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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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A Survey on Cyber Attack Detection: Techniques, datasets, and Challenge

A Survey on Cyber Attack Detection: Techniques, Datasets, an...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), international conference on

作者： G Geetha Manjula Rajagopal K Purna Chand Department of Computing Technologies School of Computing (Research Scholor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computing Technologies School of Computing (Assistant Professor) SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering(Data Science) (Professor) B V Raju Institute of Technology Narsapur India

Networking technology is expanded along with cyber threats. Cyber threats are the instances which adversely influences the functional operations of the system or network. It results in various issues such as illegal access, data leakage, data stolen, DDoS attacks, etc. To overcome such issues, several intrusion detection systems are implemented over the past years. Though, these intrusion detection systems exhibit several limitations while trying to achieve excellent efficiency in attack detection. In this survey, totally 50 cyber-attack detection based research works are analysed under several perspectives. The analysis is done on several IDS techniques implemented in existing works. The reviewed learning-based approaches are explained in two major categories as ML and DL approaches. Here, the ML algorithms are further classified into three types: supervised, unsupervised and reinforcement learning approaches. Moreover, the existing works are also analysed in accordance with optimization algorithms and it is classified as metaheuristic and stochastic algorithms. In which, population-based algorithms and swarm-based algorithms are the categories classified under metaheuristic algorithms. In addition, the analyses are also performed on datasets usage and cyber-attack mitigation models. Furthermore, the analysis on existing works is done on performance analysis. And it is analysed under performance of cyber-attack detection models with specific description about maximum and minimum performance attained by recent works on cyber-attack detection models. Finally, the chronological review and challenges involved in existing cyber-attack detection models are discussed.

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A Lightening Fine-grained Access Control Scheme Supporting Full Policy Hiding in Cloud Storage 29

A Lightening Fine-grained Access Control Scheme Supporting F...

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Shi, Jiaoli Hu, Chao Yao, Shimao Mei, Zhuolin Wu, Bin Li, Hui Jiujiang Univ. School of Computer and Big Data Jiujiang China Jiangxi Univ. of Finance and Economics School of Software IoT Engineering Nanchang China Jiujiang Key Lab. of Network and Information Security Jiujiang China Jiujiang University School of Computer and Big Data Jiujiang China Peking University School of Elec. and Comp. Engineering Shenzhen China

ISBN: (纸本)9798350330717

Efficiency, security, and flexibility are difficult to balance in a full policy hiding CP-ABE scheme. This paper creatively converts the privacy matching problem of access policy and a user's private key to the privacy three-party set computation problem. Then the new privacy set computation problem is resolved by a message driven key generation method. Additionally, the ROBDD (Reduced Ordered Binary Decision Diagrams) is utilized to rich access structure expression and convert access policies into feasible path sets. Each feasible path is composed of attributes and these attributes are bound to the ciphertext using CP-ABE. Based on the innovative ideas mentioned above, we present a Lighten Fine-grained Access Control scheme supporting Full Policy Hidden (LFAC-FPH) that integrates data confidentiality protection, access policy rich expression but complete hiding, fine-grained access control, and lightweight terminal computing. Our scheme has three advantages: 1) High Efficiency. The proposed scheme can achieve fast privacy matching of access policies during user decryption. 2) Full Policy Hiding. The access policy bound by an owner on the ciphertext does not leak any information. Moreover, a user cannot know attributes they do not own. 3) High Flexibility. The decryption process does not require online from owners or KGC, and system attributes can be added at any time. © 2023 IEEE.

关键词： CP-ABE full policy hiding lighten computation Private-Preserving

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data Cataloging based SINDy Approach for Weather Forecasting

Data Cataloging based SINDy Approach for Weather Forecasting

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international conference on Advanced computing, ICAC

作者： X Regis J John Mathuram V Padiyarajan Department of Mechanical Engineering Loyola-ICAM college of Engineering and Technology Chennai India Department of Chemical Engineering Alagappa college of Technology Anna University Chennai India

In the present era, climatic change is a crucial phenomenon that poses a great threat to human and animal life. Accurately simulating and forecasting the temperature of a particular geographical location have been a great conundrum throughout history particularly in tropical regions due to the sophisticated and multi-faceted oceanic transactions. Nonetheless, a smattering number of machine learning techniques such as random forests, deep learning algorithms, neural networks, support vector machines, stacked autoencoders have been exploited to forecast the weather and other volatile field variables with encouraging prospects. These algorithms require dense training data and also consumes large CPU time to perform future weather predictions. This article proposes a data cataloging based Sparse regression of Nonlinear Dynamics (SINDy) method for weather prediction. It differs from the existing prediction techniques by formulating an elevated time series data from the actual measured dataset. By adopting this empirical prediction method, the governing relationship between the input and output data is attained in simple mathematical form rather than complex functions. The MATLAB simulations of temperature and relative humidity confirm the superiority of the proposed approach in aspects of performance criteria (Mean square error).

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A Principled Approach to Model Validation in Domain Generalization

A Principled Approach to Model Validation in Domain Generali...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Boyang Lyu Thuan Nguyen Matthias Scheutz Prakash Ishwar Shuchin Aeron Department of Electrical and Computer Engineering Tufts University Medford MA Department of Computer Science Tufts University Medford MA Department of Electrical and Computer Engineering Boston University Boston MA

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation function followed by a classifier jointly to minimize both the classification risk and the domain discrepancy. However, when it comes to model selection, most of these methods rely on traditional validation routines that select models solely based on the lowest classification risk on the validation set. In this paper, we theoretically demonstrate a trade-off between minimizing classification risk and mitigating domain discrepancy, i.e., it is impossible to achieve the minimum of these two objectives simultaneously. Motivated by this theoretical result, we propose a novel model selection method suggesting that the validation process should account for both the classification risk and the domain discrepancy. We validate the effectiveness of the proposed method by numerical results on several domain generalization datasets.

关键词： Signal processing algorithms Signal processing data models Acoustics Numerical models Classification algorithms Speech processing

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Classification of Currencies as Authentic or Counterfeit: A data-Driven Approach 3rd

Classification of Currencies as Authentic or Counterfeit: A ...

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3rd international conference on Signal and data processing, ICSDP 2023

作者： Koushik, K. Venkat Sai, G. Chaurasiya, Rahul K. Department of ECE Maulana Azad National Institute of Technology Bhopal India

ISBN: (纸本)9789819795772

The pervasive issue of counterfeit currency poses a significant threat to global economies, leading to substantial financial losses and eroding trust in financial institutions. While traditional methods of counterfeit detection rely on visual inspections and security feature verification, the advent of sophisticated counterfeiting techniques necessitates more advanced solutions. This study explores the application of machine learning (ML) algorithms to the problem of currency authentication, utilizing a dataset comprising images of banknotes. We employed a convolutional neural network (CNN) architecture, tailored for feature extraction from image data, which has demonstrated exceptional performance in pattern recognition tasks. Our proposed model introduces a novel preprocessing algorithm that enhances the distinctive characteristics of genuine and counterfeit notes, improving CNN's classification efficacy. We benchmarked the performance of our model against traditional ML classifiers, such as Support Vector Machines (SVM) and Random Forests. The analysis reveals the strengths of our model, including its robustness to variations in image quality and its ability to generalize from limited training data. However, limitations were observed in terms of computational intensity and the need for a substantial dataset to optimize model parameters. The findings underscore the potential of automated, ML-driven techniques in revolutionizing the detection of counterfeit currencies, presenting a scalable and effective solution to a complex challenge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Feature Selection

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An Efficient Technique for Improving Trust and Privacy in Blockchain as a Service (BaaS)

An Efficient Technique for Improving Trust and Privacy in Bl...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Sonali Vishwajeet Shinge Urmila Shrawankar Computer Science & Engg. Dept. G. H. Raisoni College of Engineering Nagpur (MS) India Computer Science & Engineering Deptartment G. H. Raisoni College of Engineering Nagpur (MS) India

Cloud computing is a developing technology used for providing various services such as PaaS, IaaS and SaaS for various applications based on the pay as per usage. The foundation of trust is the assurance and confidence that information, people, entities, data, or processes will deliver the desired outcome with a particular level of assurance. Many times, users even do not have knowledge about processing their personal information over the clouds. In cloud computing, centralized architecture is used for trust modeling, which causes various trust and privacy issues. The trust model is subject to a number of limitations, including single point failure, overload, vulnerability to malicious assaults, particularly harmful recommendations, and lack of accessibility and untraceability of trust proof. There are numerous initiatives underway to increase confidence between cloud users and CSPs. The privacy issues including data leakage and compromising data integrity in the cloud environment. The blockchain technology can provide the solution for the above issues. The Blockchain technology has various advantages as it is decentralized in nature, immutable ledgers, providing full transparency, Instant traceability, data encrypted end-to-end. In the proposed system hybrid consensus algorithms are created to overcome problems of the existing blockchain consensus algorithm. By using blockchain technology creates a Blockchain as a Service (BaaS) in the cloud environment to improve the trust and privacy of users.

关键词： Computer science Cloud computing Privacy data privacy Computational modeling data integrity Consensus algorithm

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Forecasting Future Sea Level Rise: A data-driven Approach using Climate Analysis

Forecasting Future Sea Level Rise: A Data-driven Approach us...

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international conference on Edge computing and Applications (ICECAA)

作者： Vinay Nagarad Dasavandi Krishnamurthy Sheshang Degadwala Dhairya Vyas Bosch Michigan United state of America Head of Computer Engineering Department Sigma University Vadodara Gujarat The Maharaja Sayajirao University of Baroda Vadodara Gujarat India

This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate.

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Resume Screening–Testing For data Stability

Resume Screening–Testing For Data Stability

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： Asim Wahedna Adit Vakil Somil Shah Vishakha V. Kelkar Ishan Shrivastava Department of Electronics and Telecommunication Dwarkadas J.Sanghvi College of Engineering Mumbai India

Resume parsing and evaluation are critical tasks for human resources teams, and with the surplus of candidates applying to jobs in today’s time, the process of resume evaluation is both manual and time consuming. The process of resume parsing has been traditionally done manually, which is time-consuming and prone to errors. However, with the advancements in Natural Language processing (NLP), automated resume parsing has become a popular research area. In addition to parsing, the advent of machine learning has allowed resume screening to be automated, through the use of key words and named entities. In this technical paper, we provide a comprehensive analysis of pre-existing research on resume parsing and evaluation frameworks, and provide a detailed overview of the different NLP techniques used for resume parsing, such as Named Entity Recognition, along with the various machine learning algorithms used for screening resumes, such as cosine similarity. We then implement our own models using resume datasets provided by a third-party firm, and analyze the stability and consistency of output data for each model using frequency distribution graphs, statistical averages, etc. in order to determine the best model for our given dataset. This paper provides a number of valuable graphs and observations that can be helpful for researchers and practitioners in the field of recruitment, machine learning.

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A Privacy-Preserving Method for Blockchain Oracle data Based on Improved Threshold Aggregated Signatures

A Privacy-Preserving Method for Blockchain Oracle Data Based...

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Information Systems and computing Technology (ISCTech), international conference on

作者： Huanliang Xiong Quan Wen Canghai Wu School of Software Jiangxi Agricultural University Nanchang China

Blockchain oracle is one of the important secure channels for obtaining off-chain data in the blockchain. Addressing the weak security issue in existing blockchain oracle solutions when acquiring and validating multi-source off-chain data, this paper proposes a data privacy protection method based on the ChainLink oracle architecture. Firstly, the privacy-sensitive data obtained by the blockchain oracle is desensitized and subjected to homomorphic processing. Subsequently, the obtained data is homomorphically validated, and the validated data is aggregated to reduce communication overhead. Finally, an improved threshold aggregate signature is employed to sign the aggregated data, significantly enhancing the security and stability of the data while preserving data privacy. Experimental results demonstrate that this method enables the oracle to meet the performance requirements of practical applications while effectively protecting the privacy of the acquired data.

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Fast Prediction for Suspect Candidates from Criminal Networks

Fast Prediction for Suspect Candidates from Criminal Network...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Jong Ho Jhee Myung Jun Kim Myunggeon Park Jeongheun Yeon Yoonshin Kwak Hyunjung Shin Center for KIURI Bio-Artificial Intelligence Ajou University School of Medicine Suwon South Korea Soda INRIA Saclay Palaiseau France Marketboro Crop. Seongnam South Korea Department of Artificial Intelligence Ajou University Suwon South Korea Department of Industrial Engineering Department of Artificial Intelligence Ajou University Suwon South Korea

Machine learning approaches have been introduced to support criminal investigations in recent years. In criminal investigations, Criminal acts may be similar, and similar incidents may occur consecutively by the same offender or by the same criminal group. Among the various machine learning algorithms, network-based algorithms will be suitable to reflect such associations. In general, however, inference by network-based algorithms is slow when the size of data is large, so it is fatal in crime scenes that require urgency. And worse, the criminal network must be able to handle complex information entangled with case-to-case, person-to-person, and case-to-person connections. In this study, we propose a fast inference algorithm for a large-scale criminal network. The network we designed has a unique structure like a sandwich panel, where one side is a network of crime cases and the other side is a network of people such as victims, criminals, witnesses, etc., and the two networks are connected by relationships between the case and its corresponding people. The experimental results on benchmark data showed that the proposed algorithm has fast inference time and competitive performance compared to the existing approaches. After performance validation, the proposed method was applied to the actual crime data provided by the Korean National Police to predict the suspect candidates for several cases.

关键词： Machine learning algorithms Law enforcement Machine learning Semisupervised learning Big data Benchmark testing Prediction algorithms

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