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共 7043 条记录，以下是111-120 订阅

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Image Data Hiding in Neural Compressed Latent Representations

Image Data Hiding in Neural Compressed Latent Representation...

引用

2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Huang, Chen-Hsiu Wu, Ja-Ling National Taiwan University Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350359855

We propose an end-To-end learned image data hiding framework that embeds and extracts secrets in the latent representations of.a generic neural compressor. By leveraging a perceptual loss function in conjunction with our proposed message encoder and decoder, our approach simultaneously achieves high image quality and high bit accuracy. Compared to existing techniques, our framework of.ers superior image secrecy and competitive watermarking robustness in the compressed domain while accelerating the embedding speed by over 50 times. These results demonstrate the potential of.combining data hiding techniques and neural compression and of.er new insights into developing neural compression techniques and their applications. © 2023 IEEE.

关键词： Steganography

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An URL Protection Approach for Apps on Smartphone by Built-in VPN Mechanism 9

An URL Protection Approach for Apps on Smartphone by Built-i...

引用

9th International Conference on Applied System Innovation, ICASI 2023

作者： Su, Ming-Yang Lu, Yu-Hung Lu, Hung-Yang Lian, Yu-Shih Peng, Kang-Tao Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350398380

This paper presents a security system that uses the built-in Virtual Private Networks (VPN) mechanism on Android to monitor the Uniform Resource Locators (URLs) linked to when apps are executed. Then, the proposed system sends the URLs to VirusTotal, a third-party security website for evaluation. Finally, the security score of.each URL is presented to the smartphone user, providing them the choice to decide whether to remove an app with security concerns. © 2023 IEEE.

关键词： Virtual private networks

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ElHa Net: A Pattern Recognition Neural Network 8

ElHa Net: A Pattern Recognition Neural Network

引用

8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Babiker, Elsadig Ahmed Mohamed Adlan, Hanan Hassan Ali Prince Sattam Bin Abdulaziz University College of Computer Engineering and Science Dept. of Computer Science Al-Kharj Saudi Arabia Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Dept. of Computer Science Riyadh Saudi Arabia Dept. of Computer Science Faculty of Mathematical Sciences United Kingdom

ISBN: (纸本)9798331540104

Neural Networks are the state-of.the-art models that derive intelligent systems. Generalization of.the neural network model is a judge of.how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ṫ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of.pattern recognition. ElHaNet architecture composed of.an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of.the network is found to compete favorably with state-of.the-art models in the literature. © 2024 IEEE.

关键词： Neural network models

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Selfish Users in Listen-Before-Talk Co-existence of.Cellular-Wi-Fi Networks: Counteraction Methods 21

Selfish Users in Listen-Before-Talk Co-existence of Cellular...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Saibharath, S. Mishra, Sudeepta Arora, Gazal Sharma, Anamika Hota, Chittaranjan Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

ISBN: (纸本)9783903176553

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of.cellular and Wi-Fi channels, utilization of.both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of.5G NR and Wi-Fi devices lacks the means of.communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of.coordination, network utilization becomes unfair with the existence of.selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of.selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backof. to boost their channel acquisition. We study the effect of.misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of.99.75%, we demonstrate that selfish users prof.undly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backof. window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact. © 2023 IFIP.

关键词： Wi-Fi

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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of.early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of.91.8% underscores the model's potential to accurately predict heart disease, of.ering valuable support in prevention efforts. These findings highlight the significance of.technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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HealthLink: A Cutting-Edge System for Secure Healthcare Data with IoT, Blockchain, and IPFS 3

HealthLink: A Cutting-Edge System for Secure Healthcare Data...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Vijayakumar, Kallur V. Chethan, A.S. Muneshwara, M.S. Swetha, M.S. Anand, R. Shivakumara, T. Bmsit&m Dept. of Mathematics India Bmsit&m Dept. of Computer Science & Engineering India Bmsit&m Dept. of Information Science & Engineering India Bmsit&m Dept. of Mca India

ISBN: (纸本)9798350354348

In today's rapidly evolving healthcare landscape, the integration of.cutting-edge technologies such as IoT and wearable devices has led to significant advancements, particularly in the realm of.remote patient monitoring. Despite these advancements, the traditional client/server architecture prevalent in current implementations presents substantial challenges related to security and privacy, leaving healthcare systems vulnerable to various attacks. Consequently, stringent regulatory and security measures are imperative to safeguard health data. To address these challenges and meet regulatory requirements, transitioning to a distributed architecture is essential. Blockchain technology, renowned for its distributed nature and robust security features, of.ers a promising solution to mitigate the security risks inherent in IoT-based systems. Motivated by these considerations, this study introduces HealthLink - a secure healthcare framework that seamlessly integrates IoT and Blockchain *** is designed to facilitate remote patient monitoring, particularly for chronic diseases that require continuous oversight. The framework prioritizes security, scalability, and processing efficiency. Security measures include the use of.re-encryption proxies combined with Blockchain for hash data storage, while access control is managed through smart contracts. To enhance Blockchain scalability, an of.-chain IPFS-based database is employed for data storage, and the Ethereum Blockchain-based proof.of.authority is leveraged to expedite data storage processes. As a practical demonstration, we apply the HealthLink system to diabetes management, presenting execution results through system interfaces. Experimental findings highlight substantial enhancements in the security of.healthcare systems compared to conventional methodologies, underscoring the potential of.HealthLink to revolutionize remote patient monitoring and data security in healthcare. © 2024 IEEE.

关键词： Remote patient monitoring

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Enhancing Network Intrusion Detection in a Limited Labeled Data Scenario: An Active Learning Approach

Enhancing Network Intrusion Detection in a Limited Labeled D...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md Sharafat Hossain, Md Al Amin Sikdar, Md Shahin Islam, Md Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh Selcuk University Dept. of Information Technology Engineering Konya Turkey Prime University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The Internet was originally developed to facilitate easier communication through digital devices. It has made our lives easier by providing means for communication and delivering digital resources anywhere and at any time in the world. However, the internet also exposes users to the risk of.criminals exploiting various vulnerabilities in the internet system, launching sophisticated attacks to gain valuable information and hamper users' lives. Henceforth, an effective intrusion detection system (IDS) is imperative for safeguarding the data and resources of.users in the cyber world. This research aims to enhance semi-supervised IDS models by leveraging limited labeled data and unlabeled data and exploring innovative approaches to improve adaptability and accuracy in detecting known and emerging threats, particularly in scenarios with limited labeled data available. Our study uses the UNSW-NB15 dataset, and the results show that our suggested active learning-based semi-supervised model, which was created with 2000 labeled data, is better than the supervised model that uses the same number of.labeled data. The proposed model achieved an accuracy score of.97.67% while existing studies achieved an accuracy score of.94.61%. © 2024 IEEE.

关键词： Network intrusion

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Breathing+: A Game-based System for Enhancing Respiratory Training and Motivation 6

Breathing+: A Game-based System for Enhancing Respiratory Tr...

引用

6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lin, Chang Yu Chu, Edward T.-H. National Yunlin University of Science and Technology Dept. of Computer Science and Information Engineering Yunlin Taiwan

ISBN: (纸本)9798350323535

Breathing difficulties, such as shortness of.breath or rapid breathing, can significantly harm health and diminish the quality of.life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of.training despite their advantages. Furthermore, the process of.performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of.game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of.95%. In addition, the response time to the user's breathing was around 0.23 s. © 2023 IEEE.

关键词： Motivation

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A Case Study of.Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of.DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of.existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of.implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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Self-supervised Federated Learning for Anomaly Detection

Self-supervised Federated Learning for Anomaly Detection

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Eng, Tze-Qian Pao, Hsing-Kuo Liao, Chi-Chen National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Federated learning-based strategy can confirm data privacy, as well as other related ones like split learning and differential privacy. When enjoying secured data sharing, we also worry that the data privacy preserving may harm the model's overall performance. Given the most recent progress in machine learning and AI methodology, we incorporate self-supervised learning to plug in the federated learning framework and the integrated system can guarantee the model performance and data privacy preservation simultaneously. In the integrated framework, we have different clients to keep their own data, and the data are well separated into the attribute half and the label half, for enhanced privacy, not to mention the additional privacy-preserving skill like differential privacy. Given all the aforementioned components, we can still have the privacy-preserving components equipped model performed superior to or is compatible with the performance without the privacy-preserving components. That is, one client can perform better when adding more information from other clients, without the data sharing in between, and no clients own both the attributes and the corresponding labels. The focused topic is anomaly detection and we pay attention to the imbalanced nature of.the data which shows additional challenges to the problem. Given the problem, self-supervised learning is especially useful when obtaining the label information is considered non-trivial. After all, we demonstrate the overall model effectiveness when compared to methods without any federated learning components. © 2023 IEEE.

关键词： Anomaly detection differential privacy federated learning self-supervised learning split learning

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