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74th International Astronautical Congress, IAC 2023

作者： Gąbka, Mikolaj Klima, Bartlomiej Damian, Wojciech Dyląg, Lidia Gajewska, Marta Daniol, Mateusz Kolodziejczyk, Agata AstroBio Student Scientific Association AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland Space Technology Center AGH University of Krakow Mickiewicza Ave. 30 30-059 Poland

Research presents a concept of a modular automated aquaponic system. The scope of the work included designing a modular, cost-effective aquaponic system for closed environments, enabling the cultivation of various types of plants in areas where soil-based growth is not feasible. The process began with the ideation phase, brainstorming, and conceptualization, during which the system requirements were established. During the development phase, the necessary environmental parameters that the system must provide for the cultivation of selected plant species were defined, and suitable containers for promoting proper root system growth and development were initiated. Simultaneously, an analysis of sensors available on the market was conducted, considering their capability to monitor environmental parameters directly affecting the growth and development of cultivated plants. Subsequently, CAD modelling was performed according to the initial requirements. Finally, a concept and automation design were developed. Currently, the project is in the prototyping phase, with an estimated completion date in the fourth quarter of 2023. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Aquaponics Automation Circular Economy IoT Space Technology

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UAV weaknesses against deauthentication based hijacking attacks

UAV weaknesses against deauthentication based hijacking atta...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Brúnó Krasnyánszki Sándor Tihamér Brassai András Németh Under Graduate Student in Computer Science Engineering Óbuda University Budapest Hungary Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary

Unmanned Aerial Vehicles (UAVs) and Unmanned Aerial Systems (UAS) are more important than ever before and we’re using it daily for agriculture, photo making, film industry, Search & Rescue, long range cable quality control, Geo Informatics, cartography, and state use (including border protection, disaster recovery and the military). Most of the UAV/UAS devices are using digital communication protocols nowadays, especially the IEEE 802.11 Wireless Local Area of Network for the cheaper and middle price UAV/UAS. Unfortunately, the protocols specified by the previously mentioned IEEE standard are vulnerable against some attacks. This research paper proposes a method that can interrupt the normal operation of the UAV/UAS that are using the IEEE 802.11 protocol and hijack the interrupted UAV/UAS permanently. For this method, deauthentication attack has been performed against the UAV/UASs. The attack vector is capable of denying the communication between the UAV and the control station. With sufficient prior knowledge it is possible to hijack the UAV that communicates on a 802.11 based protocol. Deauthentication based attacks are much more sophisticated than for example traditional electronic jamming, because the target can be precisely chosen and the attack will not interrupt other devices using the same communication method. The research has unfortunately some limitations in equipment, measurements conducted only in three different types of UAVs from three price categories, but in the future the authors would like to extend the range of UAVs. From this three UAVs two are hijackable by this method and the third should also be hijackable with some modifications. This research could be a base later for UAV/UAS counter measure systems for the safety of all of us.

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Phishing Websites Detection Using Machine Learning

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SSRN 2022年

作者： Jain, Suhani Choudhary, Naveen Jain, Kalpana MTech Student Computer Science Department College of Technology and Engineering MPUAT Udaipur India HOD of Computer Science Department College of Technology and Engineering MPUAT Udaipur India Computer Science Department College of Technology and Engineering MPUAT Udaipur India

Phishing is an online crime in which a criminal tries to persuade unsuspecting users to reveal sensitive (and valuable) personal information to the miscreant, such as usernames, passwords, financial account details, personal addresses, SSNs, and social contacts, for harmful purposes. Phishing is usually carried out by impersonating a reliable entity in Internet communication, which is accomplished through a combination of social engineering and technical trickery. Attackers regularly employ spoofing emails and deceptive websites to persuade users to provide personal information. Spoofing emails frequently pretend to be from legitimate companies and direct consumers to fake websites where they can enter important information. Phishing is one of the most common forms of online crime in today's world. Checking URLs against blacklists of known phishing websites, which are generally built based on manual verification, is a frequent countermeasure that is inefficient. As the Internet develops in size, automatic URL recognition becomes more necessary to offer end users with timely protection. This thesis explains how to use machine learning to detect dangerous phishing websites, with an emphasis on attributes retrieved just from the URL. It starts with a description of the available data and the feature engineering process, then moves on to choosing acceptable machine learning approaches. It compares algorithm performance and assesses the outcomes obtained. © 2022, The Authors. All rights reserved.

关键词： computer crime

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An AI-Centred Adaptive Cognitive Network Traffic Modeling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modeling & ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Ahmed Khan, Suyeb Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Baba Kuma Singh Ji Engineering College Amritsar India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis & transfer are crucial. For net management & traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads & precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity & take appropriate steps in response. In order to decrease network monitoring overheads & increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate & forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern & networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment & optimise the measured values, the matrices refilling & optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Advancements in Plant Disease Detection: Leveraging Convolutional Neural Networks for Bell Pepper Health Assessment

Advancements in Plant Disease Detection: Leveraging Convolut...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak Sanskaar Srivastava School of Aerospace Mechanical and Mechatronic Engineering University of Sydney Sydney New South Wales Australia Department of Engineering Sciences ABV-Indian Institute of Information Technology and Management Gwalior India Student in Computer Science ABV-Indian Institute of Information Technology and Management Gwalior India Student in Chemical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

In the field of plant-based disease diagnosis, convolutional neural networks (CNNs) have become an essential tool due to their exceptional ability to recognize sick foliage. With an emphasis on bell pepper plants specifically, this study provides a thorough examination of modern plant disease detection technologies. Several modeling techniques were investigated, with a particular emphasis on neurons and layers, by utilizing CNNs that were trained on a carefully selected dataset of photos of bell pepper plants. Since that early disease identification has a direct impact on crop quality, yield, and economic viability, its importance in agriculture cannot be emphasized. Our methodology, which makes it easier to identify sick bell pepper plants quickly, is a key component of proactive management plans that reduce agricultural losses and stop the spread of disease. This study emphasizes how critical it is to eliminate diseased plants as soon as possible in order to prevent illness from spreading across the garden and preserve crop health in general. As a result of these efforts, our CNN model was able to attain an incredible 96.5% accuracy, highlighting its potential to be a game-changing instrument in contemporary agriculture.

关键词： Training Plant diseases Accuracy Plants (biology) Neurons Crops Agriculture Convolutional neural networks Standards Testing

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Asteroid Hazard Prediction Using Machine Learning: A Comparative Analysis of Different Algorithms

Asteroid Hazard Prediction Using Machine Learning: A Compara...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Tushar Sharma Shamneesh Sharma Ajay Sharma Aman Kumar Arun Malik Abhimanyu Sharma Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited India

The prediction of asteroid risk is a crucial topic in the realm of space technology. The utilization of machine learning techniques has enabled the identification of asteroids in photographic and video data obtained from telescopes and satellites, prediction of their trajectory, simulation of their potential impact on Earth, assessment of the probability and magnitude of such an impact, and provision of support to decision-makers in the event of an impact. The implementation of these techniques can enhance our comprehension and preparedness for prospective asteroid collisions, along with our capacity to effectively rank initiatives and distribute resources targeted at mitigating the gravity of such an impact. Furthermore, the utilization of these techniques can enhance our readiness for prospective collisions with asteroids. The present study introduces a novel model for predicting asteroid hazards, which incorporates multiple variables such as asteroid dimensions, speed, path, and atmospheric conditions. The employed methodology involves the utilization of machine learning algorithms to scrutinize past records of asteroid collisions and replicate plausible impact scenarios. The training process involves utilizing 70% of the available data for training the model, while the remaining 30% is reserved for testing purposes. By implementing these methods in current research, researchers have achieved a noteworthy accuracy of 99.99% in the task of classifying asteroids. The model is subjected to comparative analysis with alternative methods, and it exhibits superior precision in comparison to the latter.

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An AI-Centred Adaptive Cognitive Network Traffic Modelling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modelling &...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Beyond Traditional Methods: Exploring Deep Learning Techniques for Accurate Traffic Projection

Beyond Traditional Methods: Exploring Deep Learning Techniqu...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Avinash Jha Shresthi Singh Shamneesh Sharma Isha Batra Arun Malik Saira Muzzaffar Yadaiah Nirsanametla School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Predicting traffic flow is a crucial component of the intelligent transportation system. This is a precise projection of the volume of traffic in a specific area at a specific future period. Researching traffic forecasts is beneficial in reducing traffic and facilitating safer, more affordable travel. Although classic models make use of shallow networks, the number of cars has grown exponentially in recent years, making these traditional machine learning techniques inapplicable to contemporary situations. We discuss some of the most recent developments in deep learning for traffic flow prediction in our article. The Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto are among the many deep learning designs. Encoder (SAE). These deep learning models gradually extract higher level information from raw input by using numerous layers. The most recent deep learning models created to address this specific issue are examined, and because transport networks are complicated, this review also informs the reader about the aspects that affect these models and which models perform best in particular situations.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Market research Convolutional neural networks Data mining Automobiles Long short term memory Intelligent transportation systems

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Need for Integration of Blockchain Technology in Supply Chain Management of Health Supplements

Need for Integration of Blockchain Technology in Supply Chai...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sahibdeep Singh Arun Malik Isha Batra Shamneesh Sharma Poongodi M Department of Computer Science Engineering Lovely Professional University Jalandhar Punjab India Delivery and Student Success UpGrad Campus UpGrad Education Private Limited India College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

By providing transparency, immutability, and security in the tracking of products from raw materials to end-users, blockchain technology has the potential to revolutionize the supply chain management of health supplements. Blockchain technology has the potential to revolutionize the supply chain management of health supplements. The decentralized structure of blockchain makes it possible for the information to be shared among stakeholders in a way that is both secure and unobtrusive. This helps to reduce the probability of counterfeit goods being sold and ensures that regulatory requirements are met. The uses of blockchain technology in the health supplement sector are investigated in this article. These applications include traceability, inventory management, and payment processing. In addition to this, it offers a discussion of the difficulties and constraints that may arise from applying blockchain technology to the management of the supply chain for nutritional supplements, as well as potential solutions for overcoming these obstacles. In the health supplement industry as a whole, the implementation of blockchain technology has the potential to promote transparency, accountability, and trust, which would ultimately be to the advantage of both customers and enterprises. The promising findings of this study done thus far highlight the necessity of future research and the use of blockchain technology in the health supplement business.

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HTML Smuggling: Attack and Mitigation

HTML Smuggling: Attack and Mitigation

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Security of Information and Networks (SIN), International Conference on

作者： Keshav Kaushik Govind Sharma Preeti Narooka Gunjan Chhabra Ankit Vishnoi School of Computer Science University of Petrolem and Energy Studies Dehradun India Student School of Computer Science University of Petrolem and Energy Studies Dehradun India Department of AIML School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of CSE Graphic Era Hill University Dehradun India Computer Science and Engineering Symbiosis Institute of Technology (SIT) Pune Symbiosis International (Deemed) University Pune India

HTML smuggling is a type of web-based attack that exploits vulnerabilities in web applications to deliver malicious content to unsuspecting users. This attack has become increasingly popular in recent years, as cybercriminals continue to look for new ways to circumvent security measures and deliver malware to users. Mitigating HTML smuggling attacks is a complex task, as it requires a deep understanding of web application security and the ability to identify and mitigate vulnerabilities in web applications. In this research paper, the authors provide a comprehensive overview of HTML smuggling attacks and their mitigation. They explore the techniques used by attackers to carry out the attack, including the use of steganography and obfuscation to hide malicious code within legitimate HTML code. They also discuss the impact of the attack on web applications and users, including the delivery of various types of malwares.

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