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Leak-Detector: An Improved Route leak Detection Method

Leak-Detector: An Improved Route leak Detection Method

引用

Dependability in sensor, Cloud and Big Data systems and Application (DependSys), IEEE International conference on

作者： Haoyang Gao Ning Li Yuancheng Xie Department of Computer Science Harbin Institute of Technology Weihai China

ISBN: (数字)9798331540500

ISBN: (纸本)9798331540517

BGP route leak represents a significant vulnerability in BGP security. The occurrence of BGP route leak can lead to increased internet latency and diminished service quality in less severe cases, and in more severe instances, it can result in large-scale regional outages or substantial traffic redirection, causing considerable financial losses to network operators and users. Thus, the ability to accurately and promptly identify route leak is of paramount importance for maintaining internet security. Current route leak detection methods typically involve a trade-off between accuracy and real-time performance, making it challenging to achieve both simultaneously. To address this issue, this paper proposes the Leak-Detector framework, which synthesizes the strengths of existing advanced methodologies. By integrating AS hegemony indicators with machine learning models, Leak-Detector provides accurate and real-time detection of route leak incidents, and it can precisely report both the suspects and the timing of the leak. Application of Leak-Detector to real-world route leak detection demonstrates that it achieves an accuracy of 85%, recall of 84%, and F1 score of 83%, with minimal detection latency. Furthermore, it accurately identifies most route leak suspects and their occurrence times.

关键词： Accuracy Internet security Machine learning Real-time systems Timing Security Leak detection

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Depth Information Fusion Using Radar-LiDAR-Camera Experimental Setup for ADAS applications

Depth Information Fusion Using Radar-LiDAR-Camera Experiment...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Dayananda B N Neetesh Srivastava Achala G Ashwin Nandagiri Pathipati Srihari Bethi Pardhasaradhi Linga Reddy Cenkeramaddi ECE Dept. National Institute of Technology Karnataka Surathkal India Sri Shashaprayathi Technologies Pvt. Ltd. Surathkal Mangalore India Department of Information and Communication Technology University of Agder Grimstad Norway

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Improved scene perception makes the safe driving of automotive vehicles (AVs) feasible. The most common automotive sensors for AV perception for detection, classification, a nd t racking a re t he Light Detection And Ranging (LiDAR), radar, and camera sensors. The most reliable sensors for determining range are LiDAR and radar. In this research, we consider the referencing from the camera-based object recognition to fuse the LiDAR and radar point cloud data. To minimize any unintended effects from sensor orientation and sampling time, all three sensors are installed, calibrated, and time-aligned for this experiment. Subsequently, the obtained camera sensor data is subjected to object detection using a MobileNet-based deep neural network (DNN). The radar and LiDAR point cloud data are projected with the two-dimensional bounding box width, length, and height used for object recognition. Following that, the range information from the radar and LiDAR is retrieved and combined using a weighted average fusion algorithm. This experiment is run on the ROS platform, using AWR1642 radar sensor and RealSense LiDAR camera L515 sensor. The object detection from the camera and conducting fusion on the radar and LiDAR sensor is a potential algorithm for the advanced Driver Assistant System (ADAS) emergency brake assistant (EBA) function.

关键词： Point cloud compression Laser radar Fuses Radar Artificial neural networks sensor fusion Cameras

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection

AI-Powered IoT Framework for Enhancing Building Safety throu...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S R Raja T. Rathi Devi J. Relin Francis Raj V. Krishna Sankar R. Santhana Krishnan R. Venkatalakshmi Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Christ University Bangalore India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Civil Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain.

关键词： Support vector machines Fuzzy logic Accuracy Buildings Boosting Stability analysis Real-time systems Safety Ensemble learning Monitoring

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GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM

GIIDS: Generalized Intelligent Intrusion Detection System fo...

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International conference on advanced Communication Technology (ICACT)

作者： Fahmina Kabir Nishat I Mowla Inshil Doh Division of Artificial Intelligence and Software Ewha Womans University Seoul Korea Department of Industrial Systems RISE Research Institutes of Sweden Sundsvall Sweden Department of Cyber Security Ewha Womans University Seoul Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different cross-validations.

关键词： Analytical models Scalability Stacking Intrusion detection Performance gain Autonomous aerial vehicles Robustness Data models Stability analysis Thermal stability

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Sectorized FMCW Radar by Modular System Design and MIMO Sub-Arrays for Automotive applications

Sectorized FMCW Radar by Modular System Design and MIMO Sub-...

引用

2022 International conference on Radar systems, RADAR 2022

作者： Alistarh, Cristian A. Podilchak, Symon K. Thompson, John Sellathurai, Mathini Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh United Kingdom Institute for Digital Communications University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781839537776

Automotive radars have evolved in recent years, however, angular resolution and fast processing times demand constant improvements in performance. In this paper, a novel proof-of-concept radar sensor is presented. In particular, a realtime detection approach is proposed which uses a modular radar architecture to achieve a wide field-of-view (FOV). The radar also offers improved angular resolution due to a sparse non-uniform antenna receiver and results are compared to a more standard uniform linear array equivalent. The antennas achieve improved percentage bandwidths in comparison to a more standard microstrip patch antenna solution at the same carrier frequency. In addition, the sub-modular system resolves targets with lower radiation losses in comparison with an equivalent uniform linear array under the same conditions. The proposed radar system is able to achieve a half-power beamwidth of 2.2 degrees using 32 antenna elements arranged in a non-uniform sparse configuration and can detect multiple targets with a range resolution of 10 cm. © 2022 IET conference Proceedings. All rights reserved.

关键词： Slot antennas

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MAIA S2 Versus Sentinel 2: Spectral Issues and Their Effects in the Precision Farming Context 21st

MAIA S2 Versus Sentinel 2: Spectral Issues and Their Effects...

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21st International conference on Computational Science and Its applications (ICCSA)

作者： Sarvia, Filippo De Petris, Samuele Orusa, Tommaso Borgogno-Mondino, Enrico Univ Torino DISAFA L Go Braccini 2 I-10095 Grugliasco TO Italy

ISBN: (纸本)9783030870072;9783030870065

Precision agriculture involves the integration of new technologies including Geographic Information systems (GIS), Global Navigation Satellites systems (GNSS) and Remote Sensing (RS) platforms and sensors to allow farmers to maximize the cost-benefit ratio, rather than using the traditional whole-field approach. MAIA S2 is a recent multispectral aerial sensor in strong expansion in the agricultural sector. In this work, MAIA S2 spectral properties were compared with the correspondent Sentinel-2 ones, focusing on possible effects that differences could induce onto agriculture related deductions. The reference dataset was acquired by aerial survey and radiometric and geometric pre-processing achieved to generate the correspondent at-the-ground reflectance multispectral orthomosaic by ordinary workflow as suggested by sensor suppliers. A comparison was achieved at single band level to test spectral consistency of the two data. It showed a low correlation in the red-edge and infrared bands (r < 0.5);oppositely, a higher correlation was found for the visible bands (r > 0.8). To test the effects of found discrepancies between the two data, the correspondent prescription maps were generated using the same clustering criterion. They were then compared to test consistency of deductions.

关键词： Precision agriculture MAIA S2 sensor Sentinel-2 Precision farming Prescription maps MAIA S2 radiometric accuracy

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Miniature Modular sensor for Chemical Species Tomography with Enhanced Spatial Resolution

Miniature Modular Sensor for Chemical Species Tomography wit...

引用

IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Rui Zhang Jiangnan Xia Hugh McCann Chang Liu Ihab Ahmed Mohamed Pourkashanian Andrew Gough Ian Armstrong Michael Lengden Walter Johnstone School of Engineering University of Edinbutgh Edinburgh UK Department of Mechanical Engineering University of Sheffield Sheffield UK Department of Electronic and Electrical Engineering University of Strathclyde Glasgow UK

The introduction of low/zero-carbon fuels in gas turbine engines poses challenges to the stability and efficiency of combustion processes. As a result, there is a growing demand for advanced imaging techniques that can provide enhanced spatial resolution to monitor and analyze the physiochemical parameters involved. Chemical Species Tomography (CST), utilizing multiple line-of-sight Laser Absorption Spectroscopy (LAS) measurements, has gained popularity in industrial applications due to its non-intrusive nature, robustness, and high sensitivity. Typically, existing CST sensors have few centimeters beam spacing and are highly customized for specific applications. Therefore, the limited beam spacing, and lack of generality pose challenges in terms of providing sufficient sampling and adaptability for wider application scenarios. To enhance the spatial resolution as well as improve the reconfiguration of the CST sensors, this paper proposes a miniature modular CST sensor with a 1cm beam spacing. The use of off-the-shelf components significantly reduces the cost of the system, making it more practical for industrial implementation. Experimental evaluation of the prototype sensor has been conducted for measurements of temperature and water vapor concentration. The results show consistent and accurate measurements, indicating good reliability of the sensor. The proposed miniature modular sensor offers increased flexibility and adaptability to the various CST systems, paving the way for its commercialization in industrial settings.

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Modelling and analysis of QPD based track sensor developed for inter-satellite optical communication

Modelling and analysis of QPD based track sensor developed f...

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International Symposium on advanced Networks and Telecommunication systems (ANTS)

作者： Nirlesh Singh Ravikumar Keshavappa Priyabrata Senapati Bijoy Raha K. C. Praveen K. Kalyani Sanjay Mittal S.B. Umesh S.P. Karantha Laboratory for Electro Optics Systems LEOS-ISRO Bangalore INDIA

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

Free Space Optical Communication (FSO) is an emerging and promising technology that utilizes optical signals to transmit data through free space. It offers several advantages, including high data rates, low latency, security, and immunity to electromagnetic interference. FSO systems find applications in various fields, including telecommunications, satellite communication, disaster recovery, and military operations. Utilizing lasers for free space optical communication presents an appealing alternative to conventional radio frequency (RF) communication methods, particularly in managing substantial data volumes while simultaneously reducing size, weight, and power (SWaP) requirements. Among the formidable challenges in the realm of satellite-based optical communication systems lies the necessity for highly precise acquisition and tracking of target satellites. Quad Photodetector (QPD) tracking sensors stand out as the preferred choice for discerning angular positions due to their rapid responsiveness and excellent accuracy. In this paper, we expound upon the operational principles and mathematical modelling of QPD-based tracking sensors, aiming to attain optimal sensitivity and meet predefined field of view (FOV) specifications. Additionally, we elucidate the focusing technique, detail the testing setup, and present the achieved outcomes of the tracking sensor.

关键词： Target tracking Sensitivity Free-space optical communication Prototypes Focusing Mathematical models Photodetectors Telecommunications Optical sensors Testing

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Advancements in Weather Monitoring System: A Comprehensive Review

Advancements in Weather Monitoring System: A Comprehensive R...

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International conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Manjyot Singh Soni Mitra Jisan Bera Kaustav Rajib Ghosh Institute of Engineering and Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This review provides an in-depth analysis of recent advancements and key trends in weather monitoring systems. It emphasizes the progress made in technologies and highlights their significant contributions to the field. These systems are essential tools for collecting and interpreting atmospheric data to predict and monitor weather conditions, supporting applications such as disaster management, agriculture, aviation, and climate research. The review investigates modern sensor innovations, IoT-enabled systems, and remote sensing technologies that have transformed real-time data collection and analysis. It discusses advancements in performance factors, including accuracy, scalability, energy efficiency, and costeffectiveness. Furthermore, the paper explores advanced topics like machine learning-driven weather prediction, satellite-based observation systems, and the integration of cloud computing for improved data processing capabilities. Challenges such as hardware constraints, data reliability, and integration complexities are also examined, along with potential strategies to overcome them. By synthesizing existing research, this study offers valuable insights for professionals in the field, helping them enhance and innovate weather monitoring systems. The findings provide overview of present state-of-the-art technologies and propose directions for future research and development, emphasizing the importance of this field in addressing global challenges.

关键词： Cloud computing Technological innovation Accuracy Reviews Weather forecasting Real-time systems Hazards Forecasting Monitoring Remote sensing

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Generative Adversarial Network based Digital Predistortion Linearization for 6G Joint Communications and Sensing

Generative Adversarial Network based Digital Predistortion L...

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IEEE Topical conference on Power Amplifiers for Wireless and Radio applications (PAWR)

作者： Kevin Chuang Muhammad Saad Zia Weite Zhang Communications & Cloud Analog Devices Inc Wilmington MA USA Central AI Analog Devices Inc Boston MA USA

ISBN: (数字)9798331507275

ISBN: (纸本)9798331507282

advanced digital predistortion (DPD) linearization systems have been the cornerstone of successful and energy-efficient base station deployments, particularly when the radio frequency power amplifier (PA) is pushed to operate near its maximum power ratings to achieve high wireless capacity and provide new service features. In this work, we explore how machine learning (ML) techniques can be effectively incorporated into the design methodology of PA linearization for the emerging 6G use case, namely joint communications and sensing (JCAS). Our experimental results indicate that adopting a generative adversarial network for ML-based DPD model parameters extraction consistently yields better adjacent channel leakage power ratios and normalized mean square errors for different JCAS waveforms compared to the adaptive moment estimation method using a mean squared error loss function. Moreover, this improvement is achieved using less training data, resulting in shorter convergence times to reach a certain level of linearity performance.

关键词： 6G mobile communication Wireless communication Adaptation models Wireless sensor networks Adaptive systems Linearity Generative adversarial networks Predistortion sensors Parameter extraction

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