment

WIP: A Systematic Approach for Outcome-Based Assessment

引用

Frontiers in Education (FIE) Conference

作者： F. Belkhouche K. Bendjilali Department of Computer Electrical and Software Engineering Shippensburg University of Pennsylvania Shippensburg PA USA Department of Mathematics and Computer Science Raritan Valley Community College Branchburg NJ USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice WIP paper describes methods for improving assessment. Assessment is crucial for pro-gram accreditation and continuous improvement in engineering education. Outcome-based learning is a recent student-centred approach that can be used perform assessment. Under this paradigm, clear outcomes related to the knowledge and skills that the students should have after completing the course are identified and assessed. The student attainments are measured against the outcomes using clear and well-defined metrics. In this paper, an outcome-based strategy is proposed and used for assessing courses in control systems. In order to improve assessment reliability, the learning outcomes are broken down into sub-outcomes, which are used as performance indicators. The advantage of the proposed method is twofold, first it provides more accurate and reliable metrics for measuring student attainment, and second, the assessment results are personalized, allowing to evaluate individual students and identify means to help them. This approach also allows to identify common mistakes and misconceptions. Data are collected for several semesters and analyzed. It turns out that the proposed approach allows to obtain more accurate and reliable assessment results.

关键词： Measurement Accuracy Systematics Control systems Accreditation Reliability engineering education Portfolios

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WIP: Concept Maps for computer Organization: A Systematic Study

WIP: Concept Maps for Computer Organization: A Systematic St...

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Frontiers in Education (FIE) Conference

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice WIP paper describes concept maps for computer organization. A concept map is a tool that can be used to describe the relationship between the topics (or concepts) in a specific field of study. Concept maps are used in a variety of applications to improve curricula and assessment, particularly in K12 and undergraduate STEM education. In this paper we use concept maps for modeling computer organization topics and assess their importance with respect to the depth and breadth levels required for computer science and engineering graduates. The proposed concept maps focus on two aspects: The first one is the idea of the centrality of the topics. Centrality is an important concept in graph theory. Each topic in the concept map is given a level of centrality or a centrality index. The second aspect is the relationship between the topics, particularly between the topics with high level of centrality. The objective of the modeling is to identify the most central topics and come up with a set of more standard learning outcomes for undergraduate level computer organization courses.

关键词： Surveys computer science Systematics Computational modeling Standards organizations Education Organizations Graph theory Indexes Object recognition

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Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models 7

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Jayabalaji, K.A. Laxmaiah, Bagam Thatipudi, Jayendra Gopal Chandraprabha, K. Badhe, Pravin Balaram, Allam Sri Krishna Arts And Science College Department of Software Systems Tamilnadu Coimbatore India Cmr Technical Campus Department of Cse Telangana Kandlakoya India University of North Texas Department of Computer Science DentonTX76203 United States Bannari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Sinhgad College of Pharmacy Swalife Biotech Department of Pharmacology Pune India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. This study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. The proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. These features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly. © 2023 IEEE.

关键词： computer aided diagnosis

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A Holistic QoS View of Crowdsourced Edge Cloud Platform 31

A Holistic QoS View of Crowdsourced Edge Cloud Platform

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Shen, Shihao Feng, Yicheng Xu, Mengwei Zhang, Cheng Wang, Xiaofei Wang, Wenyu Leung, Victor C.M. College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China Institute of Technology Tianjin University of Finance and Economics Tianjin China Co. Ltd. Shanghai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350399738

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users. © 2023 IEEE.

关键词： Edge computing

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Lossy LiDAR Point Cloud Compression via Cylindrical 3D Convolution Networks

Lossy LiDAR Point Cloud Compression via Cylindrical 3D Convo...

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IEEE International Conference on Image Processing

作者： Yelang Gao Pingping Zhang Xu Wang College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science City University of Hong Kong Hong Kong China

Compared with object-level and human-level point clouds, LiDAR point clouds have larger data scales and are more sparse, posing a challenge for the existing learning-based lossy compression scheme. In this paper, we resolve this issue by transforming the point cloud into a cylindrical coordinate system. In this way, we can better retain points close to the sensor with a high density while extending the receptive field of convolution in areas of low point density. Following cylindrical quantization, an autoencoder is utilized to progressively downsample voxels. The coordinates and latent features are compressed by G-PCC and hyperprior-based entropy encoding respectively. The results demonstrate that our approach performs better than PCGCv2. The visualization results also show that our algorithm can better retain the shape of objects. Ablation studies further prove the efficiency of the cylindrical coordinates. The code is publicly available at https://***/AirManH/cylindrical_pcc.

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Leveraging Predictive Maintenance for Photovoltaic Systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th International Conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Battery management systems

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal Networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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2023 International Conference on Frontiers of Robotics and software engineering, FRSE 2023

作者： Cao, Dun Dai, Renhua Zhao, Tianyu Alqahtani, Fayez Tolba, Amr Sherratt, R. Simon Zhu, Min Changsha University of Science and Technology School of Computer and Communications Engineering Hunan Changsha China Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadcast Wireless Communication and Sensor Network Technology China King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia The University of Reading School of Biomedical Engineering RG6 6AY United Kingdom Zhejiang Shuren University College of Information Science and Technology Zhejiang31000 China

ISBN: (纸本)9798350301113

Though introducing the Region Proposal Network (RPN) from object detection enabled Siamese trackers' success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance. © 2023 IEEE.

关键词： Object detection

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