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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30780 条记录，以下是1401-1410 订阅

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DDoS-Net: Classifying DDoS Attacks in Wireless sensor Networks with Hybrid Deep Learning 6

DDoS-Net: Classifying DDoS Attacks in Wireless Sensor Networ...

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6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Reza, Farhana Brac University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

With the ubiquitous deployment of Wireless sensor Networks (WSNs) in diverse applications, ensuring their security against cyber threats is imperative. One significant challenge is the susceptibility of WSNs to distributed Denial of Service (DDoS) attacks, necessitating effective countermeasures. This research addresses the critical issue of DDoS attacks in WSNs, recognizing the diverse and imbalanced nature of WSN data. The imbalance poses challenges for intrusion detection systems, and existing solutions often fall short in providing comprehensive protection. The study introduces DDoS-Net, a hybrid Deep Learning (DL) model that integrates Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) architectures. DDoS-Net is designed to handle data imbalance effectively and incorporates thorough feature analysis to enhance the model's detection capabilities. Evaluation on the WSN-BFSF dataset demonstrates the exceptional performance of DDoS-Net. The model achieves an accuracy of 98.12%, precision of 98.16%, recall of 98.12%, and an impressive f1-score of 0.98 in minimal epochs. These results surpass existing state-of-the-art models, showcasing the efficacy of the proposed DL approach in countering DDoS threats in WSNs. © 2024 ieee.

关键词： Wireless sensor networks

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Proposing A Cloud and Edge computing Based Decision Supportive Consolidated Farming System By Sensing Various Effective Parameters Using IoT

Proposing A Cloud and Edge Computing Based Decision Supporti...

引用

ieee international IOT, Electronics and Mechatronics conference (IEMTRONICS)

作者： Uddin, Md Ashif Dey, Uzzwal Kumar Akter, Moriom Khulna Univ Elect & Commun Engn Discipline Khulna 9208 Bangladesh

ISBN: (纸本)9781665486842

Now-a-days, we are facing significant challenges as a result of food shortages and a lack of large-scale food production utilizing traditional methods. As a result, global food supply rivalry has become the most pressing issue of the world. Cloud computing based on Internet of Things (IoT) and Wireless sensor Networks (WSN) have become a comforting part of the solution to overcome this situation by replacing traditional methods in food production and monitoring systems. In this work, we represent a consolidated farming system with LED, SMS, E-mail, and website-based monitoring using WSN and IoT. To monitor a smart farm, several effective sensors are utilized in various industries, such as agriculture, aquaculture, livestock, air quality, weather monitoring, and health monitoring. All sensor's data is reserved on the IoT cloud platform as well as our built website named Website for Consolidated Smart Farms Monitoring (WCSFM), allowing a concerned person to readily access all relevant data from anywhere on the planet. Our model notifies the end user of expected information based on predefined sensor values by sending SMS, E-mail notifications, and turning on LEDs. Moreover In remote places, our IoT and WSN-based approaches may be more useful.

关键词： Internet of Things Cloud computing Wireless sensor Networks Consolidated Farms Monitor sensors

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Proceedings of the 2023 ieee international conference on Computer, Information, and Telecommunication systems, CITS 2023

Proceedings of the 2023 IEEE International Conference on Com...

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2023 ieee international conference on Computer, Information, and Telecommunication systems, CITS 2023

ISBN: (纸本)9798350336092

The proceedings contain 33 papers. The topics discussed include: deep learning polar convolutional parallel concatenated (DL-PCPC) channel decoding for 6G communications;cyber deception system based on Monte Carlo simulation in the mobile edge computing (MEC);lightweight hash function based on Fermat number transform (FNT);automatic recognition for LPI radar waveform using sequence-based deep learning model;a transfer learning based CSI indoor localization using GASF image construction;profit aware resource allocation in fog computing: a Stackelberg game approach;proposal for a security model applying artificial intelligence for administrative management in a higher education institution;on implementation of sparse finite impulse response secondary path for active noise control;high layer coding for video transmission;deep neural network for user attribute recognition in metaverse hand images;lifetime estimation and reparameterization of GPS devices for tracking applications;a reparameterization and monitoring platform for energy efficient GPS trackers in behavioral analysis of wildlife;and scheduling delay-sensitive bag-of-task jobs with imprecise computations on distributed resources.

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Personalized User Profiles-based Insider Threat Detection for distributed File System 21

Personalized User Profiles-based Insider Threat Detection fo...

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21st ieee international conference on Trust, Security and Privacy in computing and Communications (ieee TrustCom)

作者： Xin, Wu Shen, Qingni Feng, Ke Xia, Yutang Wu, Zhonghai Lin, Zhenghao Peking Univ Acad Adv Interdisciplinary Studies Ctr Data Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Sch Econ Beijing Peoples R China Boston Univ Questrom Sch Business Coll Arts & Sci Boston MA 02215 USA

ISBN: (纸本)9781665494250

In recent years, data security incidents caused by insider threats in distributed file systems have attracted the attention of academia and industry. The most common way to detect insider threats is based on user profiles. Through analysis, we realize that based on existing user profiles are not efficient enough, and there are many false positives when a stable user profile has not yet been formed. In this work, we propose personalized user profiles and design an insider threat detection framework, which can intelligently detect insider threats for securing distributed file systems in real-time. To generate personalized user profiles, we come up with a time window-based clustering algorithm and a weighted kernel density estimation algorithm. Compared with non-personalized user profiles, both the Recall and Precision of insider threat detection based on personalized user profiles have been improved, resulting in their harmonic mean F1 increased to 96.52%. Meanwhile, to reduce the false positives of insider threat detection, we put forward operation recommendations based on user similarity to predict new operations that users will produce in the future, which can reduce the false positive rate (FPR). The FPR is reduced to 1.54% and the false positive identification rate (FPIR) is as high as 92.62%. Furthermore, to mitigate the risks caused by inaccurate authorization for users, we present user tags based on operation content and permission. The experimental results show that our proposed framework can detect insider threats more effectively and precisely, with lower FPR and high FPIR.

关键词： Data Security Insider Threat Detection User Profile Machine Learning distributed File System HDFS

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B4W: A Smart Wireless Intruder Detection System

B4W: A Smart Wireless Intruder Detection System

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ieee international conference on Communications (ieee ICC)

作者： Sharma, Suryansh Venkata, Prabhakar T. Singhal, Shalakha Kumar, Gogineni Gopi Sunanth Prasad, R. Venkatesha Delft Univ Technol Delft Netherlands Indian Inst Sci Bengaluru Karnataka India

ISBN: (纸本)9781538674628

Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which can be arduous, expensive, and inefficient. To address these issues we propose an end-to-end smart acoustic surveillance solution for intrusion detection using a simple low-cost system called Balls for Walls (B4W). The objective is to create a network of sensors that could also be remotely launched. The nodes responsible for surveillance employ audio sensors which are packaged within hard balls thus allowing the launch of these sensors from a distance of over 500 m. We use microphones for detecting human activity inferred through sensing the sound of footsteps against background noise. We evaluate the systems across five different terrain types. We propose a novel, low complexity detection algorithm called SEED which leverages signal energy and shape to distinguish humans from ambient noise. B4W offers a maximum detection rate of 98.3% on dry leaves and a low false alarm rate of 0.9%. The system is energy efficient to last a maximum of 170 days and it is orientation agnostic. The proposed system has been extensively tested across varying terrains and ambient signal scenarios to demonstrate its efficacy.

关键词： Wireless sensor Networks Localization Surveillance Intruder Detection Acoustic sensors Edge computing

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 ieee.

关键词： distributed ledger

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distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

Distributed Quantum Learning with co-Management in a Multi-t...

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2023 ieee international conference on Big Data, BigData 2023

作者： D'Onofrio, Anthony Hossain, Amir Santana, Lesther Machlovi, Naseem Stein, Samuel Liu, Jinwei Li, Ang Mao, Ying Fordham University Computer and Information Science Department United States United States Florida A&M University United States

ISBN: (纸本)9798350324457

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting. © 2023 ieee.

关键词： distributed Quantum computing Multi-node Quantum Quantum Resource Management

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To Design and Implement a Radar System using Arduino and Ultrasonic sensor 2

To Design and Implement a Radar System using Arduino and Ult...

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2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Rajeswari, Mummadi Lingappa, J. Basha, Mulla Gouse Deepa, Velagapuri Sravani, Vajrapu Srilatha, Kommidi Department of ECE Teegala Krishan Reddy Engineering College India Department of EEE Geethanjali College of Engineering and Technology India

ISBN: (纸本)9798350340235

Radar technology has been a cornerstone of numerous applications, from aviation to meteorology, offering invaluable capabilities for object detection and tracking. Traditional radar systems, while powerful, often come with significant costs and complexities, limiting their accessibility in various fields. In response to these limitations, this research endeavors to design and implement a radar system using Arduino microcontrollers and ultrasonic sensors. This innovative approach leverages the flexibility and affordability of the Arduino platform and the distance measurement precision of ultrasonic sensors to create a radar-like system. The research encompasses the development of the hardware, data acquisition, signal processing, and real-time visualization, providing an accessible and cost-effective alternative for radar-based applications. The study explores the integration of Arduino technology and ultrasonic sensors into radar systems, with a focus on simplicity, affordability, and practicality. By bridging the gap between traditional radar systems and DIY electronics, this research aims to democratize radar technology and open doors for applications in robotics, smart cities, industrial automation, and education. The findings presented here offer new perspectives on radar system design and underscore the potential for radar innovation in emerging fields. © 2023 ieee.

关键词： Arduino Radar system Servomotor Ultrasonic sensor

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Wireless sensor Networks for Natural Disaster Management - A Survey

Wireless Sensor Networks for Natural Disaster Management - A...

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2023 international conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Singh, Manpreet Sharma, Kapil Chitkara University Institue of Engineering and Technology Rajpura India

ISBN: (纸本)9798350302820

Wireless sensor Networks (WSNs) are networks of independently operating, geographically distributed sensors that monitor real-world objects or measure environmental factors and broadcast the data to a base station in the network. Physical, chemical, and biological sensors are the three major categories of sensors. The Wireless sensor Networks are made up of a data distribution network and a network for data acquisition. A central station oversees and manages the networks. When compared to traditional ad hoc networks, wireless sensor networks are a great alternative. Using wireless sensor networks, it is possible to monitor a wide range of factors, including temperature, humidity, pollution levels, and many more. Wireless sensor Networks applications related to disaster management include tsunami, earthquake, flood monitoring, landslide detection, volcanic eruption, drought, and forest fire detection. Wireless sensor networks make use of technologies that can send out an alarm for an instant rescue effort to start in a disaster area. This paper surveys different systems of Wireless sensor Networks in Natural Disaster Management for Emergency Alerting and monitoring operations. © 2023 ieee.

关键词： Wireless sensor networks

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Markov Blanket Composition of SLOs 8

Markov Blanket Composition of SLOs

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8th ieee international conference on Edge computing and Communications, EDGE 2024

作者： Sedlak, Boris Pujol, Victor Casamayor Donta, Praveen Kumar Dustdar, Schahram Distributed Systems Group Vienna1040 Austria

ISBN: (纸本)9798350368499

Smart environments use composable microservices pipelines to process Internet of Things (IoT) data, where each service is dependent on the outcome of its predecessor. To ensure Quality of Service (QoS), individual services must fulfill Service Level Objectives (SLOs);however, SLO fulfillment is dependent on resources (e.g., processing or storage), which are scarcely available within the Edge. Hence, when distributing services over heterogeneous devices, this raises the question of where to deploy each service to best fulfill both its own SLOs as well as those imposed by dependent services. In this paper, we maximize SLO fulfillment of a pipeline-based application by analyzing these dependencies. To achieve this, services and hosting devices alike are extended with a Markov blanket (MB) - a probabilistic view into their internal processes - which are composed into one overarching model. Given a mutable set of services, hosts, and SLOs, the composed MB allows inferring the optimal assignment between services and edge devices. We evaluated our method for a smart city scenario, which assigned pipelined services (e.g., video processing) under constraints from subsequent services (e.g., consumer latency). The results showed how our method can support infrastructure providers by optimizing SLO fulfillment for arbitrary devices currently available. © 2024 ieee.

关键词： Markov processes

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