An effective intrusion detection framework based on SVM with feature augmentation

Wang, Huiwen; Gu, Jie; Wang, Shanshan

doi:10.1016/j.knosys.2017.09.014

Cited by 229 publications

(88 citation statements)

References 39 publications

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“…It was used and designed by Guang-Bin-Huang. Extreme Learning machines are timber move forward neural networks for grouping, fading, clustering, sparse approximation, compression and feature Sophistication with a single layer or multiple layers of guarded nodes, where the parameters of adjacent to nodes (not just the weights connecting inputs to hidden nodes) need not be tuned [1] [2]. These hidden nodes can be assigned randomly and not at all updated (i.e.…”

Section: Machinementioning

confidence: 99%

“…All the algorithms discussed by wang et al [2],kuang et al [4],Aburomman[5],Fernaaz and Jabbar [8],to improve the performance of intrusion detection systems. These intrusion discovery calculations (for example SVM,ELM) which are executed utilizing appropriated structures like Jupyter to process bigger informational collections.The algorithms is executed dependent on wang.et.al [1] proposed an interruption identification structure dependent on SVM and approve their strategy on the NSL-KDD informational dataset.…”

Section: Related Workmentioning

confidence: 99%

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Extreme Learning Machine with sigmoid activation function on large data

2019

IJRTE

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This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

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Section: Machinementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Extreme Learning Machine with sigmoid activation function on large data

2019

IJRTE

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“…IDS detects whether there is an attack behavior in the network and immediately performs response processing. Recently, a large number of techniques are applied to intrusion detection, such as rule-based network intrusion detection technology [8], artificial immune system [9], clustering-based technology [10,11], Support Vector Machine (SVM) [12,13], neural network [14][15][16], etc. Currently, among many intrusion detection algorithms, neural network-related algorithms have been widely used because of their good robustness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

“…obtain the new train data D = {y k , t k } 6: generate w i and b i randomly, set the number of hidden neurons L 7: calculate the output of hidden neurons H according to the Equation (13) 8: calculate the output weight of classifier β according to the Equation (14) 9: formulate the feature matrix X t for DT 10: X t = Z − score(X t ) 11: Y t = X t A * 12: calculate the output of hidden neurons H t for test data according to the Equation (13) 13: T = H t β according to the Equation(12) …”

mentioning

confidence: 99%

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Zheng

Zhou

Wang

et al. 2020

Sensors

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The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

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“…Bearing is the important component of mechanical equipment, and the reliable fault diagnosis method of bearing is key to ensuring its safe operation, which is helpful to safe operation of mechanical equipment [1][2][3][4][5][6][7][8][9]. Support vector machine (SVM) classifier [10][11][12] has a good ability to solve the classification problems, which has been applied in fault diagnosis of bearing [13]. Relevance vector machine (RVM) based on sparse Bayesian framework has a sparser representation than SVM, which has a better application prospect in fault diagnosis of bearing.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Bearing by Utilizing LWT‐SPSR‐SVD‐Based RVM with Binary Gravitational Search Algorithm

Fei

2018

Shock and Vibration

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The fault diagnosis method of bearing based on lifting wavelet transform (LWT)-self-adaptive phase space reconstruction (SPSR)-singular value decomposition (SVD)-based relevance vector machine (RVM) with binary gravitational search algorithm (BGSA) is presented in this study, among which LWT-SPSR-SVD (LSS) is presented for feature extraction of the bearing vibration signal, the dynamic characteristics of lifting wavelet coefficients' (LWCs') reconstructed signals of the bearing vibration signal can be reflected by SPSR for LWCs' reconstructed signals of the bearing vibration signal, and BGSA is used to select the embedding space dimension and time delay of phase space reconstruction (PSR) and kernel parameter of RVM. In order to show the superiority of LWT-SPSR-SVD-based RVM with BGSA (LSS-BGSA-RVM), the traditional RVM trained by the training samples with the features based on LWT-SVD (LS-RVM) is used to compare with the proposed LSS-BGSA-RVM method. The experimental result demonstrates that compared with LS-RVM, LSS-BGSA-RVM can achieve the higher diagnosis accuracy for bearing.

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An effective intrusion detection framework based on SVM with feature augmentation

Cited by 229 publications

References 39 publications

Extreme Learning Machine with sigmoid activation function on large data

Extreme Learning Machine with sigmoid activation function on large data

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

Fault Diagnosis of Bearing by Utilizing LWT‐SPSR‐SVD‐Based RVM with Binary Gravitational Search Algorithm

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