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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Reetu Kumari Sunil Kumar Amandeep Kamlesh Dutta Aparna Kumari Dept. of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar (Haryana) India Dept. of AI & Data Science Guru Jambheshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engineering National Institute of Technology Hamirpur Himachal Pardesh India Dept. of Computer Science & Engineering Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indepth study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions.

关键词： Industries Productivity Reviews Prevention and mitigation Intrusion detection Interconnected systems Real-time systems Internet computer security Industrial Internet of Things

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AutoFlows: Inferring Message Flows from System Communication Traces

AutoFlows: Inferring Message Flows from System Communication...

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IEEE International Symposium on Quality Electronic Design

作者： Bardia Nadimi Hao Zheng Dept. Computer Science and Engineering University of South Florida Tampa United States

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

This paper proposes a novel method for automatically inferring message flow specifications from the communication traces of a system-on-chip (SoC) design that captures messages exchanged among the components during a system execution. The inferred message flows characterize the communication and coordination of components in a system design for realizing various system functions, and they are essential for SoC validation and debugging. The proposed method relieves the burden of manual development and maintenance of such specifications on human designers. Our method also uses a new accuracy metric, acceptance ratio, to evaluate the quality of the mined specifications instead of the specification size often used in the previous work, enabling more accurate specifications to be mined. Furthermore, this paper introduces the concept of essential causalities to enhance the accuracy of the message flow mining and accelerate the mining process. The effectiveness of the proposed method is evaluated on both synthetic traces and traces generated from executing several system models in GEM5. In both cases, the proposed method achieves superior accuracies compared to a previous approach. Additionally, this paper includes some practical use cases.

关键词： Measurement Deep learning Accuracy Runtime Protocols Debugging Manuals System-on-chip Maintenance System analysis and design

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A Design Framework for Preserving Cultural Heritage with AR Technology—An Innovative Exploration on Ethnic Motifs and Its Symbolism of the Naga Tribes 9th

A Design Framework for Preserving Cultural Heritage with AR ...

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9th International Conference on Research into Design, ICoRD 2023

作者： Roy, Susmita Singh, Pankaj Pratap Padun, Abhijit Dept. of Multimedia Communication and Design CIT Kokrajhar Assam BTAD Kokrajhar India Dept. of Computer Science and Engineering CIT Kokrajhar Assam BTAD Kokrajhar India

ISBN: (纸本)9789819902927

The strong sense of immersion and interaction of advanced technology have an immense contribution towards cultural preservation in this digital era. Presently, the augmented technology is preferable and very popular from the aspect of the cultural heritage domain to revive and preserve cultural assets. This article presents a design-based framework to revive the original essence and significance of ethnic motifs and symbolism embedded in the Naga woven fabrics with Augmented Reality (AR) technology. This design framework proposes a smartphone-based application to decodes and interpret the meaning and symbolism of the motifs & patterns from Naga traditional woven fabric. This framework uses AR technology to scan and extract motifs & patterns from the traditional woven fabrics, thereby displaying the story on the visual screen. The information related to the motifs and patterns is delivered with authentic interpretations of the Naga ancient core. This application further explores the possibility of implementing it for cultural education which may create a unique identity of the hand woven fabrics of Naga tribes on digital platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Augmented reality

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Leveraging Artificial Intelligence for Colon Cancer: Trends, Challenges and Innovations

Leveraging Artificial Intelligence for Colon Cancer: Trends,...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sakshi Takkar Manik Rakhra Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research.

关键词： Deep learning Ethics Technological innovation Computational modeling Buildings Transfer learning Surgery Colorectal cancer Data models Artificial intelligence

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Unraveling Customer Sentiment in Indian Fashion E-Commerce: A BERT-Based Approach

Unraveling Customer Sentiment in Indian Fashion E-Commerce: ...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Oneeka Taneja Kuppili Raja Satya Alpana Deepa C Rathod Anaum Fathima M R Vishalakshi Prabhu H Computer Science (Data Science) R. V. College of Engineering Bangalore India Dept. of Computer Science R. V. College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

With the surge of e-commerce growth in India, it has become very necessary for brands to understand the sentiments of customers so that they can improve their offerings and remain in competition. In this study, the transformer-based model, namely BERT is used for conducting sentiment analysis of customer reviews related to famous Indian clothing brands such as Myntra, Ajio and Tata Cliq. Our research utilizes BERT contextual embeddings for the capture of opinions and emotions in customer feedback. In this paper, we will look to prove the efficiency of BERT in sentiment classification regarding Indian fashion e-commerce by analyzing a large corpus of reviews from these popular platforms, as well as compare its accuracy to DistilBERT. The study will identify how this approach extracts meaningful insights from unstructured text data that turn very useful to the brands for understanding customer satisfaction and improvement areas. Sentiment analysis of online cloth industry is one of the rarely explored field and our paper introduces this idea, specifically for the Indian demographic. Our results contribute to the developing literature of sentiment analysis in the Indian e-commerce domain and illustrate some of the many ways in which state-of-the-art natural language processing techniques are put to work in retail.

关键词： Industries Sentiment analysis Reviews Customer satisfaction Clothing Transformers Electronic commerce Data mining Surges Information technology

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity

Advancing Phishing Detection with Random Forest: A Machine L...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Sucheta Chandra Priyanka Das Ankita Mandal Sriparno Chakroborty Ishika Chowdhury Kathamrita Ghosh Dept. of Computer Application and Science Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection.

关键词： Support vector machines Uniform resource locators Accuracy Machine learning algorithms Phishing Feature extraction Ensemble learning Noise measurement Random forests Overfitting

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Adaptive Privacy and Lightweight Security in Healthcare IoT: Challenges, Solutions and Future Directions

Adaptive Privacy and Lightweight Security in Healthcare IoT:...

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Innovation in Computing and engineering (ICE), International Conference on

作者： Mukul Chauhan Tayyab Khan Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

IoT in healthcare has transformed patient care by enabling and implementing the real-time monitoring for the end patients or users. However, the development of connected devices introduces significant privacy and security challenges, especially while securing the healthcare data. In this paper, a comparative review of adaptive privacy and lightweight security mechanisms in the area of healthcare IoT is shown. A comparison of current methods based on criteria including data security, privacy preservation, scalability, resource efficiency, and real-time implementation is one of the major contributions. Furthermore, this review recognizes the research gaps in the recent researches and highlights promising techniques, including advanced encryption, federated learning, and the edge computing. By addressing these challenges, the study aims to guide the development of robust, efficient, and scalable solutions that ensure privacy and security in dynamic healthcare IoT environments.

关键词： Privacy Data privacy Technological innovation Reviews Scalability Medical services Real-time systems Encryption Security Edge computing

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An Efficient Authenticated Group Key Agreement Protocol for Dynamic UAV Fleets in Untrusted Environments 5

An Efficient Authenticated Group Key Agreement Protocol for ...

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5th IEEE International Conference on Networking and Advanced Systems, ICNAS 2021

作者： Ayad, Anfal Hammal, Youcef University of Sciences and Technology Houari Boumediene Lsi Dept.of Computer Science Bab Ezzouar Algeria

ISBN: (纸本)9781665428217

Unmanned aerial vehicle (UAV) fleets are the focus of various nations nowadays. Thus, it is expected to witness an explosion of reliability and safety critical UAV-based applications. Moreover, the recent surge of research on applied artificial intelligence and the industry advancement encourage the use of cooperative UAV fleets that offer potential collaborations even between UAVs from different organizations. The untrusted nature of the operation environments in addition to the high level of criticality of such applications require stringent secure protocols. There have been several security protocol proposals to ensure authentication, confidentiality and integrity, yet the overhead generated by these solutions and their impact on performance still severely hamper UAV endurance. In this paper, we propose to tackle the problem of secure communications within dynamic UAV fleets in open public networks using a pairing free Certificateless-Authenticated Group Key Agreement (CL-AGKA) scheme with constant rounds that meets efficiency and strong security. The formal security validation of the proposed protocol has been done by using Automated Validation of Internet Security Protocols and Applications tool (AVISPA). The aforementioned tool revealed that the proposed protocol is resilient against several attacks. More important, the computation cost has been significantly reduced thanks to the elimination of the expensive pairing operations and to the integration of the signature scheme into the key agreement to build one efficient algorithm. © 2021 IEEE.

关键词： Authentication

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A Synergistic Approach to Detect Malicious Behaviour through Correlation-Based Feature Selection with Deep Learning

A Synergistic Approach to Detect Malicious Behaviour through...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fahad Hossain Mohammad Nafizul Islam Md. Ariful Islam Mohammed Mushfiq Ali Fahim Muntasir Md. Mahiuddin Dept. of Computer Science & Engineering International Islamic University Chittagong Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Malware presents a persistent cybersecurity threat by infiltrating systems to steal sensitive data. Detecting malware traffic is challenging due to its consistent asymmetry, distinguishing it from benign traffic. This paper proposes an advanced malware detection system using deep learning and feature selection techniques. A Bidirectional-GRU approach is introduced, which improves model performance by effectively selecting and removing specific features, outperforming traditional dense-layer methods. The effectiveness of feature selection is demonstrated by enhancing the performance of traditional models such as Naive Bayes and XGBoost, surpassing Gradient Boosting. Our results indicate that at a 0.6 threshold with the selection of 21 features, Bidirectional-GRU achieved a performance of 99.98%, slightly outperforming other models. Additionally, XGBoost, GRU, and Random Forest also demonstrated strong performance in malware detection. The study highlights threshold-specific performance, showing that Bi-GRU excels at lower thresholds, while GRU performs optimally at higher thresholds, showcasing adaptability to varying configurations. Key features used in the model include stime, mm users, gtime, among others, which contributed significantly to the improved detection accuracy.

关键词： Deep learning Adaptation models Accuracy Computational modeling Feature extraction Boosting Malware Bayes methods computer security Random forests

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Angle covers: Algorithms and complexity

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Journal of Graph Algorithms and Applications 2021年第2期25卷 643-661页

作者： Evans, William Gethner, Ellen Spalding-Jamieson, Jack Wolff, Alexander Dept of Computer Science Univ of British Columbia VancouverBC Canada Dept of Computer Science and Engineering University of Colorado Denver United States Universität Würzburg Würzburg Germany

Consider a graph with a rotation system, namely, for every vertex, a circular ordering of the incident edges. Given such a graph, an angle cover maps every vertex to a pair of consecutive edges in the ordering - an angle - such that each edge participates in at least one such pair. We show that any graph of maximum degree 4 admits an angle cover, give a poly-time algorithm for deciding if a graph without a degree-3 vertex has an angle cover, and prove that, given a graph of maximum degree 5, it is NP-hard to decide whether it admits an angle cover. We also consider extensions of the angle cover problem where every vertex selects a fixed number a > 1 of angles or where an angle consists of more than two consecutive edges. We show an application of angle covers to the problem of deciding if the 2-blowup of a planar graph has isomorphic thickness 2. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Computational complexity

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