Botnet Detection in Network System Through Hybrid Low Variance Filter, Correlation Filter and Supervised Mining Process

Saputra, Ferry Astika; Masputra, Muhammad Fajar; Syarif, Iwan; Ramli, Kalamullah

doi:10.1109/icdim.2018.8847076

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Some prefer using decision trees to model the appearance motion features. The approaches using decision trees 53 have the benefits of normalization and scaling of data not being required, no considerable impact of missing values, better visualization, and absence of irrelevant features, so these issues will not affect the decision trees. However, such approaches are prone to overfitting and require longer time to train the decision trees.…”

Section: Related Workmentioning

confidence: 99%

Anomaly behavior detection analysis in video surveillance: a critical review

et al. 2023

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Anomaly detection is one of the most researched topics in computer vision and machine learning. Manual detection of an oddity in a video costs significant time and money, so there is a need for an autonomous detection system that can analyze the process and detect the anomaly in the majority of captured video datasets. Through an in-depth study on the recently published works on anomaly detection, a review is prepared to highlight the various tasks performed in abnormal behavior detection. Descriptions along with the pros and cons of various machine-learning and non-machine-learning techniques are discussed in depth. Similarly, more concentration is given to the generation adversarial network (GAN), and a comprehensive description of its design for achieving a better abnormality detection rate is provided. Moreover, a comparison of various state-of-the-art approaches on the basis of their methodologies, advantages, and disadvantages is given. We further quantitatively analyze some of the recent robust approaches at the frame level on the UCSD Ped1 dataset, with the GAN-based model achieving an astonishing performance. We provide various suggestions on how to further increase the performance of GAN for abnormal behavior detection in surveillance videos.

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Section: Related Workmentioning

confidence: 99%

Anomaly behavior detection analysis in video surveillance: a critical review

et al. 2023

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“…For example, in a setting where one may expect measurement error to be distributed evenly over the variables, it is reasonable to suspect that variables with larger observed variance will be less corrupted by the error and hence contain more underlying signal. Perhaps as a result of this, it is also common to remove columns with the smallest variance as part of pre-processing, a step known in the machine learning community as applying a 'low variance filter' (see for example Silipo et al (2014), Singh et al (2017), Abou Elhamayed (2018), Langkun et al (2020), Saputra et al (2018), Kalambe et al (2020)). This is however a somewhat crude way of using this potential information, and may eliminate important variables that happen to have a small variation, a risk which the practice of scaling variables aims to mitigate.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional regression with potential prior information on variable importance

Stokell

Shah

2022

Stat Comput

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There are a variety of settings where vague prior information may be available on the importance of predictors in high-dimensional regression settings. Examples include the ordering on the variables offered by their empirical variances (which is typically discarded through standardisation), the lag of predictors when fitting autoregressive models in time series settings, or the level of missingness of the variables. Whilst such orderings may not match the true importance of variables, we argue that there is little to be lost, and potentially much to be gained, by using them. We propose a simple scheme involving fitting a sequence of models indicated by the ordering. We show that the computational cost for fitting all models when ridge regression is used is no more than for a single fit of ridge regression, and describe a strategy for Lasso regression that makes use of previous fits to greatly speed up fitting the entire sequence of models. We propose to select a final estimator by cross-validation and provide a general result on the quality of the best performing estimator on a test set selected from among a number M of competing estimators in a high-dimensional linear regression setting. Our result requires no sparsity assumptions and shows that only a $$\log M$$ log M price is incurred compared to the unknown best estimator. We demonstrate the effectiveness of our approach when applied to missing or corrupted data, and in time series settings. An R package is available on github.

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