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检索条件"任意字段=2023 International Conference on Computer Network Security and Software Engineering, CNSSE 2023"

共 6802 条记录，以下是241-250 订阅

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MalWuKong: Towards Fast, Accurate, and Multilingual Detection of Malicious Code Poisoning in OSS Supply Chains 38

MalWuKong: Towards Fast, Accurate, and Multilingual Detectio...

引用

38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Li, Ningke Wang, Shenao Feng, Mingxi Wang, Kailong Wang, Meizhen Wang, Haoyu Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350329964

In the face of increased threats within software registries and management systems, we address the critical need for effective malicious code detection. In this paper, we propose an innovative approach that integrates source code slicing, inter-procedural analysis, and cross-file inter-procedural analysis, thereby enhancing the detection precision and reducing false positives. This approach has been encapsulated within a multi-analysis-based framework for automatic detection of malicious code in real-world software packages. In its application to major third-party software registries like PyPI and NPM, our framework has proven effective, identifying 130 malicious packages from a total of 169,640 monitored over a continuous period of five weeks. This work advances the current state-of-the-art solution to malicious code detection, demonstrating significant practical impact in strengthening the software supply chain defense.

关键词： Malware Detection software Supply Chain security security Tools

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Pressure difference of irrigation pipe network system based on binary tree

Pressure difference of irrigation pipe network system based ...

引用

2023 international conference on computer Application and Information security, ICCAIS 2023

作者： Liu, Ying Zhejiang Tongji Vocational College of Science and Technology Hangzhou311231 China

ISBN: (纸本)9781510675032

[Methods]The binary tree was used to Preorder traversal the relative pressure difference of each node in the irrigation community pipeline network, solving the optimal distance problem between two points with real weights.[Results] Through the analysis of practical application case, using the binary tree first sequence traversal method respectively for different specifications of branch pipe, wool, water flow solution irrigation village pipe network system pressure difference, the results and the calculation results in other literature, the optimal scheme is consistent with the actual situation, shows that the binary tree traversal method in solving the optimal distance of real weight is feasible.[Conclusion] The binary tree traversal method has the advantages of simple principle, convenient storage and clear structure, which can be widely applied to the problem of optimal tree structure with real weight. © 2024 SPIE.

关键词： Binary trees

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5G network security: Unraveling Vulnerabilities and Innovating Defense Mechanisms 15th

5G Network Security: Unraveling Vulnerabilities and Innovati...

引用

15th international conference on e Infrastructure and e Services for Developing Countries

作者： Saeed, Mamoon M. Ali, Elmustafa Sayed Khalifa, Othman O. Mokhtar, Rania A. Univ Modern Sci UMS Dept Commun & Elect Engn Fac Engn Sanaa Yemen Red Sea Univ RSU Dept Elect & Elect Engn Port Sudan Sudan Int Islamic Univ Malaysia Dept Elect & Comp Engn Kuala Lumpur Malaysia Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 Taif 21944 Saudi Arabia

ISBN: (纸本)9783031815690;9783031815706

Rapid developments in cellular communications are accompanied by rising privacy and security worries. Many people refrain from participating in activities like social networking, shopping, transactions, and conducting a lot of business because network security and user privacy are major concerns in our daily lives. However, there is now a greater need for a private, highly secure business. This was accompanied by an increase in the requirements for it due to the growing hazards and programmers in our daily activities. The Fifth Generations (5G) groups are rapidly developing, as evidenced by the fact that the number of supporters is increasing by several times per second around the world in light of the ongoing revelations. According to statistics, 80% of people worldwide claim to use 5G mobile phones, and the percentage has been steadily rising for a very long time. Ahigh level of security is also necessary because the 5G network serves as the basis for the 5G network. From there, this article gives an overview of some of the different 5G network vulnerabilities that can occur. Additionally, certain recent advancements in 5G security have revealed several flaws that still exist, allowing experts to concentrate on and fix these flaws.

关键词： security 5g vulnerabilities evolved packet core network access security

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Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in software-Defined networks (SDN)

Big Data Framework to Detect and Mitigate Distributed Denial...

引用

2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hamedani, Amirreza Fazely Aziz, Muzzamil Wieder, Philipp Yahyapour, Ramin Göettingen University Department of Computer Science Göttingen Germany EScience Group Göttingen Germany

ISBN: (纸本)9798350322972

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate *** paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data *** main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard. © 2023 IEEE.

关键词： Machine learning

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Research on computer network Information security Protection System Based on Artificial Intelligence 2

Research on Computer Network Information Security Protection...

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2nd international conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Bao, Jian Yun, Kai Li, Haosheng Zhang, Qingwang Yang, Hao State Grid Xinjiang Information and Telecommunication Company Urumqi Xinjiang830063 China

ISBN: (纸本)9798350323818

With the rapid development of high technology and economy, our country has entered a new era of artificial intelligence in the 21st century. Home appliances of artificial intelligence, auxiliary software of artificial intelligence, learning tools of artificial intelligence, etc., have gradually become an inseparable part of national life, so the flourishing development of information technology and network makes people's life more convenient and efficient, but at the same time, because we are in the new era of artificial intelligence, these applications in different fields not only help to improve the efficiency of work in various fields, but also give The computer network information security protection technology brings challenges, so information technology and network also has two sides, which computer network information security problems to the country and people bring certain impact and trouble, so this paper discusses and discusses the computer network information security problems in the era of artificial intelligence, and puts forward the corresponding protection countermeasures. © 2023 IEEE.

关键词： Domestic appliances

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Bridge crack segmentation algorithm based on the novel full convolutional neural network

Bridge crack segmentation algorithm based on the novel full ...

引用

2023 international conference on computer Application and Information security, ICCAIS 2023

作者： Bai, Yun Wang, Haobo Xing, Feng Lu, En Liu, Wencong Engineering Training Center Xi'an University of Science & Technology Shaanxi Xi'an China School of Electric and Control Engineering Xi'an University of Science & Technology Shaanxi Xi'an China

ISBN: (纸本)9781510675032

Considering the impact of background noise, the traditional bridge crack detection method cannot completely extract the crack features and effectively fuse them, resulting in low crack detection accuracy, false and missed detection. This paper presents a bridge crack detection algorithm based on improved U-Net (I-U-Net) network, specifically designed to address the mentioned challenges. Firstly, the residuals module with bottleneck structure (Res-Bot) is combined with the U-Net model to obtain complete crack information. Secondly, in order to eliminate the interference of background noise, optimized convolutional block attention module (O-CBAM) is introduced, adding dimensionality reduction convolutional layer and dilated convolutional layer, which effectively reduces the number of network parameters while simultaneously improving the feature extraction capabilities of the network. Thirdly, based on the idea of multi-size fusion, different sizes of convolution kernels are selected according to different levels to extract crack information, and the encoder-decoder-multi-size fusion (EDMSF) module is constructed. Finally, the effectiveness of the I-U-Net network is demonstrated through the public bridge crack dataset and the self-made dataset. © 2024 SPIE.

关键词： Crack detection

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Research on security Protection System of network Digital Governance Database under Artificial Intelligence Technology 6

Research on Security Protection System of Network Digital Go...

引用

2023 IEEE 6th international conference on Information Systems and computer Aided Education, ICISCAE 2023

作者： Yang, Yinghui Wei, Jingxuan Bank of Beijing Tsinghua University Beijing China Technology Co. Ltd Beijing China

ISBN: (纸本)9798350313444

This paper develops an active network security protection system based on mobile devices on Android system. The hardware of the system consists of database encryption module, certificate management module, database encryption technology architecture, key management module and so on. In the aspect of software, the database system is used and the secure encryption algorithm is designed. Thus, the security and reliability of data are guaranteed. The system realizes the fast, convenient and low-cost processing of business data flow. It monitors and prevents the data flow, and realizes the real-time monitoring and prevention of the data flow. Experiments show that the cryptosystem based on key store can effectively improve the capacity and security of cryptosystem. © 2023 IEEE.

关键词： Artificial intelligence database network digital governance safety protection system

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Research on the construction method of encrypted malicious traffic detection system based on machine learning

Research on the construction method of encrypted malicious t...

引用

2022 international conference on computer network security and software engineering, cnsse 2022

作者： Cheng, Qihao Zhang, Yangsen He, Ziyuan Chen, Xiang Institute of Intelligent Information Processing Beijing Information Science and Technology University Beijing100101 China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing100044 China

ISBN: (纸本)9781510655980

With the development of social science and technology, the communication process is often accompanied by a variety of encrypted malicious traffic intrusion into the network system. Therefore, it is necessary to build a malicious traffic detection system to solve this problem. This paper proposes a construction scheme of malicious traffic detection system based on machine learning. The malicious traffic data selects the public cic-ids-2017 dataset, and carries out feature engineering processing on the traffic data, which is transformed into a data feature set that is easy to be trained by machine learning model. The performance of various machine learning models are tested and compared. Finally, the optimal random forest model is selected as the malicious traffic detection model. Experiment shows that the model can effectively detect encrypted malicious traffic. © 2022 SPIE.

关键词： Machine learning

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A Study of Data Augmentation for Imbalance Problem in network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

引用

3rd international conference on Blockchain Technology and Information security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Transfer Learning Innovations in network security and Threat Intelligence 3

Transfer Learning Innovations in Network Security and Threat...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mishra, Aishwarya Kumar, Sandeep Pipalia, Dhaval Vaish, Rohit Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Electrical Engineering Rk University India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

In Transfer Learning Innovations in network security and Threat Intelligence,' we introduce a holistic approach that harnesses transfer learning to fortify network security and elevate threat intelligence. This methodology revolves around three core algorithms, each addressing a specific facet of cybersecurity enhancement. Initially, a deep neural network undergoes pre-training on a diverse source domain dataset rich in cybersecurity information. This process aims to capture generalized features and knowledge pertaining to a broad spectrum of cyber threats and vulnerabilities. The outline these techniques, such as Maximum Mean Discrepancy (MMD) and adversarial training, both conceptually and mathematically. These three algorithms amalgamate to create a robust transfer learning framework, purpose-built for network security and threat intelligence. This comprehensive approach empowers security practitioners to dynamically adapt and reinforce their defense mechanisms. © 2023 IEEE.

关键词： network security

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