Comparison of Machine Learning Algorithms to Build Optimized Network Intrusion Detection System

Sultana, H. Parveen; Shrivastava, Nirvishi; Dominic, Dhanapal Durai; Nalini, N; Balajee, J M

doi:10.1166/jctn.2019.7929

Cited by 13 publications

(2 citation statements)

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“…The manual labelling method has high accuracy, but low efficiency. When the number of web pages grows rapidly, this method is eliminated, and the automatic web page classification system begins to emerge (Parveen Sultana et al, 2019). Companies that use machine learning (Pham et al, 2020) to collect and update Internet data, and in this way they provide classified web content to their customers.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: An information extraction method based on improved mixed text density web pages

2023

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To improve the effect of web page information extraction, this paper proposes an improved information extraction method of mixed text density grids. Under various power constraints of the relay node itself, this paper proposes a design scheme of joint beamforming and artificial noise based on safety and rate maximisation. Furthermore, with the help of semidefinite relaxation techniques and first‐order approximations, it can be approximated as a semidefinite programming problem that is easy to solve. In addition, this paper uses an iterative algorithm based on a continuous convex approximation to process data to improve the accuracy of web page data extraction. The experimental results show that the information extraction method based on improved mixed text density webpages proposed in this paper has a good information extraction effect.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: An information extraction method based on improved mixed text density web pages

2023

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“…Only when all aspects are combined, complement each other, and continuously improve, can network information security be effectively achieved. The countermeasures of network information security are only discussed from the technical point of view [2].…”

Section: Introductionmentioning

confidence: 99%

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Chen

2024

JCSANDM

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At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

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Efficient text summarization method for blind people using text mining techniques

Basheer

Anbarasi

Sakshi

et al. 2020

Int J Speech Technol

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Comparison of Machine Learning Algorithms to Build Optimized Network Intrusion Detection System

Cited by 13 publications

References 0 publications

RETRACTED: An information extraction method based on improved mixed text density web pages

RETRACTED: An information extraction method based on improved mixed text density web pages

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

Efficient text summarization method for blind people using text mining techniques

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