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...
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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 intrusiondetection 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 intrusiondetection technology, and recovering data. intrusiondetection 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.
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 exp...
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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 intrusiondetection *** 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.
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...
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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 intrusiondetection 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.
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 alg...
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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.
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 ...
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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 detectionalgorithm to monitor received packet power levels. A detectionalgorithm based on security scheme for Wireless sensor networks is introduced in this paper.
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 ...
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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 detectionalgorithm to monitor received packet power levels.A detectionalgorithm based on security scheme for Wireless sensor networks is introduced in this paper.
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 net...
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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.
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