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International Conference on Artificial Intelligence, Blockchain, Computing and security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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Credit Card Fraud Detection Using Machine Learning Techniques 1

Credit Card Fraud Detection Using Machine Learning Technique...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Murkute, Purvi Dhule, Chetan Lipte, Praneti Agrawal, Rahul Chavhan, Nekita G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350342796

Due to the advancement of online transactions also widespread usage of credit cards for electronic payments, credit card fraud had major concerns in the financial sectors. Efficient and effective fraud detection techniques are urgently needed to protect financial institutions and consumers against evolving and sophisticated fraudulent activity. This study analyse the methods and developments used to find credit card frauds. With the objective to detect credit card fraud using an extremely unbalanced dataset, this research gives a thorough evaluation of 4 widely known machine learning algorithms: Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost. To assess how well the algorithms detect fraudulent transactions, the area under the precision-recall curve (AUPRC) and Area Under the Receiver Operating Characteristic (AUC-ROC) was estimated. © 2023 IEEE.

关键词： Area Under Precision Recall Curve (AUPRC) Area Under the Receiver Operating Characteristic (AUC-ROC) Credit Card Fraud Detection Gradient Boosting Machines Logistic Regression Machine Learning algorithms Random Forest XGBoost

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Design of Smart Irrigation System Based on MLA

Design of Smart Irrigation System Based on MLA

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Bobade, Gunjan Dhule, Chetan Khadilkar, Ritika Agrawal, Rahul Morris, Nekita Chavhan G H Rasioni College of Engineering IOT Cyber Security Department of Data Science Nagpur India

ISBN: (纸本)9798350310061

Agriculture has always been a fundamental aspectof Indian culture, making it the country's most efficient andimportant occupation. Over the years, smart and advanced techniques have been developed to optimize farming practices, and one such development is the smart irrigation system. This system has become increasingly popular among farmers as it helps them reduce water usage while maximizing profits. This has not only helped farmers increase their profits but has also made farming a more sustainable and eco-friendly occupation. The proposed Model is a AI Based Smart irrigation System, which utilizes machine learning algorithms (ML) to predict soil moisture levels based on parameters such as temperature, humidity, and soil moisture. This system is developed through cloud computing, allowing for real-time data to be extracted from (IoT) sensors. The decision tree, a supervised learning algorithm, is applied to the data to classify or predict values. The result obtained from the decision tree helps predict the appropriate amount of water needed for the crops, gives the accuracy of 90% It allowing for efficient water usage and promoting yield growth. © 2023 IEEE.

关键词： Decision trees

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CNN-BO-LSTM: an ensemble framework for prognosis of liver cancer

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1103-1109页

作者： K N, Sunil Kumar Kashyap, Pavan P. Bhyratae, Darshan A. Bhyratae, Suhas A. Kalaivani, A. Department of ISE Sri Venkateshwara College of Engineering Bangalore India Department of Electronics & amp Communication Engineering Sri Venkateshwara College of Engineering Bangalore India Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of MCA New Horizon College of Engineering Marathahalli Bangalore India Department of CSE-Cyber Security Sri Venkateshwara College of Engineering Bangalore India

computer-assisted diagnosis (CAD) is preferred for cancer identification across the globe, which relies on computerized image processing. The creation of previous CAD instruments involved a semi-automated approach that employed traditional deep learning techniques. Such techniques are not well versed with CAD instruments in terms of accuracy. Therefore, the given manuscript presents a convolutional neural network normalized architecture embedded with Bayesian optimization and long short-term memory (CNN-BO-LSTM) for the identification of liver cancer. Early pre-processing is done on the input magnetic resonance imaging (MRI) scan images to improve clarity. Next, we used dynamic binary classification to apply the accurate and region of interest (ROI) extracting approach. It is followed by automatic retrieval of CNN-based appearances from the ROI approach. For classification purposes, LSTM is used, which categorizes the images as benign or malignant. The proposed design’s testing outcomes, which combine characteristics with CNN-based ROI extraction and LSTM classification, surpassed the current state-of-the-art techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： computer aided diagnosis (CAD) Convolutional neural network (CNN) Features extraction and bayesian optimization Long short term memory (LSTM)

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Fake News Detection Using Machine Learning Algorithm 2

Fake News Detection Using Machine Learning Algorithm

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2nd IEEE International Conference on Communication, security and Artificial Intelligence, ICCSAI 2023

作者： Narkhede, Apurva Patharkar, Devyani Chavan, Nekita Agrawal, Rahul Dhule, Chetan Iot Cyber Security G H Raisoni College of Engineering Department of Data Science Nagpur India

ISBN: (纸本)9798350369960

Fake news is a big concern since it spreads widely over social media and other media channels, creating significant social and national damage with devastating consequences. In order to address this issue, substantial research how to be recognized on fake news detection has been done. The purpose of this study is to analyze the existing research on fake news detection and select the best conventional machine learning frameworks to develop an algorithm for supervised machine learning. The algorithm will be able to classify false reports as true or fraudulent using textual analysis methods such as NLP. The proposed process involves data preprocessing and vectorization, where the NLP library will be used to perform tokenization and feature extraction of text data, utilizing tools such as Count Vectorizer and Tiff Vectorizer. Further, feature selection methods will be employed to evaluate and determine the most fitting features to achieve the highest precision based on confusion matrix results. The outcome of the decision tree classifier algorithm gives the accuracy of 90%. © 2023 IEEE.

关键词： data preprocessing Fake news feature extraction Machine learning algorithm vectorization

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Customer Segmentation Based on E-Commerce using K-Mean Clustering

Customer Segmentation Based on E-Commerce using K-Mean Clust...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Bhimarapu, Harshita Bhelkar, Sakshi Chavhan, Nekita Dhule, Chetan Agrawal, Rahul G H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350306927

Due to the increasing growth of customers in market, there is fierce competition in business. Relationships between businesses and customer will be strengthened by understanding customer needs. The importance of giving users a comprehensive experience increases as a result of the digital market's rapid growth. The dataset from an ecommerce website is used in this study to identify all the factors for analysis, including date, customer id, and product;with the help of k-Mean clustering and decision tree. This research work will determine the most effective technique for Customer Segmentation. This facilitates the identification and resolution of distinct requirements and preferences among diverse customer segments. Customer Segmentation is a multifaceted strategy that can positively impact various aspects businesses, from revenue and profitability to customer satisfaction and long-term growth. © 2023 IEEE.

关键词： K-means clustering

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Perception System of Autonomous Vehicles: Sophisticated Attacks, Defense Strategies, and Future Directions 15

Perception System of Autonomous Vehicles: Sophisticated Atta...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neethu, M.R. Remyakrishnan, P. Mukesh, Sa Barath Kumar, J. Royal, Ala Manas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

The advent of autonomous vehicles marks a significant milestone in the evolution of automotive technologies. Central to the operation of autonomous vehicles is the perception system that interprets the surroundings and makes decisions. The security of perception systems against malicious attacks is a critical concern. This work thoroughly analyzes the vulnerabilities inherent in the perception systems of autonomous vehicles, examining the multifaceted threats posed by adversaries. The crucial need for ongoing research in AV security is underscored and a framework for future investigations to develop more secure and trustworthy autonomous vehicles is provided. The analysis emphasizes the importance of a proactive approach to security, advocating for integrating advanced defense mechanisms as a fundamental component of autonomous vehicle design and development and future research challenges. © 2024 IEEE.

关键词： Autonomous Vehicles Camera Lidar Perception Systems security attacks

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Encryption Using Residue Number System: Research Trends and Future Challenges 14

Encryption Using Residue Number System: Research Trends and ...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Shevchuk, Ruslan Yakymenko, Igor Kasianchuk, Mykhailo West Ukrainian National University Department of Computer Science Ternopil Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland Ukrainian National University Department of Cyber Security West Ternopil Ukraine

ISBN: (纸本)9798350350036

This paper presents a comprehensive knowledge mapping and in-depth analysis of the application of residue number system in encryption technique research to understand better global trends and directions in this field that e merged between 1967 and 2023. The analysis is based on an investigation of 1447 research articles indexed in the Web of science. The knowledge mapping based on CiteSpace 6.3.R1 presents the current research status, which contains the analysis of the collaboration network, co-citation network, references with citation bursts keyword and cluster analysis. © 2024 IEEE.

关键词： CiteSpace domaine map analysis encryption residue number system scientomet-ric database

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Polynomial sharings on two secrets: Buy one, get one free

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2024年第3期2024卷 671-706页

作者： Arnold, Paula Berndt, Sebastian Eisenbarth, Thomas Orlt, Maximilian Institute for IT security University of Lübeck Lübeck Germany Department of Electrical Engineering and Computer Science Technische Hochschule Lübeck Lübeck Germany Department of Computer Science TU Darmstadt Darmstadt Germany University of Lübeck Germany

While passive side-channel attacks and active fault attacks have been studied intensively in the last few decades, strong attackers combining these attacks have only been studied relatively recently. Due to its simplicity, most countermeasures against passive attacks are based on additive sharing. Unfortunately, extending these countermeasures against faults often leads to quite a significant performance penalty, either due to the use of expensive cryptographic operations or a large number of shares due to massive duplication. Just recently, Berndt, Eisenbarth, Gourjon, Faust, Orlt, and Seker thus proposed to use polynomial sharing against combined attackers (CRYPTO 2023). While they construct gadgets secure against combined attackers using only a linear number of shares, the overhead introduced might still be too large for practical scenarios. In this work, we show how the overhead of nearly all known constructions using polynomial sharing can be reduced by nearly half by embedding two secrets in the coefficients of one polynomial at the expense of increasing the degree of the polynomial by one. We present a very general framework that allows adapting these constructions to this new sharing scheme and prove the security of this approach against purely passive side-channel attacks, purely active fault attacks, and combined attacks. Furthermore, we present new gadgets allowing us to operate upon the different secrets in a number of useful ways. © 2024, Ruhr-University of Bochum. All rights reserved.

关键词： Polynomials

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Shared Resource Entanglement Attacks against Serverless Computing

Shared Resource Entanglement Attacks against Serverless Comp...

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2024 IEEE Conference on Communications and Network security, CNS 2024

作者： Cui, Yuwen Wei, Mingkui Lu, Zhuo Liu, Yao University of South Florida Department of Computer United States George Mason University Department of Cyber Security Engineering United States University of South Florida Department of Electrical Engineering United States

ISBN: (纸本)9798350375961

Serverless computing has emerged as a popular paradigm for building and deploying applications in the cloud. At the heart of this paradigm lies the concept of serverless functions, which have gained significant popularity due to their simplicity, scalability, and cost-efficiency. However, running serverless services on the same physical machine can introduce security threats and risks, primarily when identical serverless functions belong to different tenants. In this paper, we investigate the resource consumption of serverless functions and propose a shared resource entanglement (SRE) threat model. The SRE threat model enables adversaries to infer sensitive data from a victim's serverless function by intercepting and monitoring the host kernel interacting data patterns and the data load and write rate patterns. The experiment results demonstrate that adversaries can identify patterns in the data processing of coresident serverless functions using the SRE threat model. Also, this vulnerability could lead to the leakage of sensitive data in the Kubernetes platform. © 2024 IEEE.

关键词： Information leakage

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