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

共 3169 条记录，以下是121-130 订阅

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Generating a distributed network attack dataset based on weather station IoT Cyber Physical systems 7

Generating a distributed network attack dataset based on wea...

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7th international conference on Artificial Intelligence, Big Data, computing and Data Communication systems (ICABCD)

作者： Johnson, David Roy, Kaushik North Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9798350387919;9798350387902

There has been a large increase in Internet of Things (IoT) devices used in many parts of life. These devices, also called edge devices, have been known to be vulnerable to attackers and to be used as a medium for network attacks. A simple loT device uses one or more sensors to read data from the real world and performs a simple action based on the sensor readings, such as a motion sensor in a home turning on the lights when motion is detected. Building a dataset to model real-world network traffic, along with pseudo-realistic uses of network attacks is essential for creating an intrusion detection system (IDS). In this paper, we develop an loT network anomaly dataset using synthetic and real-world network attacks. Virtual machines with Ubuntu 22.04 were used to contain the network data in a sandboxed environment. For our edge devices, we use Le Potato by Libre Computers as an alternative to Raspberry Pi 3, which is a popular single board computer used for home automation and hobbyist projects where the Raspberry Pi acts as the computer for the smart device. Each device has Waveshare Weather sensor attached to it, which reads climate data such as temperature, humidity, and barometric pressure. The edge devices are located within the Cyber Defense and AI lab at North Carolina Agriculture and Technology State University. Real network attack tools were used to generate traffic to label the current data as attack data, along with the attack type. In the absence of the attack tools in use, the network traffic is labeled normal, or benign. The dataset is comprised of homogeneous data using environmental sensors that measure local climate data. In this study, we use XGBoost, LSTM+CNN, and Multilayer Perceptron along with various resampling methods to evaluate our synthetic dataset. In our results, we find that XGBoost produces the best performance at classifying attacks when using RandomUndersampler.

关键词： anomaly detection deep neural network network attack IoT Internet of Things distributed learning federated learning

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The Smart Optimization of Security systems in Wireless Personal sensor Networks 2

The Smart Optimization of Security Systems in Wireless Perso...

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2nd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Das, Sunanda Sindhu Madhuri, G. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Wireless personal sensor networks (WPSNs) are an emerging technology that provides a platform for monitoring and collecting data from a variety of sources. These networks are used to monitor environmental, health, and even lifestyle factors. As the use of WPSNs continues to grow, it is important to consider the security implications of using these networks. WPSNs rely on a variety of technologies to ensure secure communication between devices. These include encryption, authentication, secure protocols, and secure key management. Encryption is used to protect data from being intercepted and decoded by an unauthorized user. Authentication is used to verify the personality of the user and the device attempting to access the WPSN. Secure protocols are used to ensure secure communication between the devices and the server. Finally, secure key management is used to manage and store the encryption keys used to protect the data. In addition to encryption, authentication, and secure protocols, WPSNs must also be designed with a robust access control system. Access control systems are used to limit access to the WPSN based on user credentials and other factors. This assist ensure that authorized users alone can access the data stored on the WPSN. © 2023 ieee.

关键词： Authentication

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Engineering distributed Collective Intelligence in Cyber-Physical Swarms

Engineering Distributed Collective Intelligence in Cyber-Phy...

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international conference on distributed computing in sensor systems (dcoss)

作者： Gianluca Aguzzi Claudio Savaglio University of Bologna Cesena Italy University of Calabria Calabria Italy

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Cyber-physical swarms represent a paradigm shift in distributed systems, mirroring characteristics akin to natural swarms, such as self-organization, scalability, and fault tolerance. This paper delves into these complex systems, characterized by vast networks of cyber-physical entities with limited environmental awareness, yet capable of exhibiting emergent collective behaviors. These systems encompass a diverse array of scenarios, ranging from swarm robotics to the interconnectivity in smart cities, as well as the collaboration among augmented humans. The engineering of such systems presents unique challenges, primarily due to their intricate complexity and the spontaneous nature of their collective *** paper aims to dissect these challenges, offering a clear delineation of potential approaches. We present a comprehensive analysis, shedding light on the intricacies of engineering cyber-physical swarms and discussing modern solutions in engineering collective applications for such systems.

关键词： Privacy Smart cities Scalability Refining Swarm robotics Transforms Complexity theory

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Malware Detection By distributed systems with Partial Centralization 12

Malware Detection By Distributed Systems with Partial Centra...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Savenko, Bohdan Kashtalian, Antonina Lysenko, Sergii Savenko, Oleg Khmelnytskyi National University Khmelnytskyi29016 Ukraine

ISBN: (纸本)9798350358056

The paper proposes approach to use new tools for malware detection in corporate networks, which are distributed systems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. The results of experimental studies approved the efficiency of the proposed solution. © 2023 ieee.

关键词： distributed systems malware malware detection network security partial centralization worm-virus

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Novel Voice-based Authentication for Mission-critical Operations

Novel Voice-based Authentication for Mission-critical Operat...

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international conference on distributed computing in sensor systems (dcoss)

作者： I. B. Mnaouer A. Samsudeen M. Sherif J. Adithya S.E. Choutri G.B. Satrya SEAST Canadian University Dubai Dubai United Arab Emirates

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

We explore innovative voice-based authentication methods designed for high-stakes environments, distinguishing between knowledge-based authentication and voice-based authentication. Traditional methods, relying on passwords, face limitations against advanced cyber threats. Voice-based authentication, utilizing distinct vocal traits, emerges as a secure and userfriendly alternative. The paper aims to contribute to securing critical infrastructures by introducing an efficient authentication solution tailored for mission-critical operations through a random forest algorithm. The study concludes with experimental results that demonstrate the effectiveness of the Random Forest technique in achieving high accuracy levels when parameter tuning is applied (0.85816) compared to not using parameter tuning (0.8564).

关键词： Mission critical systems Knowledge based systems Authentication Passwords Forestry Internet of Things distributed computing

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MEMS RESERVOIR computing USING FREQUENCY MODULATED ACCELEROMETER 35

MEMS RESERVOIR COMPUTING USING FREQUENCY MODULATED ACCELEROM...

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35th ieee international conference on Micro Electro Mechanical systems conference (ieee MEMS)

作者： Mizumoto, Takahiro Hirai, Yoshikazu Banerjee, Amit Tsuchiya, Toshiyuki Kyoto Univ Kyoto Japan

ISBN: (纸本)9781665409117

We developed an intelligent MEMS sensor with data processing ability for data acquisition and conversion at low power consumption. It enables data processing at the sensing device for efficient distributed processing. We employed a MEMS reservoir which integrates sensing and computing functions in single device structure. In this research we designed a frequency modulated capacitive accelerometer and characterized its frequency response. The measured response was used for simulation of time series prediction and the machine learning task was successfully demonstrated.

关键词： Reservoir computing Frequency Modulated Accelerometer Intelligent sensor Nonlinear Resonator

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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location 25

indexPDT: A High Scalable Distributed Classification Approac...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sun, Zhijie Li, Jing Xie, Jun Zheng, Binfan Zeng, Li Zhao, Rongqian Huawei Technologies Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. The geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. The cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively. © 2023 ieee.

关键词： Location

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(POSTER) Navigating the Unknown: Anomaly Detection in sensor Nodes Based on Event Traces

(POSTER) Navigating the Unknown: Anomaly Detection in Sensor...

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international conference on distributed computing in sensor systems (dcoss)

作者： Saurabh Band Anna Foerster Dept. Sustainable Communication Networks University of Bremen Bremen Germany

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In wireless sensor networks, post-deployment is-sues persist despite extensive testing, primarily due to unpredictable environmental factors and limited debugging tools for resource-constrained end nodes. This challenge is particularly pronounced in remote applications such as extraterrestrial habitats. To address this, we propose a runtime anomaly detection and diagnosis method for resource-constrained sensor nodes. A key advantage of our approach is its ability to learn expected behavior from historical data, eliminating the need for explicit behavior modeling, unlike other runtime fault detection methods. Our method comprises three main components: logging, detection, and diagnosis. We log event traces on the sensor nodes, enabling activity tracking down to the variable level. For anomaly detection, we explore various methods, including state transition, execution interval analysis, and clustering. Subsequently, diagnosis is performed using the logged event traces.

关键词： Wireless sensor networks Runtime Navigation Fault detection Habitats Data models Environmental factors

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Adversarial Training on Limited-Resource Devices Through Asymmetric Disturbances

Adversarial Training on Limited-Resource Devices Through Asy...

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international conference on distributed computing in sensor systems (dcoss)

作者： Mohammed Rajhi Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In the domain of adversarial training, establishing machine learning model robustness remains a paramount challenge, especially in environments constrained by resources such as mobile and IoT devices. This paper introduces a transformative framework, employing asymmetric perturbations to ensure a bounded loss and augmented computational efficiency. Our approach stands on a solid theoretical foundation and undergoes comprehensive performance evaluations. Across various metrics, we validate the efficacy of our methodology, showcasing its readiness for broad implementation in resource-limited settings.

关键词： Training Adaptation models Computational modeling Perturbation methods Machine learning Solids Robustness

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Utilizing Natural Thermal Gradients as Micro Energy Sources for Wireless sensor Networks

Utilizing Natural Thermal Gradients as Micro Energy Sources ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Sven Pullwitt Lars Wolf Technische Universität Braunschweig Braunschweig Germany

The plethora of applications for Wireless sensor Networks (WSNs) result in a wide range of environmental conditions for these networks. In many cases, the energy supply is the limiting factor for the lifetime of a network, as changing batteries is very hard or completely unfeasible. Energy harvesting is a very promising concept, as it allows to achieve a theoretically infinite network lifetime. Using Thermoelectric Generators (TEGs) to convert the thermal gradient between the upper soil layers and the ambient air promises energy supply in cases where other commonly used energy sources, such as solar cells, cannot be used due to lack of sunlight. We investigate the performance of a TEG harvester in a long-term experiment. Furthermore, we analyze the energy collected in relation to factors such as sunlight and season and show the feasibility of powering a WSN node from this power source.

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