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检索条件"任意字段=2019 International Conference on Computer Science Communication and Network Security, CSCNS 2019"

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2019 international conference on computer science communication and network security, cscns 2019

2019 International Conference on Computer Science Communicat...

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2019 international conference on computer science communication and network security, cscns 2019

The proceedings contain 109 papers. The topics discussed include: multi-channel wireless A/D acquisition system based on NRF24L01 and LABVIEW;ADE-FDTD method for study of two-dimensional electromagnetic wave propagation in lossy Lorentz medium;analysis of research on satellite network caching mechanism based on ICN;a FPGA implementation of GMM MUD algorithm for space formation aircraft communication system;research on the operational regional coverage of satellite and spacecraft tracking and controlling;joint 2-D angles and time delay estimation of coherent wideband signals;and a survey of video capsule endoscope system based on wireless energy transfer technology.

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Research on network security Situation Assessment Method 1

引用

13th international conference on computer Engineering and networks (CENet)

作者： Gao, Yuan Wen, Jin Chen, Pu Wang, Zhiqiang Beijing Elect Sci & Technol Inst Dept Elect & Commun Engn Beijing 10070 Peoples R China Beijing Elect Sci & Technol Inst Dept Cyberspace Secur Beijing 10070 Peoples R China

ISBN: (数字)9789819992478

ISBN: (纸本)9789819992492;9789819992478;9789819992461

The Internet has penetrated into various fields of human production and life. While enjoying Internet technology, people have to face various problems brought about by the Internet, among which network security issues are particularly prominent. The network security situation assessment summarizes, filters and analyzes security events generated by devices, builds suitable mathematical models based on security indicators and assesses the level of security threats to the entire network system, thereby analyzing and capturing the overall security status of the network. This paper analyzes the relevant research at home and abroad, and selects Elman neural network model, intuitionistic fuzzy set model and hidden Markov model for network security situation assessment. The result is compared with the expert assessment, and the advantages and disadvantages of the different models are analyzed in conjunction with relevant model theory. It is found that the network security situation assessment model more suitable for the current network environment is the intuitionistic fuzzy set model.

关键词： network security situation assessment Elman neural network Intuitionistic fuzzy set Hidden Marko

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An Updatable Key Management Scheme for Underwater Wireless Sensor networks 23rd

An Updatable Key Management Scheme for Underwater Wireless S...

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23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Guan, Zhiyun Wu, Junhua Li, Guangshun Wang, Tielin Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China

ISBN: (纸本)9789819708581;9789819708598

In recent years, underwater wireless sensor networks (UWSNs) have emerged as promising network model for various marine exploration, detection, and protection applications. The problems of frequent node movement and high battery energy consumption have led to the design of terrestrial wireless sensor networks (TWSNs) that are not applicable to underwater. There is an urgent need to develop new and efficient security mechanisms. On the other hand, nodes drift with the current. It is extremely challenging to balance the energy constraints while ensuring secure and private data transfer between moving nodes. In this paper, we propose an updatable key management scheme for UWSNs. A group mobility model suitable for underwater is used in a network model based on a hierarchical structure. We process the communication through a symmetric polynomial-based key pre-distribution scheme. In addition, we design a dynamic key update scheme based on reverse hash chains to extend the network security survival time. The obtained results show that our approach saves storage and communication overhead while providing highly secure connectivity and good resistance to node capture.

关键词： Underwater wireless sensor networks (UWSNs) Key management security Symmetric polynomials

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Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd international conference on Computational Intelligence, communication Technology and networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： network security

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XGBoost and Deep Learning Based-Federated Learning for DDoS Attack Detection in 5G Core network VNFs. 6

XGBoost and Deep Learning Based-Federated Learning for DDoS ...

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6th international conference on computer communication and the Internet (ICCCI)

作者： Maiga, Abdoul-Aziz Ataro, Edwin Githinji, Stanley Pan African Univ Inst Basic Sci Technol & Innovat Nairobi Kenya Tech Univ Kenya Dept Elect & Elect Engn Nairobi Kenya US Int Univ Africa USIU A Dept Comp Nairobi Kenya

ISBN: (纸本)9798350351774;9798350351767

Distributed Denial-of-Service (DDoS) attacks can employ cloud network zombies to compromise the availability of 5G network services and hinder the ability of telecommunication service providers (TSPs) to deliver promised service level agreements (SLAs), which can lead to potential losses for both TSPs and their clients. In this study, a technique for mitigating DDoS attacks in 5G core network Virtual network Functions (VNFs) was proposed. VNFs are cloud-based network functions that provide 5G services. First, eXtreme Gradient Boosting (XGBoost) is used to extract relevant features, and then the proposed hybrid deep neural network uses the XGBoost-extracted features for DDoS attack detection and mitigation. To address data privacy in heterogeneous 5G networks, where VNFs can be provided and hosted in different cloud computing systems, Federated Learning (FL) is used for the proposed model training. The anti-DDoS framework was evaluated through a simulation using the CICDDoS2019 dataset with 10 VNFs for FL. Through extensive experimentation and evaluation, the results demonstrated promising outcomes characterized by high accuracy, low false positive rate, and minimal detection time. The proposed solution offers a robust defense mechanism against evolving DDoS threats that can target 5G core networks, thereby ensuring the availability of critical network infrastructure.

关键词： Deep learning machine learning DDoS attacks 5G core network security SLA VNF

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An Enhanced Intrusion Detection Model with FeedForward Neural network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

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27th international conference on Advanced communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： network intrusion

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CGPNet: Enhancing Medical Image Classification through Channel Grouping and Partial Convolution network 22

CGPNet: Enhancing Medical Image Classification through Chann...

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IEEE 22nd international conference on Trust, security and Privacy in Computing and communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Chen, Kairen Chen, Shuhong Wang, Guojun Wang, Chenchen Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the rapid development of artificial intelligence (AI), various industries have been propelled forward. In the field of medicine, AI has proven to be invaluable in aiding doctors to gain further insights into medical conditions,medical imaging is a prime ***, the availability of large-scale datasets like ImageNet has significantly contributed to the success of deep learning models in various computer vision tasks. However, when it comes to medical image classification, the availability of large, labeled datasets is relatively limited. As a result, the number of models specifically trained for medical image classification is comparatively smaller. This paper presents a novel neural network, named Channel Grouping and Partial Convolution network (CGPNet), built upon the foundation of the InceptionNext model. Leveraging the commonly used lightweight technique, along with the introduction of grouped partial convolutions and dynamic convolutions, our proposed network exhibits promising performance. In particular, experiments were conducted on the ISIC-2019 (international Skin Imaging Collaboration) dataset, which demonstrates that our model achieves 2.61% improvement in top-1 accuracy compared to InceptionNext.

关键词： medical images image classification attention convolutional neural network

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Evaluating Machine Learning Approaches for DDoS Attack Detection Using CIC-DDoS2019 2

Evaluating Machine Learning Approaches for DDoS Attack Detec...

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2nd international conference on Advanced Computing and communication Technologies, ICACCTech 2024

作者： Kapil, Divya Mittal, Varsha Gangodkar, DurgaPrasad School of Computing CSE Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331519056

With the increasing majority of DDoS attacks, adequate detection techniques are required to protect network infrastructure. DDoS attacks are a substantial hazard to network security. The CICDDoS 2019 dataset furnishes an exhaustive collection of normal and malicious network traffic, enabling researchers to design and analyze machine learning classifiers for detecting and categorizing DDoS attacks. In this research, we present a detailed study of various machine learning techniques used for the well-known dataset, aiming to enrich the accuracy and efficiency of DDoS attack detection and categorization. We also investigate the application of machine learning techniques to detect and classify DDoS attacks, leveraging the CIC-DDoS2019 dataset for experimental validation. We assess the performance of several machine learning algorithms in determining DDoS attacks, providing a comprehensive analysis of their effectiveness. We use chi-square for feature selection and Logistic Regression, ANN and Random Forest classifier and show that Random forest got the 99.91 % accuracy. We perform comparison analysis of the performance of different models and emphasizing the significance of preprocessing steps, we seek to direct future research and growth in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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Encrypted Malicious Traffic Detection Based on Graph Convolutional network and Temporal Dissection 27

Encrypted Malicious Traffic Detection Based on Graph Convolu...

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27th international conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Yuchen Wang, Shanshan Jin Au-yeung Chen, Zhenxiang Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China Quancheng Lab Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

With the rapid development of the Internet, network security issues are receiving increasing attention and various network attack methods are emerging, among which encrypted malicious traffic, as a common attack method, has become a major challenge in the field of network security due to its extremely high concealment and impact. Traditional network security detection methods are difficult to accurately detect and defend against encrypted malicious traffic because they focus more on the detection of traffic content and fail to analyze and detect malicious traffic in terms of the structure and characteristics of the traffic data itself. Therefore, we construct network traffic graphs based on graph convolutional network to classify nodes within the network traffic data. In addition, we introduce temporal dissection to gain a more comprehensive understanding of network micro-dynamics. This study innovatively proposes a method that combines graph convolutional network and temporal dissection for the detection of encrypted malicious traffic. We conduct experiments on two real-world encrypted network traffic datasets, the results show that the accuracy, precision, recall and F1-measure of the method exceed 98% on the USTC-TFC2016 dataset, and detect more than 7900 flows per second, which is nearly 27 times faster than the same detection effect model. The performance on the DataCon2020 dataset is also better than the other six models, confirming that our method can effectively improve the effectiveness and speed of encrypted malicious traffic detection.

关键词： deep learning network traffic graph graph convolutional network temporal dissection encrypted traffic malicious traffic detection

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Information Encryption Transmission Method of Automobile communication network Based on Neural network 1

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4th international conference on Machine Learning for Cyber security, ML4CS 2022

作者： Kong, Chunhua Ma, Kai Wei, Jiatong College of Automotive Engineering Jilin Communications Polytechnic Changchun130012 China Jilin Communications Polytechnic Changchun130012 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

In order to ensure the information security of automobile communication network, an encryption transmission method of automobile communication network information based on neural network is proposed. Use neural network to generate random key to encrypt vehicle communication network information. Design encryption transmission scheme, including data encryption module, dynamic key generation module and data transmission module. The encrypted transmission scheme is used to transmit the ciphertext of the vehicle communication network information to the other end. The results show that compared with des and RSA encryption methods, the proposed method has shorter encryption time and higher key sensitivity, indicating that the proposed encryption transmission method is more efficient and secure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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