Intrusion Detection Method Based on Deep Learning

Tian, Chongrui; Zhang, Fengbin; Li, Zhaoxiang; Wang, Ruidong; Huang, Xunhua; Liang, Xi; Zhang, Yi

doi:10.1155/2022/1338392

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…ML‐based DoS detection system was introduced in de Lima Filho et al 48 for classifying network traffic. The intrusion detection method was analyzed in Tian et al 49 by DL. The fuzzy attribute triadic concept vector model (FCTA) was designed in Wang et al 50 with higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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Section: Related Workmentioning

confidence: 99%

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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Retracted: Intrusion Detection Method Based on Deep Learning

2023

Wireless Communications and Mobile Computing

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Prospective Directions of Traffic Analysis and Intrusion Detection Based on Neural Networks

Ilyenko

Kravchuk

et al. 2022

Cybersecurity

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The main problems of the network security at the moment are the difficulty of combining existing systems from different vendors and ensuring their stable interaction with each other. Intrusion detection is one of the main tasks of a proper level of network security, because it is they who notify about attacks and can block them when detected. Today, monitoring and analyzing the quality of traffic in the network, detecting and preventing intrusions is helped by IDS systems and IDS systems of the new generation IPS. However, they have been found to have certain drawbacks, such as the limitations of signature-based systems, as static attack signatures limit the flexibility of systems and pose the threat of missing detection of other attacks not entered into the database. This gives rise to the creation of more and more new hybrid systems, but the challenge is to ensure their efficiency and flexibility, which is helped by the use of artificial neural networks (ANNs). This paper considers ways to improve the use of the convolutional neural network model itself by means of modified processing, data analysis, the use of Softmax and FocalLoss functions to avoid the problem of uneven distribution of sample data by the ratio of positive and negative samples, based on training using the KDD99 dataset. The article provides practical examples of possible integration of IDS and ANN systems. Combinations of backpropagation neural networks and radiant-basis neural networks, which showed some of the best results and proved that the combination of networks helps to increase the efficiency of these systems and create a flexible network adjusted to the needs and requirements of the systems. Although the use of artificial neural networks is a popular tool, it has identified a number of disadvantages: critical dependence on the quality of the dataset, which pours both the quality of networking and the amount of data (the more data, the better and more accurate the network training). But if the data is excessive, there is a chance of missing such implicit, but also dangerous attacks as R2L and U2R.

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Intrusion Detection Method Based on Deep Learning

Cited by 3 publications

References 22 publications

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Retracted: Intrusion Detection Method Based on Deep Learning

Prospective Directions of Traffic Analysis and Intrusion Detection Based on Neural Networks

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