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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Mandeep Mishra, Prafull Aggarwal, Nitin Kumar, Vinay Sharma, Pramod Kumar Yadav, Ritesh Bennett University School of Computer Science Engineering and Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Mfps Supervisor Oracle Development Database Admin Data Science PA United States Kiet Group of Institutions Department of Csit UP Ghaziabad India Kiet Group of Institute Department of Applied Science UP Ghaziabad India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender System, are needed to solve this problem. These tools give users access to relevant information and let them customise their search results based on their liking. The Collaborative Filtering Recommendation System finds the group of active users who are your closest neighbours. This study shows many ways to use the supervised machine learning algorithm (KNN) method for the Movie recommendation System with different similarity measures, such as the cosine metric, Squared Error Similarity, Linear Correlation Coefficient, and Baseline Predictor Correlation. For the movie recommender system, these many changes to the KNN algorithm were tested on real data from the given dataset and compared based on accuracy measures such as the number of matching pairs and different types of error like mean absolute error, squared mean error, and squared root mean error. By making changes to the web browser, this recommendation system processing tool could be used as a substitute in the real world. © 2024 IEEE.

关键词： Collaborative filtering

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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling data privacy Protocols databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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Application of Machine Learning in Mental Diseases Diagnosis 25

Application of Machine Learning in Mental Diseases Diagnosis

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alsawi, Rahaf Alazaidah, Raed Samara, Ghassan Alzoui, Haneen Alidmat, Omar Elsoud, Esraa Abu Talat Khouj, Mohammed Katrawi, Anwar Alluwaici, Mo'Ath Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient's way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person's mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method. © 2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Fish Size Measurement in Aquatic Environments

A Survey on Fish Size Measurement in Aquatic Environments

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2024 IEEE Bangalore Humanitarian Technology Conference, B-HTC 2024

作者： Ankitha, K. Venugopala, P.S. Kumaki, Takeshi NMAMIT Dept. of Artificial Intelligence and Data Science Nitte India Ritsumeikan University Dept. of Electronic and Computer Engineering Kusatsu Shiga Japan

ISBN: (纸本)9798350344509

Fish size measurement in aquatic environments plays a pivotal role in understanding fish biology, ecology, and fisheries management. This survey explores various methodologies employed for fish size measurement, emphasizing their significance in aquaculture, fisheries, Ocean and ecological research. Techniques encompass both contact and non-contact methods, with advancements in imaging technologies allowing for non-invasive measurements. These approaches involve the use of cameras, image processing software, and advanced machinery to estimate fish size without causing stress or harm to the fish. While these methods offer promising avenues for precise measurements, challenges exist, including accuracy concerns and environmental limitations. Consequently, there's ongoing research to refine these techniques and ensure their applicability across different fish species and aquatic conditions. Overall, this survey highlights the evolving background of fish size measurement techniques and their importance in various domains within aquatic sciences. © 2024 IEEE.

关键词： Fish

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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TSDDISCOVER: Discovering data Dependency for Time Series data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Unsupervised Graph-Based Anomaly Detection Using Variational Autoencoder 24

Unsupervised Graph-Based Anomaly Detection Using Variational...

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24th IEEE International Conference on data Mining Workshops, ICDMW 2024

作者： Sadrolhefazi, Zeinab Sadat Zare, Hadi Asghari, Seyyed Amir Moradi, Parham Jalili, Mahdi University of Tehran Department of Data Science and Technology Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Tehran Iran RMIT University School of Engineering Melbourne Australia

ISBN: (纸本)9798331530631

Anomaly detection is recognized as a critical task in machine learning, particularly when applied to high-dimensional and graph structured data. Traditional clustering based methods for anomaly detection are often challenged by such settings. In this paper, by leveraging Variational Graph Autoencoders (VGAEs) a new method is proposed to embed graph-structured data into a low-dimensional latent space, thereby enhancing anomaly detection through unsupervised learning. Synthetic labels, generated via spectral clustering, are incorporated to enable the model to learn from the intrinsic structure of the graph while improving cluster separability in transductive learning settings. Additionally, a dynamic early stopping mechanism based on Exponential Moving Average (EMA) is introduced to optimize training duration. Extensive experimental results demonstrate that VGAEs outperform earlier works in terms of accuracy and efficiency, particularly when applied to complex, noisy, and high-dimensional datasets. By combining deep learning with clustering, the proposed approach offers potential applications in areas such as healthcare and social network analysis, where the detection of anomalies and clustering of complex data are essential. By embedding graph data into meaningful clusters, this work addresses the challenges posed by high-dimensional and sparse data, contributing to the advancement of deep clustering models. © 2024 IEEE.

关键词： Graph embeddings

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AntiMal: an Approach of Malware Detection Employing Swin Transformer 13

AntiMal: an Approach of Malware Detection Employing Swin Tra...

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13th International Conference on Communication and Network Security, ICCNS 2023

作者： Lyu, Chenxi Yao, Yinan Guo, Xiaodong Huang, Zihong Dong, Chen Zhang, Yuanyuan Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China Department of Computer Science and Engineering University of South Florida FL United States

ISBN: (纸本)9798400707964

In the present era, the menace of malicious software is growing continuously, posing a significant challenge in the realm of cybersecurity. Currently, traditional machine learning algorithms have been widely applied to the analysis of malicious software and are considered effective methods. However, these approaches often require extensive feature engineering, feature learning, and feature representation, which can be time-consuming and resource-intensive. In this paper, a method for the identification of malicious software, named "AntiMal,"is proposed. This method utilizes the Swin Transformer as the backbone network and combines it with image features for static analysis. It leverages the dataset provided by Microsoft for the Kaggle Microsoft Malware Classification Challenge. Initially, it converts the binary data of malicious code into grayscale images, treating every 8 bits as a pixel. Subsequently, it employs PyTorch to build and train the Swin Transformer, classifying the images in the validation set. The results demonstrate that this approach excels in the problem of malicious software classification, achieving an impressive accuracy of up to 95%, showcasing remarkable precision and generalization capabilities. Furthermore, it exhibits significant advantages when dealing with large datasets, requiring fewer computational resources, thus offering exceptional computational efficiency. © 2023 ACM.

关键词： Malware

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SMAKAP: Secure Mutual Authentication and Key Agreement Protocol for RFID Systems 17

SMAKAP: Secure Mutual Authentication and Key Agreement Proto...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Naziri, Shayesta Wang, Xu Yu, Guangsheng Xu, Jian Shrestha, Sudhir Liang, Christy Jie School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia School of Computer Science University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9798331509736

Radio Frequency Identification (RFID) is a crucial technology in the Internet of Things (IoT), enabling seamless wireless communication and data exchange. However, these technologies can pose significant security chal-lenges if not implemented with proper attention to security protocols-especially in communication, where pre-shared keys are not used between active tags and readers for device authentication. Some recent authentication protocols rely solely on a hash function, nonce, and single public kay agreement, which can lead to failure to implement robust security and proper authentication or ineffective for high security application environments. To effectively address these challenges this paper proposes a secure Elliptic Curve Cryptography (ECC) based lightweight mutual authentication protocol utilizing a hybrid key agreement protocol between active tag and reader for secure communication in RFID-enabled devices in the IoT environments. The informal analysis demonstrates a secure communication environment for data privacy and flexibility through effective key management. This protocol is adaptable to various applications by addressing specific requirements and limitations. © 2024 IEEE.

关键词： Authentication Protocol

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Joint Wireless and Computing Resource Management with Optimal Slice Selection in in-network-edge Metaverse System 15

Joint Wireless and Computing Resource Management with Optima...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Rashid, Sulaiman Muhammad Aliyu, Ibrahim Isah, Abubakar Lee, Jihoon Oh, Sangwon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350364637

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks. © 2024 IEEE.

关键词： Mixed-integer linear programming

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