Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

Du, Xiaoyu; Cheng, Cheng; Wang, Yujing; Han, Zhijie

doi:10.3390/a15070238

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Machine Learning [22][23][24][25], Clustering [26][27][28], and Spectral Imaging [29]. Over the past few years, UMAP has many improvements.…”

Section: Introductionmentioning

confidence: 99%

Kumap: Kernel Uniform Manifold Approximation and Projection for Out-of-sample Extensions Problem

Ran,

Li,

Zou

2024

Preprint

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Uniform Manifold Approximation and Projection (UMAP) is a popular dimensionality reduction and visualization algorithm recently proposed and widely used in several fields. However, UMAP encounters difficulties in mapping new samples into low-dimensional embeddings with what has been learnt from the learning process, which often referred to as the out-of-sample problem. In this paper, a kernel UMAP (KUMAP) method is proposed to address this problem, which is a kernel-based expansion technique. It uses the Laplacian kernel function to map the original samples to the low-dimensional space. In addition, to make full use of the label information in the sample data, a supervised kernel UMAP (SKUMAP) is also proposed. The KUMAP and SKUMAP methods are evaluated on different scale datasets in terms of the preservation of structure in small neighborhood data, silhouette coefficients, and classification accuracy. Compared with UMAP and other representative method, the KUMAP and SKUMAP methods have better embedding quality, higher classification accuracy, and better visualization.

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“…Machine Learning [22][23][24][25], Clustering [26][27][28], and Spectral Imaging [29]. Over the past few years, UMAP has many improvements.…”

Section: Introductionmentioning

confidence: 99%

Kumap: Kernel Uniform Manifold Approximation and Projection for Out-of-sample Extensions Problem

Ran,

Li,

Zou

2024

Preprint

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“…Networks are in constant danger of various attack types. From port scanning to message fooding and from attacks to MAC and the network layer to application layer attacks, many different attack types may threaten network security [1]. An attacker can reach private and precious information by intruding on the network.…”

Section: Introductionmentioning

confidence: 99%

Nesting Circles: An Interactive Visualization Paradigm for Network Intrusion Detection System Alerts

Shahryari

Khanli

Ramezani

et al. 2023

Security and Communication Networks

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Intrusion detection systems (IDSs) are valuable tools for fighting against those who want to intrude on the network and steal sensitive information for any reason. These tools, however, have difficulties in their essence. The generated alerts are in textual format, and extracting the exact information from the textual files needs lots of time and scrutiny. Also, not all alerts are accurate, and these tools suffer a setback named false-positive alerts, meaning that although no attack occurs, they may log some alerts. It is almost impossible to detect the penetration according to the discussed conditions. Information visualization is a method that transforms information into a visual representation for a better and quicker understanding. Indeed, the more the visualization is representative and straightforward, the more information it can transfer and the more worthy it is. This paper proposes a new paradigm for visualizing IDS alerts named nesting circles. We keep simplicity by using circles as the primary mark and the size and color as the only used channels. This makes the visualization easy to read and intuitive to understand. Furthermore, nesting circles provide a complete visualization of explicit and implicit information to the admin, and the previous approaches lacked this vital feature. The efficiency of nesting circles is examined through the VAST challenge case study, and it is shown to be effective in finding hidden attacks in the logs.

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“…Several studies have evaluated network intruder detection techniques, particularly those based on machine learning and deep learning techniques, using the UNSW-NB15 dataset. We will compare our method to those of four other researchers in this section: Breiman[9], Singh et al[10], Kabir et al[11],and Du et al[12]. Table2compares our top UNSW-NB15 dataset results to those of other writers.…”

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confidence: 99%

Intrusion Detection System Using Hybrid model

Chebbi,

Younes,

Ayed

2024

Preprint

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Technology and network industries have advanced quickly in recent decades.The expansion of piracy and the compromise of many existing systems has made it essential to develop information security solutions that can identify new threats. In order to address these issues, this work proposes a system called IDS-AI that is based on artificial intelligence techniques. It is capable of detecting recent and dispersed invasions. We employed an auto-encoder for features reduction and two models to assess CNN and SVM in order to evaluate our strategy. The experimental analysis of the dataset demonstrates the suggested model’s capability to produce reliable results. On the UNSW-NB15 dataset, our model actually achieves 99.58% and 99.66% accuracy for SVM and CNN, respectively.

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Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

Cited by 7 publications

References 26 publications

Kumap: Kernel Uniform Manifold Approximation and Projection for Out-of-sample Extensions Problem

Kumap: Kernel Uniform Manifold Approximation and Projection for Out-of-sample Extensions Problem

Nesting Circles: An Interactive Visualization Paradigm for Network Intrusion Detection System Alerts

Intrusion Detection System Using Hybrid model

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