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Distort to Detect, not Affect: Detecting Stealthy sensor Attacks with Micro-distortion

Distort to Detect, not Affect: Detecting Stealthy Sensor Att...

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2021 IEEE International conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Sourav, Suman Chen, Binbin Advanced Digital Sciences Center Singapore Singapore

ISBN: (纸本)9781665415026

In this paper, we propose an effective and easily deployable approach to detect the presence of stealthy sensor attacks in industrial control systems, where (legacy) control devices critically rely on accurate (and usually non-encrypted) sensor readings. Specifically, we focus on stealthy attacks that crash a sensor and then immediately impersonate that sensor by sending out fake readings. We consider attackers who aim to stay hidden in the system for a prolonged period. To detect such attacks, our approach relies on continuous injection of 'micro distortion' to the original sensor's readings. In particular, the injected distortion should be kept strictly within a small magnitude (e.g., 0.5 % of the possible operating value range), to ensure it does not affect the normal functioning of the ICS. Our approach uses a pre-shared secret sequence between a sensor and the defender to generate the micro-distortions. One key challenge is that the micro-distortions injected are often much lower than the sensor's actual readings, hence can be easily overwhelmed by the latter. To overcome this, we leverage the observation that sensor readings in many ICS (and power grid in particular) often change gradually in a significant fraction of time (i.e., with small difference between consecutive time slots). We devise a simple yet effective algorithm that can detect stealthy attackers in a highly accurate and fast (i.e., using less than 100 samples) manner. We demonstrate the effectiveness of our defense using real-world sensor reading traces from two different smart grid systems. © 2021 IEEE.

关键词： Legacy systems

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The Influence of Computer Vision on Road Traffic

The Influence of Computer Vision on Road Traffic

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International conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Melvin Verdinan Mulia Hans Dominic Tirta Said Achmad Rhio Sutoyo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Implementing computer vision in road traffic management brings significant impacts, making transportation systerms more efficient, safe, and sustainable. This advanced technology analyzes real-time traffic conditions, providing crucial data for effective traffic management. Using computer vision, authorities can monitor traffic flow, detect incidents, and manage congestion more effectively. The primary aim of this paper is to explore the challenges and impacts associated with using computer vision in road traffic management while offering insights for future research and practical applications. A Systematic Literature Review (SLR) was conducted, adhering to the PRISMA Flowchart Diagram methodology, to identify and evaluate relevant studies in this field. The systematic approach ensures a comprehensive understanding of the current state of computer vision in the transportation sector. The findings from this review underscore the transformative potential of computer vision in enhancing traffic management systems. Moreover, this work highlights several existing challenges that need to be addressed to maximize the benefits of computer vision. These challenges include the need for more advanced algorithms capable of handling diverse and complex traffic scenarios and integrating this technology with existing infrastructure. By identifying these challenges, the paper provides valuable insights for future research, emphasizing the importance of developing more robust and adaptive computer vision systems. Ultimately, this research aims to contribute to advancing computer vision in road traffic management, promoting safer and more efficient transportation systems worldwide.

关键词： YOLO Computer vision Accuracy Transportation sensor fusion Traffic control Cameras Road traffic Real-time systems Systematic literature review

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AI-Driven Insights: Enhancing Personality Type Prediction with advanced Machine Learning Algorithms

AI-Driven Insights: Enhancing Personality Type Prediction wi...

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International conference on Contemporary Computing and Informatics (IC3I)

作者： Rajeev Semwal Nandita Tripathi Ajay Rana Udit Kumar Pandey Shilpa Parihar Mahesh Kumar Bairwa Amity Institute of Psychology and Allied Sciences Amity University Uttar Pradesh Greater Noida Campus Uttar Pradesh India Amity School of Engineering and Technology Amity University Uttar Pradesh Greater Noida Campus Uttar Pradesh India Department of Management Graphic Era Hill University Haldwani India Jai Narain Vyas University Jodhpur Rajasthan India Principal Incharge Hospitality and Tourism Department State Institute of Hotel Management Jhalawar Rajasthan India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Artificial intelligence (AI) and Internet of Things (IoT) technology together present transformative possibilities in the field of personality type prediction, with applications in psychology, human resources, marketing, and personal development. This work presents an in-depth investigation of IoT systems, such as sensor networks and data aggregation platforms, and advanced machine learning algorithms, including neural networks, support vector machines (SVM), and decision trees. It also proposes a conceptual system design leveraging these technologies to enhance the accuracy and efficiency of personality forecasts. Through a comprehensive literature review and conceptual system design, the paper identifies key challenges and constraints, including data quality, computational complexity, and ethical considerations. Additionally, it introduces hypothetical implementation scenarios to demonstrate the anticipated benefits and practical applications of the system, such as personalised insights and real-time data processing. The paper concludes with a discussion of future directions, emphasising the potential of emerging IoT technologies, like edge computing and 5G, and AI advancements to improve predictive capabilities and expand research opportunities. This study contributes to the growing body of knowledge at the intersection of AI, IoT, and psychology by providing crucial insights for developing innovative approaches across various domains.

关键词： Support vector machines Ethics Machine learning algorithms Accuracy Psychology systems architecture Transforms Real-time systems Internet of Things Artificial intelligence

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Multi-Coil-Based Inductive Power Transfer System For Insitu Soil Sensing Application

Multi-Coil-Based Inductive Power Transfer System For Insitu ...

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Micro and Nanotechnology for Power Generation and Energy Conversion applications (PowerMEMS)

作者： Sheng Ding John Sanchez Shad Roundy Ramesh Goel Cody Zesiger Darrin J. Young University of Utah USA Utah State University USA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

A 3-coil-based inductive power transfer system was designed and demonstrated for energizing a 20 mW underground battery-free soil moisture sensor with an improved efficiency and voltage gain compared to a 2-coil-based implementation. The 3-coil-based design employs an intermediate tuned coil, which is critically coupled to the last-stage coil interfacing the sensor, thus effectively mitigating the loading effect from the sensor to ensure an enhanced performance provided certain design conditions are met. The prototype system achieves an inductive power transfer efficiency and a voltage gain of 4.2% and 4.6, respectively, over a 30 cm distance in air. The system further demonstrates an efficiency of 3.2% and a voltage gain of 4 across a 30 cm depth of soil. The prototype system is also capable of wirelessly transferring 40 mW of power over the same distance with an efficiency of 4.8%, limited by the input stage power driving capability. The multi-coil-based system design approach is desirable for enabling advanced, wireless, and battery-free underground soil monitoring systems, which may demand high power dissipation.

关键词： Coils Wireless communication Wireless sensor networks Soil moisture Prototypes Moisture Voltage sensors Monitoring System analysis and design

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A Deep Learning-based Multi-Path Routing Protocol for Improving Security using Encryption in Underwater Wireless sensor Networks

A Deep Learning-based Multi-Path Routing Protocol for Improv...

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Electronics and Sustainable Communication systems (ICESC), 2020 International conference on

作者： K. Prakash S. Sathya Department of Computer Science VELS Institute of Science Technology & Advanced Studies(VISTAS) Chennai India Sri Sarada Mahavidyalayam Arts and Science College for Women’s Ulundurpet Department of Information Technology VELS Institute of Science Technology & Advanced Studies(VISTAS) Chennai India

Underwater Wireless sensor Networks (UWSN) is an interesting topic today in both scientific research and commercial applications. Major challenges of UASN design constraints are unreliable network lifetime and low battery power of underwater sensor nodes. In addition, sensor nodes may generate sensitive data that needs to be hidden. Therefore, encryption ciphers maintain security by encrypting the data before sending it. However, encryption methods require more computation and energy, and network lifetime is reduced. A key issue in this research area is that due to the complexity of underwater environments and slow transmission rates, inefficient architectures for multi-path variable data transmission are a security measure combined with encryption to prevent overate attacks with high delay tolerance. To overcome these issues, in this work proposed the method Multi-path Routing Protocol (MRP) based on Spectral Social Spider optimization (SSSO) feature selection. Initially, collected the sensor network dataset from standard repository using for classification to avoid the delay and improving the transmission using best features. The first step is to remove redundant data for each cluster head by applying a pre-processing step that uses data redundancy elimination techniques to reduce unbalanced data and missing values. Second-stage feature selection is based on the maximum weightage features using maximum threshold limits based on Spectral Social Spider optimization(SSSO). SSSO algorithm for analysis relay method for dependable packet delivery. After feature selection to sending and receiving the data based on the features using Improved Data Encryption Standard (I-DES) by analysing the cipher text to identify the encryption algorithm, focused cryptanalysis methods can be used. Before classification evaluating the features metrics based on Softmax Neuron Classifier (SNC) using for estimating the features weights validation. The final classification stage using Recurs

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Martian Flight: Enabling Motion Estimation of NASA's Next-Generation Mars Flying Drone by Implementing a Neuromorphic Event-Camera and Explainable Fuzzy Spiking Neural Network Model

Martian Flight: Enabling Motion Estimation of NASA's Next-Ge...

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conference on Digital Avionics systems (DASC)

作者： David AR Harbour Kelly Cohen Steven D Harbour Bradley Ratliff Alex Henderson Hallie Pennel Stephen Schlager Tarek M Taha Chris Yakopcic Vijayan K Asari Jan Boril Aqsa Sultana Shaik Nordin Abouzahra Anya Bulter Connor Prikkel NASA (Ohio Space Grant Consortium) University of Dayton ELE Undergrad Beavercreek Ohio USA Director of AI Bio Lab University of Cincinnati Mason Ohio USA Director of AI Hardware Research Parallax Advanced Research Beavercreek Ohio USA Applied Sensing Lab University of Dayton Dayton Ohio USA Parallax Research Institute Beavercreek Ohio USA Sinclair College Clayton Ohio USA University of Dayton Dayton Ohio USA Air Force Department University of Defense Brno Czechia University of Dayton Beavercreek Ohio USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The event camera is researched, developed, and designed to imitate the human eye; it is a groundbreaking vision sensor with the following advantages over a standard camera: a net rate that is much faster, a latency that is far less, a high dynamic range, and it uses far less power. These fundamental properties assist in enabling the design of third-generation algorithms in Spiking Neural Networks intended to mimic the human brain and vision processing. Moreover, these fundamental properties enable swift robotics despite the challenges of motion blur and high latency that standard cameras face. Also, these properties should enable motion estimation from the surface features on Mars, which is difficult to achieve with standard cameras. For this research, the team is using a NASA Space use-case application. For this NASA Space application, the team has chosen a challenging motion-estimation task involving a Mars-based above-ground Helicopter beyond “Ingenuity” and the planet and surface of Mars. Event-based cameras have been gaining interest within the computer vision community. They are particularly suitable for applications with challenging temporal constraints and safety requirements. Thus, Event-based sensors are an excellent match for Spiking Neural Networks (SNNs), as coupling an asynchronous sensor with neuromorphic hardware can result in real-time systems with minimal power requirements. Moreover, methods to verify and validate event-based sensing platforms for space applications are lacking. In this paper, we investigate the addition of fuzzy logic models to arrive at an explainable SNN algorithm for the NASA space use-case. Ultimately, the team aims to develop a unified model yielding reasonably accurate optical flow estimates.

关键词： Mars Neuromorphics Event detection NASA Robot vision systems Spiking neural networks Cameras sensor systems Real-time systems Standards

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Creating A Web-Based Platform For Programming Humanoid Robots

Creating A Web-Based Platform For Programming Humanoid Robot...

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International conference on High Technology for Sustainable Development (HiTech)

作者： Ventsislav Nachev Daniela Gotseva Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350358100

ISBN: (纸本)9798350358117

This research project focuses on the design and implementation of a web-based programming platform designed for Misty humanoid robots. Misty robots possess advanced capabilities, and to fully utilize their potential, a user-friendly and accessible programming environment is essential. The proposed platform aims to streamline the development process, enabling both novice and experienced developers to create custom applications and behaviors for Misty robots. This research involves the design and implementation of a secure and scalable architecture, integration of essential sensor data and hardware interfaces, and the development of a comprehensive software development kit. The platform's versatility will empower developers to explore new possibilities in the field of humanoid robotics, contributing to advancements in robotics research, education, and commercial applications.

关键词： Computer languages Education Humanoid robots Robot sensing systems Libraries Hardware Sustainable development Programming profession Software development management Programming environments

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Acoustofluidic Based Wireless Micropump for Portable Drug Delivery applications 43

Acoustofluidic Based Wireless Micropump for Portable Drug De...

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43rd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： You, Rui Fu, Xing Duan, Xuexin Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China

ISBN: (纸本)9781728111797

In this study, an acoustofluidic based wireless micropump for drug delivery was proposed and fabricated. The key actuator of this micropump is a small gigahertz piezoelectric resonator, which could induce strong fluidic streaming at low applied power. This acoustofluidic micropump has stable and accurate dosage resolution (7.0 mu L), and sufficient flow rate (1.34 mL/min). The miniaturized size and wireless controlled operation prove it as a portable drug delivery system.

关键词： Wireless communication Drugs Wireless sensor networks Electric potential Micropumps Drug delivery sensor systems

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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International conference on Intelligence in Next Generation Networks (ICIN)

作者： Sarantis Kalafatidis Dimitrios Tsiktsiris Dimitrios Trakos Antonios Lalas Konstantinos Votis Dimitrios Tzovaras Centre for Research and Technology Hellas / Information Technologies Institute Greece

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. Swarm-Catcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher's potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher's architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety.

关键词： Cloud computing Technological innovation 5G mobile communication Surveillance Prevention and mitigation Autonomous aerial vehicles Public security Real-time systems Critical infrastructure systems support

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Design of Tunnel Construction Monitoring and Surface Subsidence Warning System Based on ARIMA Model

Design of Tunnel Construction Monitoring and Surface Subside...

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Advances in Electrical Engineering and Computer applications( AEECA), 2020 IEEE International conference on

作者： Feng Tang Yi Ren Qing Zhu Jian He Guifu Xia Chongqing Engineering Quality and Safety General Office of Housing and Urban-Rural Development China Chongqing Airport Group Co. Ltd. China China Construction Third Engineering Bureau Group Co. Ltd. Wuhan China

ISBN: (纸本)9798350355260

Aiming at the demand of high precision monitoring in urban tunnel construction, this paper proposes a computer aided monitoring system based on autoregressive comprehensive moving average autoregressive model. The system incorporates advanced sensor arrays and machine learning algorithms to achieve real-time data acquisition, analysis and early warning of settlement changes during tunnel construction. The experimental results show that the maximum settlement value of the system at the monitoring point SZY-ZL4 is -3mm and the minimum settlement value is -13mm, while the maximum settlement value of the monitoring point SZY-ZL2 is 0mm and the minimum settlement value is -3mm, showing a high degree of monitoring accuracy and cost-effectiveness. By using the average autoregressive model in time series analysis and data mining technology, this study can accurately predict the settlement trend in tunnel construction and give early warning in time. The computer aided monitoring system provides a scientific and efficient safety state assessment and early warning strategy for tunnel construction safety.

关键词： Accuracy Costs Deformation Computational modeling Alarm systems Market research Safety Monitoring Surface treatment sensor arrays

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