检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1895-1908页

作者： Lu, Zhi Lu, Songfeng Tang, Xueming Wu, Junjun Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518063 China

Federated learning (FL) safeguards user privacy by uploading gradients instead of raw data. However, inference attacks can reconstruct raw data using gradients uploaded by users in FL. To mitigate this issue, researchers have combined privacy computing techniques with FL. However, these techniques may not ensure the Byzantine robustness of aggregation or the integrity of the aggregated outcomes. Most current robust privacy FL methods assess differences between gradients and benchmarks in the direction, allowing adversaries to poison the aggregation against the magnitude. Furthermore, these methods cannot ensure the integrity of the aggregation results. To overcome these challenges, this study proposes a novel algorithm, robust and verifiable privacy federated learning (RVPFL), which can more effectively eliminate the poisoning attack of the opponent by measuring the direction and magnitude of the gradient in the ciphertext state. The proposed algorithm guarantees the integrity of server aggregation results while safeguarding user privacy. In this study, comprehensive theoretical analysis and experimental validation of RVPFL are conducted to demonstrate its superiority. The proposed RVPFL algorithm solves the Byzantine robustness problem of aggregation and the integrity problem of aggregation results, which helps to research and develop more robust and effective privacy-preserving federal learning techniques. © 2020 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Exploring the landscape of network security: a comparative analysis of attack detection strategies

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3211-3228页

作者： Rajesh Kanna, P. Santhi, P. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

引用

SN Computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

来源：评论

学校读者我要写书评

暂无评论

Secure EEG: Secure Seizure Prediction using Encryption and Machine Learning 5

Secure EEG: Secure Seizure Prediction using Encryption and M...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Blessy Sumam, G.G. Gayathri, S. Gopika, S. Er&dci Institute of Technology Cdac Cyber Forensics and Information Security Thiruvananthapuram India Cdac Thiruvananthapuram India Kristu Jayanti College Department of Computer Science Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

This research work introduces a pioneering methodology for predicting seizures by leveraging Electroencephalography (EEG) data in individuals diagnosed with epilepsy. The proposed system combines machine learning algorithms with data encryption techniques to enhance both prediction accuracy and data security. For seizure prediction, LSTM was employed due to its adaptability to varying sequence lengths and capacity for real-time processing, positioning it as a potent tool that holds promise for early prediction, enabling timely intervention and potentially enhancing overall patient well-being. To ensure data privacy, Fernet encryption is employed to encrypt model parameters and test data. The encrypted model demonstrates promising results in seizure prediction accuracy during evaluation, achieving an accuracy score of 97%. Furthermore, the integration of encryption techniques ensures the confidentiality of sensitive EEG information, meeting stringent data security standards. This novel approach presents a robust framework for accurate seizure prediction while addressing critical concerns regarding data privacy in healthcare applications. The obtained results contribute valuable insights into the model's ability to accurately discern between seizure and non-seizure states, crucial for assessing its practical utility in real-world © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things

引用

IET cyber-Systems and Robotics 2023年第1期5卷 51-66页

作者： Jia-Cheng Huang Guo-Qiang Zeng Guang-Gang Geng Jian Weng Kang-Di Lu College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou UniversityWenzhouChina National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

关键词： cyber-systems deep learning deep neural network Internet of things swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Research on Anomaly Detection in Vehicular CAN Based on Bi-LSTM

引用

Journal of cyber security and Mobility 2023年第5期12卷 629-652页

作者： Kan, Xiaopeng Zhou, Zhihong Yao, Lihong Zuo, Yuxin School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Cyber Science and Technology Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China

Controller Area Network (CAN) is one of the most widely used in-vehicle networks in modern vehicles. Due to the lack of security mechanisms such as encryption and authentication, CAN is vulnerable to external hackers in the intelligent network environment. In the paper, a lightweight CAN bus anomaly detection model based on the Bi-LSTM model is proposed. The Bi-LSTM model learns ID sequence correlation features to detect anomalies. At the same time, the Attention mechanism is introduced to improve the model’s efficiency. The paper focuses on replay attacks, denial of service attacks and fuzzing attacks. The experimental results show that the anomaly detection model based on Bi-LSTM can detect three attack types quickly and accurately. © 2023 River Publishers.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

A hybrid encryption for secure data deduplication the cloud

引用

International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Detecting Deepfake Videos using Face Recognition and Neural Networks 1

Detecting Deepfake Videos using Face Recognition and Neural ...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Murugan, Muthu Aravind Mathu, T. Priya, S Jeba Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350372748

Deepfake videos created using advanced artificial intelligence techniques, pose a significant threat to digital media credibility. This project introduces a holistic strategy for identifying these videos, incorporating face recognition, feature extraction, and an innovative deep-learning model. The methodology involves pre-processing video data, extracting facial features using the face recognition library, and training a neural network model on processed face-only videos. The project filters videos based on frame count, extracts face, and creates a curated dataset for the detection model. Face-only videos are loaded and pre-processed to train a custom neural network model, which combines a pre-trained ResNext CNN with an LSTM layer for temporal feature extraction. The model is trained using Adam optimization with a cross-entropy loss function, and after completion, it has an accuracy of 95% and is capable of differentiate between fake and real videos using a confidence score. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Advancing Multiclass Emotion Recognition with CNN-RNN Architecture and Illuminating Module for Real-time Precision using Facial Expressions

Advancing Multiclass Emotion Recognition with CNN-RNN Archit...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Prince, Simrith C. Babu, Nirmal Varghese Computer Science And Engineering Karunya Institute Of Technology And Sciences Coimbatore India Data Science And Cyber Security Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Emotions, intrinsic to the human experience, serve as the catalyst for a groundbreaking technological innovation in multiclass emotion recognition through facial expressions. This study introduces a revolutionary system that harnesses advanced deep learning techniques, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to unravel the intricate interplay between emotions and cutting-edge technology. Commencing with a nuanced exploration of the evolutionary origins of emotions, the research lays a robust foundation for understanding the neural mechanisms behind emotional responses. The proposed algorithm unfolds in two pivotal stages: a meticulously designed CNN extracts intricate features from raw facial images, capturing nuanced expressions, while an adaptive RNN establishes temporal dependencies between sequential frames, allowing for a comprehensive understanding of the dynamic nature of emotional states. To address real world challenges, an innovative illuminating module is seamlessly integrated, accounting for contextual factors such as lighting conditions. Empirical evaluations on benchmark datasets underscore the system's exceptional accuracy, achieving an impressive 87.2% accuracy post hyperparameter tuning, surpassing state-of-the-art methods. This transformative method not only precisely captures static facial features but dynamically interprets evolving emotional expressions in real-time, marking a paradigm shift in the field of emotion recognition. Beyond its technical achievements, the proposed system holds profound implications across psychology, human-computer interaction, and market research, positioning itself as a pioneering force in the evolving landscape of emotion-centric technologies. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Generic,efficient and isochronous Gaussian sampling over the integers

引用

cybersecurity 2022年第3期5卷 52-73页

作者： Shuo Sun Yongbin Zhou Yunfeng Ji Rui Zhang Yang Tao Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Cyber Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Gaussian sampling over the integers is one of the fundamental building blocks of lattice-based *** the extensively used trapdoor sampling algorithms,it is ineluctable until *** the influence of numerous side-channel attacks,it is still challenging to construct a Gaussian sampler that is generic,efficient,and resistant to timing *** this paper,our contribution is ***,we propose a secure,efficient exponential Bernoulli sampling *** can be applied to Gaussian samplers based on rejection *** apply it to FALCON,a candidate of round 3 of the NIST post-quantum cryptography standardization project,and reduce its signature generation time by 13–14%.Second,we develop an isochronous Gaussian sampler based on rejection *** Algorithm can securely sample from Gaussian distributions with different standard deviations and arbitrary *** apply it to PALISADE(S&P 2018),an open-source lattice-based cryptography *** the online phase of trapdoor sampling,the running time of the G-lattice sampling algorithm is reduced by 44.12%while resisting timing ***,we improve the efficiency of the COSAC sampler(PQC 2020).The new COSAC sampler is 1.46x-1.63x faster than the original and has the lowest expected number of trials among all Gaussian samplers based on rejection *** it needs a more efficient algorithm sampling from the normal distribution to improve its performance.

关键词： Lattice-based cryptography Gaussian sampler Rejection sampling Timing attacks Trapdoor

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：