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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anitha, R. Kishore, N. Vijay Anand, M. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

In an era marked by remarkable technological advancements, the way we create and share information has undergone a profound transformation. This paradigm shift is epitomized by the NextGen Dynamic Video Generator using AI, a cutting-edge tool that seamlessly integrates artificial intelligence with content creation. What sets this tool apart is the customizability, which allows the users to fine-tune every aspect of the tutorials. Users could tune creative elements like humor, the dept. of explanation, character appearance, and voice to tailor tutorials to precise specifications. The core functionality of the tool revolves around script generation, the Cohere's language, laying the groundwork for the tutorial's content. Furthermore, seamless integration with Edge TTS ensures that the generated scripts are delivered with utmost clarity and engagement, enhancing the overall learning experience. Character animation is powered by SadTalker, adding a dynamic and captivating dimension to these tutorials. This animated character serves as a relatable guide, facilitating a deeper connection between the content and the audience. The tool also seamlessly integrates relevant and eye-catching images from Google, which are incorporated into the presentation slides. The workflow is a well-orchestrated process involving script generation, audio dialogue creation, image retrieval, video generation, and the seamless fusion of character animations and slides. The resulting video tutorials are not only comprehensive but also engaging and ready to be shared as valuable educational resources. © 2023 IEEE.

关键词： Image retrieval

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

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35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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Intelligent Routing for IoT-Driven MANETs Using Computational Intelligence

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Procedia computer science 2025年 258卷 2798-2807页

作者： Ramanpreet Kaur Kavita Taneja Harmunish Taneja Dept. of Computer Science and Applications Panjab University Chandigarh India

The interlinkage of the Internet of Things (IoT) with Mobile Ad Hoc Networks (MANETs) creates an autonomous communication ecosystem, imperative for applications ranging from disaster recovery to smart agriculture. On the other hand, this coalescing gives rise to crucial challenges, especially in energy efficiency, higher transmission rates, and network connectivity. As the devices in IoT-driven MANETs have restricted battery life, the optimum use of energy is vital for extending network lifespan. The global explosion of IoT devices demands rapid data transfer and constant connectivity. Moreover, frequent topological changes brought on by the high mobility devices in IoT-driven MANETs elevate the chances of link failure. These disruptive actions undermine network productivity resulting in reduced throughput and higher latency. Computational intelligence approaches offer credible solutions for resolving these concerns. This study presents a Unified Energy-aware Link Auto-configuration Routing Model (UELARM) for IoT-driven MANETs that favors energy-aware paths and the auto-configuration of broken links. UELARM exploits the flower pollination algorithm by incorporating three vital parameters such as energy cost, average community time, and packet loss to explore the optimal route. Additionally, for automatically configuring broken links in IoT-driven MANETs, UELARM presents a route repair approach based on mobility prediction, drawing inspiration from the termite colony optimization algorithm. The simulation results indicate that the UELARM outpaces other state-of-the-art routing algorithms regarding energy consumption, throughput, delay and network lifetime.

关键词： Computational Intelligence Flower Pollination Link Repair MANET Routing Termite Colony

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IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connectivity Hub 2

IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connec...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gundavarapu, Mallikarjuna Rao Swapna, Veerlapalii Reddy, Enugu Sriamsh Kumar, Pindi Praveen Prathapani, Abhinay Shivaji, Kothapally Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

The problem with traditional notice boards is their static nature, leading to outdated information and manual effort to update. An IoT-based smart notice board solves this by providing real-time updates but requires addressing challenges such as connectivity, power management, compatibility, user interface, security, and maintenance. Addressing these challenges ensures an effective and user-friendly smart notice board. The proposed model involves using an LED dot matrix, ESP32, Kodular app, Firebase Cloud and DS3231 module. The app sends messages to the Firebase then it passes the message to ESP32, which displays message on the LED dot matrix, We use Firebase cloud which provides infinite range. Firebase provides a NoSQL database that allows developers to store and synchronize data in real-time. It is suitable for applications that require real-time updates, such as chat apps, collaborative tools, and live dashboards. The DS3231 module shows real-time information. This study implements a feature that allows the user to retrieve the current time and temperature by pressing a button in the Kodular app. When the button is pressed, a command is sent to the ESP32, which retrieves the relevant data from the DS3231 module and sends it to display. This study creates a buffer to store messages, accessible by indexes, allowing easy retrieval. The proposed system provides a comprehensive solution for displaying messages, real-time data, and efficient message storage. QR codes are also provided to scan. © 2024 IEEE.

关键词： Database Dot Matrix DS3231 ESP32 Firebase cloud IoT Kodular app LED Module NoSQL QR code Real-Time Updates

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Comparing Developmental Approaches for Game-Based Learning in Cyber-Security Campaigns 1

Comparing Developmental Approaches for Game-Based Learning i...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sreehari, S. Gokulapriya, R. Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Digital game-based learning (DGBL) has been viewed as an effective teaching strategy that encourages students to pick up and learn a subject. This paper explores its viability to help increase the reach and efficiency of the existing cybersecurity awareness spreading campaigns that find adolescent students as their demographic. This work intends to reinforce the benefits of multimedia learning in schools and universities with the use of video games and further find the ideal type and genre of game that can be developed to spread awareness about cybersecurity to students in grades 8th to 12th (tailored towards the Indian context). Game genres were compared on the basis of having a simple gameplay loop, being easy for instructors to train themselves in, being inclusive to special needs children, being able to be published as an independent title, and having very low hardware specification requirements. Ideally, the paper proposes that this game would be a single-player experience that would follow a game-based learning approach to maximize the game's reach. Once identified, the model of the game was assessed using already existing implementations. Finally, the ideal model, a single-player visual novel is proposed. A future iteration of the paper will implement the proposed model of game design and perform an analysis of the effects the video game had on the learning experience of the students surveyed. © 2023 IEEE.

关键词： Students

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Navigating the Landscape of Intelligent Systems, Smart Technologies, and User-Centric Evolution 2

Navigating the Landscape of Intelligent Systems, Smart Techn...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Komatireddy, Sanjay Reddy Krishna Bhargavi, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350327533

Human-computer Interaction (HCI) is a dynamic field pi votal in shaping human-Technology relationships. HCI is a multidisciplinary field, essential in understanding and enhancing the interaction between humans and technology. Challenges within HCI encompass design, usability, accessibility, and emotional resonance. The aim of this research study is to highlight the multifaceted nature of HCI, offering insights into its transformative and complementing potential and its continuous influence on the digital landscape. By delving into the challenges faced and the current technologies (Multimodal Affect Recognition, Artificial Intelligence, etc.), this review serves as a foundation for further research and development in this vital domain, offering valuable insights into the dynamic HCI landscape. © 2024 IEEE.

关键词： Affect Recognition Human-computer Interaction (HCI) Multimodal Interaction Personalization Psychology Usability Challenges

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Neural Harvest : Advancements in Crop Yield Forecasting 7

Neural Harvest : Advancements in Crop Yield Forecasting

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Seelam, Srinivasa Reddy Thivari, Charitha Sai Parabattina, Bhagath Narendra Kumar, G. Sumanth, K. Lakireddy Bali Reddy College of Engineering Dept. of Computer Science And Engineering Mylavaram India

ISBN: (纸本)9798350359299

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation's economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome. © 2024 IEEE.

关键词： Feedforward neural networks

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A Review on Phishing Threats and Data Securityin Online Trading Systems using Artificial Intelligence Techniques 2

A Review on Phishing Threats and Data Securityin Online Trad...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Murugun, S. Haniah, Sheikh Koti, Shambhavi M. Sahani, Saurav Shekhar, S.N. Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru562157 India

ISBN: (纸本)9798350383867

Phishing threats pose significant risks to online trading systems, threatening the security and integrity of financial transactions. In this paper, we present a comprehensive review of phishing threats and data securityin online trading systems, focusing on the application of artificial intelligence (AI) techniques for detection andprevention. We discuss the evolving landscape of online trading, highlighting the risks associated with technological disruptions, malware, and phishing attacks. The paper explores various AI-based approaches, including machine learning and natural language processing, for detecting and mitigating phishing threats in real time. Additionally, we review recent studies on phishing classification techniques, AI-enabled detection mechanisms, cyber security awareness training, and deep learning-based approaches for phishing detection. Through a systematic review of existing literature, we identify challenges such as data availability, model interpretability, and scalability, and propose future research directions to enhance cyber security defenses against evolving phishing threats in online trading systems. Furthermore, we are Enhancing user education and awareness which plays a crucial role in effectively combating phishing attacks. Also, highlighting the need for collaboration between industry stakeholders, policymakers, and researchers to develop effective strategies for protecting online traders and investorsfrom financial fraud and identity theft. © 2024 IEEE.

关键词： Phishing

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Built-Up and Land Use Land Cover Mapping for Sustainable Planning in Himalayan Region by Using Machine Learning and Sentinel-2 Satellite Imagery 1

Built-Up and Land Use Land Cover Mapping for Sustainable Pla...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Singh, Suraj Saini, Rashmi Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350364590

The Himalaya region, known for its ecological sensitivity, also faces recurring natural hazards such as earthquakes, landslides, sinking, glacier bursts, and flash floods, which necessitate the implementation of effective planning and management strategies to ensure sustainable development. This study focuses on Joshimath, a subdivision of Chamoli district in Uttarakhand, India, situated in the Greater Himalaya region, covering an area of 4563.52 km2, Our objective is to map built-up areas using Machine learning techniques and Sentinel-2 satellite multispectral imagery to support sustainable planning initiatives. Two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), were employed in this study. RF achieved an overall accuracy of 92.62% with a kappa value of 0.911, while SVM achieved an accuracy of 90.557 with a kappa value of 0.886. Precision and recall metrics for the built-up class were calculated, with RF achieving 94% precision and 83.22% recall, and SVM achieving 93.48% precision and 82.24% recall. Additionally, the total area classified as built-up by RF was 236.96 km2, whereas SVM classified 198.67 km2. The results demonstrate the effectiveness of machine learning techniques, particularly RF and SVM, in accurately mapping built-up areas in the Himalaya region. This information is crucial for sustainable planning and management practices, enabling stakeholders to make informed decisions regarding land use and development while preserving the ecological integrity of the region. © 2024 IEEE.

关键词： Mapping

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