ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features

Liu, Jin Ping; He, Jiezhou; Zhang, Wuxia; Ma, Tianyu; Tang, Zhaohui; Niyoyita, Jean Paul; Gui, Weihua

doi:10.1016/j.knosys.2019.04.008

Cited by 31 publications

(8 citation statements)

References 28 publications

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“…At last, the sampling weight of the sub-clusters is multiplied by the number of the minority class in date set D to calculate the under-sampling number of each sub-cluster, as shown in formula (7).…”

Section: The Proposed Algorithm a Adaptive Calculate The Number Of Un...mentioning

confidence: 99%

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An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data

Hong

Liu

2022

IEEE Access

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In view of the low classification accuracy of the minority class in imbalanced data, an algorithm called DPF-EL (Density Peaks and Fitness Ensemble Learning) based on density peaks clustering and fitness is proposed. Firstly, this method uses the density peaks clustering algorithm to divide the majority class into different sub-clusters, the local density calculated in the clustering process is used to assign weights to each sub-cluster, and the number of under-samplings is determined by the weights. Secondly, the concept of fitness is introduced into the sub-clusters. The selection probability of the samples is calculated according to the size of their fitness, and the majority class is under-sampled based on the selection probability. Finally, combined with boosting algorithm, iterative training is performed on the balanced data set. Experimental tests were conducted with KEEL imbalanced data sets, and the experimental results show that the performance of DPF-EL algorithm is better than other algorithms, which indicates the feasibility of the proposed algorithm. INDEX TERMS Density peaks clustering, imbalanced data, classification, fitness, undersampling.

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Section: The Proposed Algorithm a Adaptive Calculate The Number Of Un...mentioning

confidence: 99%

“…Step1: based on the formulas (1) and ( 2), the density peaks clustering algorithm is used to cluster the majority class, and the under-sampling number of each cluster is calculated by the formula ( 5) to (7).…”

Section: Algorithm Designmentioning

confidence: 99%

An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data

Hong

Liu

2022

IEEE Access

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“…Several ELMs with larger weights are chosen to construct the final ensemble for making decision. To effectively deal with the problem of network intrusion detection, (Liu et al 2019) proposed a selective ensemble of kernel ELMs-based intrusion detection method. In their proposed ensemble method, a marginal distance minimization-based selective ensemble is designed to construct the final intrusion detector.…”

Section: Introductionmentioning

confidence: 99%

Rotation transformation-based selective ensemble of one-class extreme learning machines

Xing

Bai

2021

Adv. in Comp. Int.

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Extreme learning machine (ELM) possesses merits of rapid learning speed and good generalization ability. However, due to the random initialization of connection weights, the network outputs of ELM are usually unstable. Similar to ELM, one-class ELM (OCELM) also has the disadvantage of output instability. To enhance the stability and generalization performance of OCELM, a selective ensemble of OCELMs based on rotation transformation is proposed. First, principal component analysis (PCA)-based rotation transformation is utilized to construct different transformed training sets. Furthermore, several component OCELMs are trained independently on these training sets. Second, a dissimilarity measure based on angle cosine is used to evaluate the dissimilarity between each pair of OCELMs. The diversity of each component OCELM in the obtained ensemble can be further achieved. Thereafter, the component OCELMs with lower value of diversity are removed from the original ensemble. Finally, the voting strategy is utilized to determine that testing samples belong to the target class or the non-target class. Experimental results on 15 UCI benchmark data sets and one handwritten digit data set show that the proposed method is superior to its related approaches.

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“…In this work, we consider the approximation of the linear quantile ridge regression estimator via random projection. Random projection is a classical technique for reducing storage and computational costs in various settings (Liu et al 2019; Wang et al 2013). Applications of random projection to vision problems have been popularly studied (Anand et al 2012; Bingham & Mannila, 2001; Mu et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Random projections for quantile ridge regression

Zhou

Jiang

et al. 2021

Stat

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Quantile regression estimate gives more complete information about the response distribution but is more costly to compute than mean regression. When the dimension is large, a ridge penalty is conventionally used to stabilize the estimate and achieve better bias-variance trade-off. We investigate a random projection approach to ease the computational burden and establish its statistical properties. Monte Carlo studies are carried out to illustrate the computational and statistical properties of the estimates.

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ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features

Cited by 31 publications

References 28 publications

An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data

An Ensemble Learning Algorithm Based on Density Peaks Clustering and Fitness for Imbalanced Data

Rotation transformation-based selective ensemble of one-class extreme learning machines

Random projections for quantile ridge regression

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