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Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

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SOFT COMPUTING 2023年第10期27卷 6835-6844页

作者： Wang Shaorong Long Guiling Yichun Univ Sch Math & Comp Sci Yichun 336000 Jiangxi Peoples R China

The continuous progress of society has created conditions for the widespread use of information technology. People rely more and more on information technology and the Internet. People can use the Internet to retrieve relevant information to meet their work needs, but the widespread use of the Internet is also accompanied by network security issues. The existence of network security problems will affect people's work to varying degrees. Network security management departments use intrusion detection technology and set firewalls to improve the security of people's personal information on the Internet and prevent viruses and some criminals from stealing people's important information through the Internet, causing adverse effects on the Internet environment. In the daily management of the school, the extensive use of the campus network can not only facilitate students' daily study and life, but also improve the efficiency of school management. Ensuring the network security of campus network is an important work to ensure the campus management and students' study and life. The four parts of network security work include setting firewall, encrypting cloud data, using intrusion detection technology, and recovering data. intrusion detection technology is extremely important for the development of network security work, which can help people actively find vulnerabilities in networks and systems, identify possible behaviors that may invade systems and networks, and give early warning or automatically close intrusion channels. There are various types of cloud data, so cloud database plays an extremely important role in the development of various fields. This paper uses intrusion detection algorithms to design the campus network security protection system, which provides security for the use of the campus network.

关键词： Cloud data intrusion detection algorithm Campus network security Protection system

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E-commerce consumer privacy protection and immersive business experience simulation based on intrusion detection algorithms

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ENTERTAINMENT COMPUTING 2024年 51卷

作者： Zhuang, Sumei Zhanjiang Univ Sci & Technol Sch Management Zhanjiang 524000 Guangdong Peoples R China

In the process of e -commerce shopping, immersive service experiences and entertainment shopping models are increasingly popular among consumers. This article analyzes the privacy protection and immersive business experience simulation of e -commerce consumers based on intrusion detection *** studying intrusion detection algorithms, this article delves into personal privacy protection models related to public network ecommerce consumption. This article first explores the significance and current status of intrusion detection algorithms, as well as the research status of deep learning in this field, and conducts comparative experiments on improved intrusion detection algorithms. Then, after analyzing the requirements of the evaluation support system for privacy protection algorithms, the overall framework of the personal privacy protection system was designed and studied. intrusion detection algorithm identifies possible privacy threats by monitoring and analyzing user behavior data, and then proposes an immersive business experience simulation framework, which includes virtual reality technology and personalized recommendation system, enabling users to immerse themselves in the virtual environment of e -commerce platform and enjoy personalized shopping experience. Interactive experiences can provide e -commerce consumers with more shopping pleasure, and at the same time, businesses need to ensure that consumer privacy is effectively protected during the consumption process. In the end, a new personal privacy protection model was successfully constructed, which verified the great advantages of the algorithm in terms of running time, and concluded that using intrusion detection algorithms can greatly protect the personal privacy of e -commerce consumers using public networks.

关键词： intrusion detection algorithm Electronic commerce Consumer privacy protection Immersive Business experience simulation

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A Survey of Quantitative Comparative Research on intrusion detection algorithms in Wireless Sensor Networks 20

A Survey of Quantitative Comparative Research on Intrusion D...

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IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

作者： Yang, Yahong Wang, Pan Wang, ZiXuan Li, Zeyi Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing Jiangsu Peoples R China

ISBN: (数字)9781665462976

ISBN: (纸本)9781665462976

Wireless Sensor Network (WSN) is vulnerable to malicious attacks by third parties due to limited communication capabilities and unstable channels. Moreover, WSN nodes have limited energy, small storage space, and poor computing power, which restrict traditional intrusion detection methods. In recent years, people have tried to apply many traditional intrusion detection algorithms to WSN. Meanwhile, experimental results compared several intrusion detection algorithms. However, the experimental comparison of these intrusion detection algorithms is not comprehensive enough. Deep Forest and ELM algorithms are not considered. Therefore, this paper evaluates eight intrusion detection algorithms on four datasets and details the selection of algorithms, datasets, and evaluation indicators. The final experimental results show that the comprehensive performance of Deep Forest on WSN is better. The intrusion detection algorithm research in this paper provides more ideas.

关键词： Wireless Sensor Network intrusion detection algorithm Quantitative Comparative Study Deep Forest ELM

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Semi-Supervised intrusion detection Method based on Adversarial Autocoder 19

Semi-Supervised Intrusion Detection Method based on Adversar...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Liu, Xiaobing Luo, Entao Yang, Jie Yin, Xiangdong Huang, Tangsen Hu, Lehua Hunan Univ Sci & Engn Sch Informat Engn Yongzhou Peoples R China

ISBN: (纸本)9781665421744

In terms of the problems of low-grade accuracy and high-grade inaccurate alarm rate for intrusion detection algorithm on the basis of unsupervised learning, and high cost of training samples required by supervised algorithm, an intrusion detection algorithm ground on adversarial autocoder is proposed. This is a semi-supervised learning algorithm, which only needs a handful of labeled data in the training data set for training, and supports unlabeled data in the training data set, so as to improve the performance. Firstly, the autocoder reduces the dimensionality of the import data by withdrawing essential features as reference of latent variables;secondly, it uses the generative adversarial network to make the latent variables of the autocoder follow an arbitrary distribution for regularization;and finally, it uses the cross entropy loss of labeled data to achieve the classification of semi-supervised learning. Experimental results show that, compared with other algorithms, the proposed algorithm has certain advantages in detecting a limited number of labeled samples, which can achieve high accuracy and low false alarm rate, while reducing the demand for labeled data.

关键词： intrusion detection algorithm Autocoder Generative adversarial network Semi-supervised learning.

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Applying an intrusion detection algorithm to Wireless Sensor Networks

Applying an Intrusion Detection Algorithm to Wireless Sensor...

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2nd International Workshop on Knowledge Discovery Data Mining

作者： Wang, Qi Wang, Shu Meng, Zhonglou Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China

ISBN: (纸本)9780769535432

Although static sensor nodes have low computation and communication capabilities, they have specific properties, and can acquire stable neighboring nodes' information, which can be used for detection of anomalies in networking and behaviors of the neighbor nodes, thus providing security for Wireless sensor networks. In many attacks against sensor networks, the first step for an attacker is to impersonate itself as a legitimate node within the network. To make a sensor node capable of detecting an intruder, a simple dynamic statistical model of the neighboring nodes is needed to build, together with a low-complexity detection algorithm to monitor received packet power levels. A detection algorithm based on security scheme for Wireless sensor networks is introduced in this paper.

关键词： Wireless sensor networks intrusion detection algorithm abnormal receive power algorithm simulation

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Applying an intrusion detection algorithm to Wireless Sensor Networks

Applying an Intrusion Detection Algorithm to Wireless Sensor...

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2009 Second International Workshop on Knowledge Discovery and Data Mining

作者： QiWang,ShuWang,ZhonglouMeng Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan,China

Although static sensor nodes have low computation and communication capabilities,they have specific properties,and can acquire stable neighboring nodes' information,which can be used for detection of anomalies in networking and behaviors of the neighbor nodes,thus providing security for Wireless sensor *** many attacks against sensor networks,the first step for an attacker is to impersonate itself as a legitimate node within the *** make a sensor node capable of detecting an intruder,a simple dynamic statistical model of the neighboring nodes is needed to build,together with a low-complexity detection algorithm to monitor received packet power levels.A detection algorithm based on security scheme for Wireless sensor networks is introduced in this paper.

关键词： Wireless sensor networks intrusion detection algorithm abnormal receive power algorithm simulation

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Construction Cosine Radial Basic Function Neural Networks Based on Artificial Immune Networks

Construction Cosine Radial Basic Function Neural Networks Ba...

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6th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Zeng, YongJin Zhuang, JianDong Jimei Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

ISBN: (纸本)9783642173127

In this paper, we propose a novel intrusion detection algorithm utilizing both Artificial Immune Network and RBF neural network. The proposed anomaly detection method using multiple granularities artificial immune network algorithm to get the candidate hidden neurons firstly, and then, we training a cosine RBF neural network base on gradient descent learning process. The principle interest of this work is to benchmark the performance of the proposed algorithm by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. It is observed that the proposed approach gives better performance over some traditional approaches.

关键词： intrusion detection algorithm RBF Neural Network Multiple Granularities Immune Network

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