检索结果-内蒙古大学图书馆

Intrusion detection system in distributed cloud computing: Hybrid clustering and classification methods

Measurement: Sensors 2023年 25卷

作者： Samunnisa, K. Kumar, G. Sunil Vijaya Madhavi, K. Research Scholar Department of Computer Science and Engineering JNTUA A.P Anantapur India Professor of CSE & Dean - CSE and Allied Departments Lords Institute of Engineering & Technology Telangana Hyderabad India Professor & HOD of CSE Department JNTUA College of Engineering A.P Anantapur India

Cloud Computing is popular nowadays due to its storage and data access services. Security and privacy are prime concerns when network threats increase. Cloud computing offers organizations and enterprises a scalable, flexible, and cost-effective infrastructure to store data on the Web. An anomaly-based IDS implementation protects the integrity of the data in a database by identifying and quarantining records when something appears to have changed unexpectedly. Machine learning based clustering and classification methods are used for anomaly based IDS attack classification and scalability in advanced networking environments. Machine learning is a fast, efficient, and adaptable approach to develop intrusion detection models that can deal with emerging threats, i.e., known and unknown attacks (including zero-day attacks). This paper proposes an efficient Hybrid clustering and classification models for implementing an anomaly-based IDS for malicious attack type classifications such as normal (no intrusion), DoS, Probe, U2R, and R2L using threshold-based functions, and the results are tested with two different threshold values (e), 0.01 & 0.5. The experiments have been performed on two tested datasets, namely, NSL-KDD and KDDcup99. Detection rate, False alarm ratio, and accuracy have been used to study the performance of the proposed methodology. After applying the proposed approach, the K-means with random forest has been shown at two different threshold values to have a better classification accuracy, detection rate, and false alarm rate of 99.85%, 99.78% and 0.09% on the NSL-KDD dataset and 98.27%, 98.12% and 2.08% respectively on the KDDcup99 dataset. © 2022 The Authors

关键词： Distributed cloud

来源：评论

学校读者我要写书评

暂无评论

Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

arXiv

引用

arXiv 2022年

作者： Hu, Qin Li, Feng Zou, Xukai Xiao, Yinhao Department of Computer and Information Science Indiana University-Purdue University IndianapolisIN United States Department of Computer Information and Graphics Technology Indiana University-Purdue University IndianapolisIN United States School of Information Science Guangdong University of Finance and Economics Guangzhou China

An emerging computational paradigm, named federated edge learning (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding whether to participate in FEL or not given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize mechanism design to enable truthful information solicitation. With the help of correlated equilibrium, we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

引用

BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

来源：评论

学校读者我要写书评

暂无评论

Multiple Redundant Weighted Cluster Path Scheduling Scheme for Damaged Sensor-Based Wireless Sensor Network

Multiple Redundant Weighted Cluster Path Scheduling Scheme f...

引用

IEEE Future Networks World Forum (FNWF)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Houbing Song Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Department of Electrical & Electronic Engineering Science University of Cyprus Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Information Systems University of Maryland Baltimore Country Baltimore MD USA

In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

引用

29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security

IoT Based Human Biometric System Using Alcohol Level Detecti...

引用

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Anurag Sinha Nitish Kumar Amrita Ticku Vyom Modi Meghal Shah Kapil Shrivastava School of Computing and Information Sciences IGNOU New Delhi India Department of Computer Science Lovely Professional University Phagwara Punjab India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Rajkot Gujarat India Department of Computer Science & Engineering Charotar University of Science & Technology Gujarat India Assistant professor Department of computer engineering and applications GLA University mathura India

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver; they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks

引用

Procedia computer science 2023年 230卷 257-263页

作者： M Priya S Castro K Sathesh Kumar N Kalaivani Assoc. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam 611002 Asst. Professor Dept. of Information Technology Karpagam College of Engineering Coimbatore 641032 Asst. Professor Dept. of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tiruchengode 637205 Assoc. Professor Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore 641041

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs.

关键词： Named Data Networks CP attacks, Efficiency, Overhead analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Network Lifetime of WSNs through the Implementation of a Modified Ant Colony Optimization Algorithm

引用

Procedia computer science 2023年 230卷 368-376页

作者： Malathi Murugesan Kanimozhi R Dharani K G Devi D Assoc. Professor Dept. of Electronics and Communication Engineering E. G. S. Pillay Engineering College Nagapattinam - 611002 Asst. Professor Dept. of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Deviyakurichi Assoc. Professor Dept. of Electronics and Communication Engineering Akshaya College of Engineering & Technology Coimbatore Asst. Professor Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology

Energy efficient routing has been the mainstay of research in wireless sensor networks. With rising utility of WSNs in a broad range of applications, a significant volume of research contributions are targeted towards obtaining an energy efficient routing with a good quality of service (QoS) metric. In recent times, the utility of evolutionary algorithm for such multi-objective problems has been on the rising trend as evolutionary models closely mimic the real time scenario of any problem optimization. In this research work, a modified ant colony optimization (M-ACO) has been proposed and implemented for optimization in terms of energy conservation and thereby to extend the network lifetime of the network

关键词： WSN Intelligent Routing Energy Efficiency Modified Ant Colony Optimization Hierarchical protocols

来源：评论

学校读者我要写书评

暂无评论

RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture

arXiv

引用

arXiv 2024年

作者： Pan, Xiaoyu Zheng, Bowen Jiang, Xinwei Zeng, Zijiao Kou, Qilong Wang, He Jin, Xiaogang State Key Lab of CAD & CG Zhejiang University China ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab China Tencent Games Digital Content Technology Center China Co. LTD China Department of Computer Science UCL Centre for Artificial Intelligence University College London United Kingdom

Optical motion capture (MoCap) is the "gold standard" for accurately capturing full-body motions. To make use of raw MoCap point data, the system labels the points with corresponding body part locations and solves the fullbody motions. However, MoCap data often contains mislabeling, occlusion and positional errors, requiring extensive manual correction. To alleviate this burden, we introduce RoMo, a learning-based framework for robustly labeling and solving raw optical motion capture data. In the labeling stage, RoMo employs a divide-and-conquer strategy to break down the complex full-body labeling challenge into manageable subtasks: alignment, full-body segmentation and part-specific labeling. To utilize the temporal continuity of markers, RoMo generates marker tracklets using a K-partite graph-based clustering algorithm, where markers serve as nodes, and edges are formed based on positional and feature similarities. For motion solving, to prevent error accumulation along the kinematic chain, we introduce a hybrid inverse kinematic solver that utilizes joint positions as intermediate representations and adjusts the template skeleton to match estimated joint positions. We demonstrate that RoMo achieves high labeling and solving accuracy across multiple metrics and various datasets. Extensive comparisons show that our method outperforms state-of-the-art research methods. On a real dataset, RoMo improves the F1 score of hand labeling from 0.94 to 0.98, and reduces joint position error of body motion solving by 25%. Furthermore, RoMo can be applied in scenarios where commercial systems are inadequate. The code and data for RoMo are available at https:// ***/non-void/RoMo. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion capture

来源：评论

学校读者我要写书评

暂无评论

Hierarchical attention fusion for geo-localization

arXiv

引用

arXiv 2021年

作者： Yan, Liqi Cui, Yiming Chen, Yingjie Liu, Dongfang Department of Computer Science and Technology Fudan University China Department of Electrical and Computer Engineering University of Florida United States Department of Computer Graphics Technology Purdue University United States

Geo-localization is a critical task in computer vision. In this work, we cast the geo-localization as a 2D image retrieval task. Current state-of-the-art methods for 2D geo-localization are not robust to locate a scene with drastic scale variations because they only exploit features from one semantic level for image representations. To address this limitation, we introduce a hierarchical attention fusion network using multi-scale features for geo-localization. We extract the hierarchical feature maps from a convolutional neural network (CNN) and organically fuse the extracted features for image representations. Our training is self-supervised using adaptive weights to control the attention of feature emphasis from each hierarchical level. Evaluation results on the image retrieval and the large-scale geo-localization benchmarks indicate that our method outperforms the existing state-of-the-art methods. © 2021, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：