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Flood and Loot Attack in Distributed Routing Protocols in PCNs 17

Flood and Loot Attack in Distributed Routing Protocols in PC...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Neeraj Kapoor, Kalpesh Indian Institute of Technology Dept. of Computer Science & Engg. Guwahati India Indian Institute of Technology Dept. of Mathematics Guwahati India

ISBN: (纸本)9798331531195

Payment Channel Networks (PCNs) can significantly enhance the scalability of blockchain transactions without requiring major modifications to the underlying distributed ledger protocol. However, to enable efficient and secure payment routing over multiple channels, PCNs necessitate additional components: an onion routing protocol for anonymity and a locking mechanism to prevent race conditions. Unfortunately, these mechanisms can be exploited by malicious actors to launch flood and loot attacks. We investigate the impact of flood and loot attacks on networks that employ local knowledge-based routing algorithms. In these scenarios, malicious nodes can manipulate hash-locks to unfairly capitalize on the benefits of participating nodes. We specifically examine the effects of these attacks on three popular routing algorithms: Swift, Speedy Murmurs, and Silent Whispers. Our analysis employs metrics like attack gain, attack cost, and transaction ratio to assess the impact of the attacks. Our simulation-based results demonstrate that, even with a relatively small fraction of malicious nodes (between 0.66% and 3.3%), the average attack transaction ratio across all evaluated routing algorithms is a concerning 16.23%. © 2025 IEEE.

关键词： Distributed ledger

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Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency 11

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Olekar, Vijay Patil, Yash Pawar, Vishvam Pitale, Shubham Jaiswal, V.R. Naranje, Vishal Pune Institute of Computer Technology Pune Dept. of Information Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications. © 2024 IEEE.

关键词： Cyber Physical System

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Bibliometric Analysis of IoT Application Research: Unveiling Trends and Future Directions for Terahertz Communication in Wireless Systems 4

Bibliometric Analysis of IoT Application Research: Unveiling...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hajoary, Deepak Basumatary, Rinku Narzary, Raju Dept. of Management Studies Assam Kokrajhar India Dept. of Computer Science and Technology Assam Kokrajhar India

ISBN: (纸本)9798350387490

This abstract presents a bibliometric analysis of IoT application research, focusing on unveiling trends and future directions in the field of terahertz communication for wireless systems. The study employs bibliometric techniques to analyze and assess the existing literature on IoT applications, with a specific emphasis on terahertz communication. By examining the publication trends, citation patterns, and collaboration networks, this research provides valuable insights into the current state of terahertz communication in the context of IoT. The findings highlight the emerging applications, key research areas, and potential challenges in terahertz communication for wireless systems. This bibliometric analysis serves as a roadmap for researchers and industry professionals interested in further advancing the field, shaping the future of IoT applications with terahertz communication technologles. © 2024 IEEE.

关键词： 5G mobile communication systems

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Do large language models and humans have similar behaviors in causal inference with script knowledge? 13

Do large language models and humans have similar behaviors i...

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13th Joint Conference on Lexical and Computational Semantics, StarSEM 2024

作者： Hong, Xudong Ryzhova, Margarita Biondi, Daniel Adrian Demberg, Vera Dept. of Computer Science Saarland University Germany Dept. of Language Science and Technology Saarland University Germany

ISBN: (纸本)9798891761063

Recently, large pre-trained language models (LLMs) have demonstrated superior language understanding abilities, including zero-shot causal reasoning. However, it is unclear to what extent their capabilities are similar to human ones. We here study the processing of an event B in a script-based story, which causally depends on a previous event A. In our manipulation, event A is stated, negated, or omitted in an earlier section of the text. We first conducted a self-paced reading experiment, which showed that humans exhibit significantly longer reading times when causal conflicts exist (¬A → B) than under logical conditions (A → B). However, reading times remain similar when cause A is not explicitly mentioned, indicating that humans can easily infer event B from their script knowledge. We then tested a variety of LLMs on the same data to check to what extent the models replicate human behavior. Our experiments show that 1) only recent LLMs, like GPT-3 or Vicuna, correlate with human behavior in the ¬A → B condition. 2) Despite this correlation, all models still fail to predict that nil → B is less surprising than ¬A → B, indicating that LLMs still have difficulties integrating script knowledge. Code and data are available at https://***/tony-hong/causal-script. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Design of a Hybrid Framework for Detecting Attacks in Fog-based IoT architecture using Deep Learning 15

Design of a Hybrid Framework for Detecting Attacks in Fog-ba...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pokale, Navnath B. Sharma, Pooja Mane, Deepak T. Dept. of Computer Engineering School of Engineering and Technology D. Y. Patil University Ambi Pune410507 India Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331300579

IoT-Fog computing offers a broad variety of services for IoT-based end-systems. End IoT devices communicate with cloud nodes and fog nodes to administer client tasks. During the data collection process between the fog and cloud layers, IoT endpoints are more likely to be compromised by critical attacks such as DDoS and other security threats. It is necessary to detect these network vulnerabilities early on. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. Due to the limited processing power and less storage, Deep Learning algorithms cannot be directly implemented on IoT devices. In this article, we present a three stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog architecture. Enhanced Z-score normalization based data preparation will be carried out in the beginning. On the basis of preprocessed data, features such as IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as an input to hybrid classifiers called DBN and optimized DeepMaxout in the third stage to classify the assaults based on the input dataset. A hybrid optimization model known as the BMJFO (Blue Monkey Jelly Fish Optimization) algorithm is presented for the best DeepMaxout training. The suggested model produced increased accuracy, precision, sensitivity, and specificity. The suggested model produced superior accuracy, precision, sensitivity, and specificity of 95.80%, 94.94%, 96.38%, and 97.94%, respectively. © Grenze Scientific Society, 2024.

关键词： Fog computing

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Counting Malicious Host Behind NAT Using Ensemble Machine Learning Approach 6

Counting Malicious Host Behind NAT Using Ensemble Machine Le...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Shukla, Sanjeev Misra, Manoj Varshney, Gaurav Indian Institute of Technology Roorkee Dept. of Computer Science and Engg Uttrakhand Roorkee India Indian Institute of Technology Jammu Dept. of Computer Science and Engg J&K Jammu India

ISBN: (纸本)9798350374162

Network Address Translation (NAT) is widely used to map private IP addresses to public ones, offering anonymity that not only benefits legitimate users by protecting their internal IP addresses but also poses a challenge by enabling malicious users to obscure their identities during cyber-attacks. Identifying the actual host behind NAT involved in such activities is critical for effective forensic analysis. In this study, we introduce a novel, passive, and non-intrusive technique that leverages ensemble machine learning (ML) to pinpoint and characterize malicious hosts behind NAT. By analyzing network traffic patterns at the flow level, our method successfully penetrates the veil of anonymity NAT provides. Designed to be operating system-agnostic and independent of IP addresses, our approach addresses privacy concerns while maintaining high efficacy. Demonstrating a remarkable accuracy rate of 99.12%, our technique not only outperforms existing ML and non-ML-based methods but also sets a new benchmark in detecting malicious entities operating behind NAT. © 2024 IEEE.

关键词： Anonymity

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AI-Powered DeepFake Defense 3

AI-Powered DeepFake Defense

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chaitra, M. Kushal, B. Likhitha, H. Priyanka, H.R. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Deepfake technology, known for its ability to produce convincingly face-swapped videos, presents significant risks to privacy, security, and public trust. Misuse of this technology for spreading misinformation, defamation, and influencing politics is on the rise, creating a pressing need for more effective detection mechanisms. As deepfake generation methods continue to advance, conventional detection techniques are increasingly unable to keep up, often falling short in accuracy and adaptability. This project aims to create an AI-driven detection system specialized in identifying face-swap deepfakes. The system focuses on subtle irregularities in facial details, movements, and the synchronization between audio and visuals. Employing neural network models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Capsule Networks, it pinpoints anomalies like unnatural expressions, atypical skin textures, and lighting inconsistencies. The system achieves high reliability with metrics including accuracy (96%), precision (95%), recall (94%), and F1 score (95%), showing marked improvement compared to traditional techniques. It outperforms existing methods such as XceptionNet (92%) on the Celeb-DF dataset, underscoring its superior generalizability and robustness. This scalable solution has broad applications in media, law enforcement, and cybersecurity, reinforcing efforts to combat deepfake threats and fostering improved trust and security across various sectors. © 2025 IEEE.

关键词： Long short-term memory

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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Smart Oral Health Monitor 3

Smart Oral Health Monitor

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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