Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Siddique, A.B.; Manivannan, D.

doi:10.1109/access.2022.3205337

Cited by 35 publications

(11 citation statements)

References 62 publications

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“…The training and test sets for the ANNs are then partially balanced to limit overlearning of a smaller number of output classes [25]. Partial balancing is performed by calculating the median of the number of real-world destinations for each type of surgery, with specific destination quantities above the median limited to the median number, and destination quantities below the median utilizing all available cases, for each of the 9 surgical specialties.…”

Section: Methodsmentioning

confidence: 99%

A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations

Walczak¹,

Velanovich

2023

PLoS ONE

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Background Frailty is frequently used by clinicians to help determine surgical outcomes. The frailty index, which represents the frequency of frailty indicators present in an individual, is one method for evaluating patient frailty to predict surgical outcomes. However, the frailty index treats all indicators of frailty that are used in the index as equivalent. Our hypothesis is that frailty indicators may be divided into groups of high and low-impact indicators and this separation will improve surgical discharge outcome prediction accuracy. Data and methods Population data for inpatient elective operations was collected from the 2018 American College of Surgeons National Surgical Quality Improvement Program Participant Use Files. Artificial neural network (ANN) models trained using backpropagation are used to evaluate the relative accuracy for predicting surgical outcome of discharge destination using a traditional modified frailty index (mFI) or a new joint mFI that separates high-impact and low-impact indicators into distinct groups as input variables. Predictions are made across nine possible discharge destinations. A leave-one-out method is used to indicate the relative contribution of high and low-impact variables. Results Except for the surgical specialty of cardiac surgery, the ANN model using distinct high and low-impact mFI indexes uniformly outperformed the ANN models using a single traditional mFI. Prediction accuracy improved from 3.4% to 28.1%. The leave-one-out experiment shows that except for the case of otolaryngology operations, the high-impact index indicators provided more support when determining surgical discharge destination outcomes. Conclusion Frailty indicators are not uniformly similar and should be treated differently in clinical outcome prediction systems.

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Section: Methodsmentioning

confidence: 99%

A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations

Walczak¹,

Velanovich

2023

PLoS ONE

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“…Chui et al [29] proposed a three-stage data generation algorithm that combines synthetic minority oversampling technique with generative adversarial networks (GANs) and variational autoencoder to produce high-quality data, aiming to address the issue of generating additional training samples for minority class in adversarial attacks. In addition to utilizing random oversampling, Dina et al [30] employed a data synthesis approach called a conditional generative adversarial network (CTGAN) to balance the data and improve the accuracy of various machine learning classifiers on the NSL-KDD and UNSW-NB15 intrusion detection datasets. Furthermore, Gaggero et al [31] proposed an anomaly detection algorithm based on neural network autoencoders by analyzing the typical architecture of the storage system in microgrids and potential vulnerabilities therein.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection Based on Adaptive Sample Distribution Dual-Experience Replay Reinforcement Learning

Tan,

Wang,

Zhu

et al. 2024

Mathematics

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In order to cope with ever-evolving and increasing cyber threats, intrusion detection systems have become a crucial component of cyber security. Compared with signature-based intrusion detection methods, anomaly-based methods typically employ machine learning techniques to train detection models and possess the capability to discover unknown attacks. However, intrusion detection methods face the challenge of low detection rates for minority class attacks due to imbalanced data distributions. Traditional intrusion detection algorithms address this issue by resampling or generating synthetic data. Additionally, reinforcement learning, as a machine learning method that interacts with the environment to obtain feedback and improve performance, is gradually being considered for application in the field of intrusion detection. This paper proposes a reinforcement-learning-based intrusion detection method that innovatively uses adaptive sample distribution dual-experience replay to enhance a reinforcement learning algorithm, aiming to effectively address the issue of imbalanced sample distribution. We have also developed a reinforcement learning environment specifically designed for intrusion detection tasks. Experimental results demonstrate that the proposed model achieves favorable performance on the NSL-KDD, AWID, and CICIoT2023 datasets, effectively dealing with imbalanced data and showing better classification performance in detecting minority attacks.

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“…[39], [40], [41], [42], [43] A. Generative Models 1) TimeGAN: TimeGAN, a generative model designed for time series data, leverages a Generative Adversarial Network (GAN) framework to generate synthetic time series data that closely resembles the original data's statistical properties and dependencies [44], [45]. It comprises two main components: the generator and the discriminator.…”

Section: Data Expansion Techniquesmentioning

confidence: 99%

Overview of Data Augmentation Techniques in Time Series Analysis

ANNAKI,

RAHMOUNE,

BOURHALEB

2024

IJACSA

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Time series data analysis is vital in numerous fields, driven by advancements in deep learning and machine learning. This paper presents a comprehensive overview of data augmentation techniques in time series analysis, with a specific focus on their applications within deep learning and machine learning. We commence with a systematic methodology for literature selection, curating 757 articles from prominent databases. Subsequent sections delve into various data augmentation techniques, encompassing traditional approaches like interpolation and advanced methods like Synthetic Data Generation, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). These techniques address complexities inherent in time series data. Moreover, we scrutinize limitations, including computational costs and overfitting risks. However, it's essential to note that our analysis does not end with limitations. We also comprehensively analyzed the advantages and applicability of the techniques under consideration. This holistic evaluation allows us to provide a balanced perspective. In summary, this overview illuminates data augmentation's role in time series analysis within deep and machine-learning contexts. It provides valuable insights for researchers and practitioners, advancing these fields and charting paths for future exploration.

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Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Cited by 35 publications

References 62 publications

A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations

A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations

Intrusion Detection Based on Adaptive Sample Distribution Dual-Experience Replay Reinforcement Learning

Overview of Data Augmentation Techniques in Time Series Analysis

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