A 3D Approach for the Visualization of Network Intrusion Detection Data

Zong, Wei; Chow, Yang-Wai; Susilo, Willy

doi:10.1109/cw.2018.00064

Cited by 5 publications

(13 citation statements)

References 27 publications

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“…Hence, when examining certain areas of data in 3D space, this data can be inversed and examined in higher dimensional space. From the visualization results, we show that key visual features of the UNSW-NB15 datasets are comparable with those presented in related work [17]. Zong et al [17] showed that most generic attacks are visually clustered together in both the training and test sets.…”

Section: Results For the Unsw-nb15 Datasetsupporting

confidence: 74%

“…From the visualization results, we show that key visual features of the UNSW-NB15 datasets are comparable with those presented in related work [17]. Zong et al [17] showed that most generic attacks are visually clustered together in both the training and test sets. In addition, there are some clusters that contain only normal connections in both the training and test sets.…”

Section: Results For the Unsw-nb15 Datasetsupporting

confidence: 74%

“…6(f) respectively show sections that contain a mixture of network traffic in the training and test sets. Despite the visualization results in our experiment differing from the results in [17], the visual features are similar. This affirms the validity of our 3D visualization results.…”

Section: Results For the Unsw-nb15 Datasetmentioning

confidence: 45%

“…McKenna et al [9] showed a cyber security dashboard that can help experts understand global attack patterns. An approach to visualizing network intrusion datasets in 3D space was presented in Zong et al [17]. Results of this approach demonstrated that it can be used to identify visual characteristics in the datasets, which can potentially contribute to improving detection performance of machine learning models in NIDS.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, previous work has shown that complex attack patterns in NIDS can be visualized in various forms [1] [9]. Previous research in this area includes a visual approach to analyzing the characteristics of network intrusion detection datasets in 3D space [17]. Visualization makes these characteristics more comprehendible and intuitive, while they may be difficult to perceive when using traditional statistical data analysis alone [7].…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality Reduction and Visualization of Network Intrusion Detection Data

Zong

Chow

Susilo

2019

Information Security and Privacy

Self Cite

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Nowadays, network intrusion detection is researched extensively due to increasing global network threats. Many researchers propose to incorporate machine learning techniques in network intrusion detection systems since these techniques allow for automated intrusion detection with high accuracy. Furthermore, dimensionality reduction techniques can improve the performance of machine learning models, and as such, are widely used as a pre-processing step. Nevertheless, many researchers consider machine learning techniques as a black box because of its complex intrinsic mechanism. Visualization plays an important role in facilitating the understanding of such sophisticated techniques because visualization is able to offer intuitive meaning to the machine learning results. This research investigates the performance of two dimensionality reduction techniques on network intrusion detection datasets. In addition, this work also demonstrates visualizing the resulting data in 3-dimensional space. The purpose of this is to possibly gain insight into the results, which can potentially aid in the improvement of machine learning performance.

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Section: Results For the Unsw-nb15 Datasetsupporting

confidence: 74%

Section: Results For the Unsw-nb15 Datasetsupporting

confidence: 74%

Section: Results For the Unsw-nb15 Datasetmentioning

confidence: 45%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dimensionality Reduction and Visualization of Network Intrusion Detection Data

Zong

Chow

Susilo

2019

Information Security and Privacy

Self Cite

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Intrusion detection: A model based on the improved vision transformer

Yang

Gao

et al. 2022

Trans Emerging Tel Tech

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We propose an intrusion detection model based on an improved vision transformer (ViT). More specifically, the model uses an attention mechanism to process data, which overcomes the flaw of the short-term memory in recurrent neural network (RNN) and the difficulty of learning remote dependency in convolutional neural network. It supports parallelization and has a faster computing speed than RNN. A sliding window mechanism is presented to improve the capability of modeling local features for ViT. The hierarchical focal loss function is used to improve the classification effect, and solve the issue of the data imbalance. The public intrusion detection dataset NSL-KDD is used for experimental simulations. By experimental simulations, the accuracy is up to 99.68%, the false-positive rate is 0.22%, and the recall rate is 99.57%, which show that the improved ViT has better accuracy, false positive rate, and recall rate than existing intrusion detection models.

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UNSW‐NB15 computer security dataset: Analysis through visualization

Zoghi

Serpen

2023

Security and Privacy

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Class imbalance refers to a major issue in data mining where data with unequal class distribution can deteriorate classification performance. Although it alone affects the performance of the classifiers, the joint‐effect of class imbalance and overlap is more damaging. Data overlap happens when multiple classes are assigned to a single data point causing the classifiers to misidentify the class boundaries. This study offers a deep insight into the intricacies of the UNSW‐NB15 dataset and two issues that may lead the data‐driven models to demonstrate poor performance. The most commonly used visualization methods such as bar chart, 3D and 2D scatter plots, intercluster distance map, and parallel coordinate diagram were employed to depict the data imbalanced and overlap. However, their limitations in capturing the overlapping issue led us to propose an accurate, easy‐to‐interpret, and scalable overlapping visualization method. The method clearly detects the data overlap and illustrates the effect of several data scalers in dealing with the data overlap. To verify the accuracy of the proposed method, a number of classifiers were implemented along with the scalers and the calculated AUC scores were compared to those calculated from the classifiers that were implemented on the original dataset.

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A 3D Approach for the Visualization of Network Intrusion Detection Data

Cited by 5 publications

References 27 publications

Dimensionality Reduction and Visualization of Network Intrusion Detection Data

Dimensionality Reduction and Visualization of Network Intrusion Detection Data

Intrusion detection: A model based on the improved vision transformer

UNSW‐NB15 computer security dataset: Analysis through visualization

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