Oversampling Methods to Handle the Class Imbalance Problem: A Review

Sharma, Harsh; Gosain, Anushika

doi:10.1007/978-3-031-27609-5_8

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…A healthy number of intrepid researchers have applied oversampling [9][10][11][12][13][14], undersampling [15][16][17][18][19], and hybrid [20][21][22][23] preprocessing methods to restore balance to their training datasets. These methods are combined with feature classification methods to maximize benefits.…”

Section: A Data-level Mitigation Effortsmentioning

confidence: 99%

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

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The growth of the internet has advanced information-sharing capabilities and vastly increased the importance of global network security. However, because new and inconspicuous abnormal behaviors are nearly impossible to detect in massive network access environments, modern intrusion detection systems have identified a high rate of false-positive (FP) and false-negative (FN) attacks. To overcome this, this paper proposes a hybrid deep learning model that significantly mitigates the disadvantages of consistently imbalanced sample attack data. First, it resolves imbalanced data using random undersampling and synthetic minority oversampling techniques. Then, convolutional neural networks (CNNs) extract local and spatial features, and a transformer encoder extracts global and temporal features. The novelty of this combination increases recognition accuracy at the algorithm level, which is crucial to reducing FPs and FNs. The model was subjected to multiclassification testing on the NSL-KDD and CICIDS2017 benchmark datasets, and the results show that our model has higher classification accuracy and lower FP rates than state-ofthe-art intrusion detection models. Moreover, it significantly improves the detection rate of low-frequency attacks.

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Section: A Data-level Mitigation Effortsmentioning

confidence: 99%

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

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“…Note that the position of a user's phone (e.g., height and direction) affects RSSI measurements as does the model and software/firmware version of the phone; at the exact the same location, a combination of different user and phone could result in different RSSI values. Given the dominance of a few users and phones in constructing the database, we can also apply techniques like data augmentation [25] and data resampling [26] to reduce the risk of bias and overfitting and thereby improve a model's generalization as discussed in Section III-A1.…”

Section: Categorymentioning

confidence: 99%

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

Li,

Tang,

Kim

et al. 2024

Preprint

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Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for largescale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular, publicly-available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution, and quality of labels, and (3) the composition of the database records, and then identify potential issues and ways to address them in using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

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Section: Categorymentioning

confidence: 99%