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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Shabu, S.L. Jany Delphia, J. Preethi, A. Poornima, D. Karunya, K. Gayathri, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

Considering the development of cloud computing, customers' data may now be accessed remotely from any location via an online cloud server. Thanks to cloud services, users can experience the great ease of on-demand cloud apps, which don't take into account local infrastructure constraints. Since various users may be working together while accessing the data, data sharing becomes essential to achieving beneficial outcomes. In order to ensure that a user's private data cannot be read by unauthorized parties, the current PriCloud security solutions mostly concentrate on authentication. They overlook a small privacy issue, though, which occurs when a user asks the cloud server to look for ways to share data with other users. Additionally, there is PriCloud, which uses blockchain-based private payments and anonymous storage smart contracts to let customers pay their storage providers without giving up their privacy. Whether or not the challenged access request is successful in obtaining the necessary data access rights, it may nevertheless disclose the user's privacy. To address the aforementioned privacy issue for cloud storage, we propose in this work a Privacy-Preserving Authentication Protocol with Shared Authority (PPAPS). The matching method for anonymous access requests in the PP APS allows for shared access authority while taking security and privacy into account. The second stage guarantees a user can access just their own information fields by implementing attribute-based access control. Lastly, the cloud server uses proxy re-encryption to facilitate sharing of data between different users. © 2024 IEEE.

关键词： Cloud security

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Reliable and Secured Cluster based Routing in WSN via Lyrebird-Panda Optimization Algorithm 2

Reliable and Secured Cluster based Routing in WSN via Lyrebi...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepak, B.N. Biradar, Mallanagouda Joshi, Vijayalaxmi Pawar, Krunal B. Department of Information Science and Engineering SJB Institute of Technology Bangalore India Department of Computer Science and Engineering Sharnbasva University Kalaburagi India Department of Computer Science and Engineering SJB Institute of Technology Bangalore India Pune India

ISBN: (纸本)9798331504960

Routing and clustering approaches can assist address the primary issue of dependable energy-aware data transmission in wireless sensor networks (WSN). Data transmission's use in WSN situations has grown recently due to its dynamic nature. However, WSNs face a number of difficulties when sending data, such as a shorter network lifetime, lower EE, and a worse level of security. This study develops a new cluster head selection model for WSN. In this case, a new technique called Red Panda Incorporated Lyrebird Optimization (RPI-LBO) is used to elect the CHs in the best possible way. Energy, link longevity, link quality, security, and trust are all taken into account when performing the CHS. The link lifetime for 100 nodes using RPI-LBO is 42 from the 1200th to the 1800th round. However, the suggested RPI-LBO revealed a longer connection lifetime than the examined LBO, RPA, WaOA, SBOA, and COA schemes for all node counts. © 2024 IEEE.

关键词： Clustering algorithms

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Adaptive Neural Network-Based Improved Fabric Defect Detection Scheme 12th

Adaptive Neural Network-Based Improved Fabric Defect Detecti...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Prabha, P. Anantha Abirami, S. Varshini, N. Amirtha Garishma, K. Priya, M. Deva Karthick, S. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Efficient detection of defects in fabric plays a dominant role in automating quality control. It significantly enhances efficiency as well as accuracy of quality assurance in textile manufacturing. Skilled humans who physically examine defective designs in numerous sectors use traditional defect detection techniques. The main drawbacks of manual methods include deficiency of concentration, inaccuracy, weariness and increased time consumption. computer Vision (CV) and Digital Image Processing (DIP) techniques may be efficient in overcoming these shortcomings. The proposed approach involves data acquisition to record images which are subsequently pre-processed using histogram equalization to improve the defective area. A distinctive Neural Network (NN) is used for defect detection. This method improves precision and efficiency in identifying defects. The proposed method is built using Adaptive NNs (ANNs) along with Radial Basis Functions (RBFs) and is aimed at revolutionary identification of fabric flaws. The networks are built using attributes derived from images segmented using Expectation Maximization (EM) depending on target’s region attributes. As a result, the type of defects is classified based on the extracted features, thus yielding increased detection accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radial basis function networks

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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Rescue me : AI Emergency Response and Disaster Management System 2

Rescue me : AI Emergency Response and Disaster Management Sy...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Reddy, Medda Sreeketh Vamsi, Chintala Kathambari, P. Sathyabama Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350349221

Rescue Me is an innovative AI-based emergency response and disaster management system that aims to revolutionize the way emergency services handle critical situations. By utilizing advanced artificial intelligence algorithms, Rescue Me is able to efficiently analyze and prioritize emergency calls, ensuring a faster response time and ultimately saving lives. The system employs a combination of machine learning, natural language processing, and data analytics to accurately determine the urgency and severity of each emergency. Through real-time monitoring ofvarious data sources such as sensors, social media, and emergency phone calls, Rescue Me can quickly identify and predict potential risks and allocate appropriate resources accordingly. Furthermore, it enables seamless communication and coordination between emergency services, ensuring a cohesive response strategy. In addition, Rescue Me provides valuable insights for post-disaster analysis, facilitating future preparedness andmitigation efforts. By leveraging the power of AI technology, Rescue Me aims to enhance emergency services' effectiveness, improve disaster management capabilities, and ultimately contribute to a safer and moreresilient society. © 2024 IEEE.

关键词： Emergency services

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Enhancing Cloud Security in DDoS: A Holistic Approach Leveraging Machine Learning Techniques 7

Enhancing Cloud Security in DDoS: A Holistic Approach Levera...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Nair, Manju C. Ankayarkanni, B. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

Industrial application development, distribution, and deployment are impacted by the cloud computing industry's explosive growth. The majority of enterprises appear to be shifting in the last few days toward adopting cloud environments and different cloud-based services. Ensuring a secure and reliable environmental solution throughout cloud space is crucial for protecting and safeguarding the transactions conducted by enterprises. Static verification of cloud user behavior using methods like support vector machines and linear regression have been tried to discover pre-defined dangers and advance cyber- security in the market. These security solutions have limited functionality because they are static. Making decisions about access control entails executing a block or permit action. Additionally, the prior approaches have issues with data security for endpoints that are not cloud-managed. Furthermore, distributed denial-of- service (DDoS) and data privacy assaults are the most prevalent types of Cloud security attacks, with 16% and 14% of users, respectively. In order to address the aforementioned problems, this research focuses on creating a novel security solution for effective DDoS attack detection utilizing machine learning (ML) approaches. The primary goal is to influence the direction of cloud security in the future by utilizing machine learning algorithms, which can offer automated and adaptable methods for improving security in cloud environments. ML can provide solutions that go beyond focusing only on pattern recognition and analysis in sensitive data, to include full algorithms for securing enterprise data across all cloud apps. The more accurate model is trained with 99.6% accuracy by the random forest (RF) classifier, according to experimental results. The suggested machine learning algorithms are put to the test, the outcomes are confirmed, and the algorithm's effectiveness is assessed by contrasting it with other currently used techniques. © 2024 IEEE.

关键词： Cloud security

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Phishtor: Phishing Detection Using Machine Learning

Phishtor: Phishing Detection Using Machine Learning

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Sathish Kumar, D. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science Engineering and Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350309140

Nowadays, the majority of people rely heavily on the internet. Online transactions, online sign-ups, and online purchasing are used by everyone. One kind of threat to websites is phishing, which is an illegal activity that involves obtaining user data, including credit/debit card numbers, passwords, and login credentials. An efficient phishing detection method based on ensemble learning is presented in this study. The technique proposed, which combines three classifiers (K-Nearest Neighbours, KNN, Support Vector Machines, and Gradient Boosting Classifiers, GBC) to achieve the highest accuracy performance, has been shown to yield positive results overall (97.4%). The proposed technique uses feature extraction for processing the Uniform Resource Locator (URL) and predicts the website whether it's phishing or not. Therefore, it is preferable and used to employ the suggested technique to identify phishing using machine learning. © 2024 IEEE.

关键词： Phishing

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Datawindow: Python Library to Simplify the Data science Process 5

Datawindow: Python Library to Simplify the Data Science Proc...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lawrence, Daniel Anu Barathi, B.U. Sreeji, S. Sathyabama Institute of Science & Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350383522

This paper is a documentation for datawindow, a Python package which has the goal of simplifying the data processing and analysis techniques. Datawindow provides eight methods which allows users to quickly and easily handle activities in the data science process such as model building, data cleaning and summarization using user-friendly interfaces. The datawindow keeps user-friendliness and modularity as the main focus in the library's design. Datawindow provides the user-friendly experience by utilizing Tkinter for graphical user interfaces. Datawindow users Pandas for data processing and Tkinter for the interfaces to help provide clarity to the users. This ensures that the library satisfies user needs and aims to provide the user with a streamlined data science experience. Datawindow seeks to refocus the focus of the data analysis and data science process from coding and programming to allowing data workers to focus more on the creative processes and analytical thinking part of the analysis using interfaces. © 2024 IEEE.

关键词： cluster analysis data analysis workflow data cleaning data processing data summarization deep learning exploratory data analysis machine learning modular design numpy pandas python library tkinter user interface

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COCAME: A Computational Offloading in Cloud Assisted Mobile Environments Structure to Enhance Performance and Energy 14

COCAME: A Computational Offloading in Cloud Assisted Mobile ...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Aishwarya, R. Mathivanan, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344837

The widespread popularity and affordability of smart mobile devices provide users with a fantastic interface. However, inherent hardware limitations, encompassing storage, CPU, and power consumption, pose challenges. The utilization of various services significantly drains the limited battery capacity of these devices. Complex mobile applications, constrained by restricted resources, suffer from diminished performance and power capacity, making battery enhancement a formidable challenge. Leveraging cutting-edge technologies can potentially boost device sales by 5% annually. It is crucial for infrastructure and application developers to address energy and performance challenges. In the realm of Mobile Cloud Computing, computational offloading plays a pivotal role in enabling high-computational apps on smartphones, potentially improving power efficiency and performance. Despite numerous advancements in computational offloading structures and methodologies, existing frameworks struggle to accurately assess the added burden of relocating application components during runtime. To enhance both performance and energy efficiency, the proposed COCAME approach focuses on strategically transferring minimal instances of computationally intensive application components at runtime. © 2024 IEEE.

关键词： Mobile cloud computing

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Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第3期19卷 29-59页

作者： Simon Elias Bibri John Krogstie Amin Kaboli Alexandre Alahi School of Architecture Civil and Environmental Engineering(ENAC)Civil Engineering Institute(IIC)Visual Intelligence for Transportation(VITA)Swiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway School of Engineering Institute of Mechanical EngineeringSwiss Federal Institute of Technology Lausanne(EPFL)LausanneSwitzerland

The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart *** strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental *** aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental ***,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying *** bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental *** ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis *** the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologi

关键词： Smarter eco-cities Smart eco-cities Smart cities Artificial intelligence Artificial intelligence of things Machine learning Environmental sustainability Climate change

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