An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

Tang, Yanqiang; Li, Chenghai

doi:10.1109/access.2021.3093313

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…The comparative analysis of the proposed method with the conventional intrusion detection methods like Optimized ELM [24], Improved Kernel based ELM [13] and Optimized Deep Learning [25] using the UNSW-NB15 is depicted in Figure 13. The detailed analysis using the UNSW-NB15 and CSE-CIC-IDS2018 is depicted in Table 5.…”

Section: Comparative Analysis With State Of the Art Methodsmentioning

confidence: 99%

“…An optimized ELM was designed by Tang, Y. and Li, C [24] to detect intrusions in the network through sequential learning. In this, the issue concerning the initialization of the deviations and weights of the online regularized ELM was optimally solved using the optimization approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The slower convergence of the optimization technique, along with the local optimal convergence limits the performance. Tang, Y. and Li, C [24] Optimized ELM The computation complexity of the method was minimal, with a shorter training time.…”

Section: Literature Reviewmentioning

confidence: 99%

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Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

2023

IJIES

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The intrusion detection system (IDS) plays an imperative role in defending the network from attacks. But, the IDS data is imbalanced, making the process complex for detecting the attacks accurately. According to these problems, this study proposes a network intrusion detection system based on an enhanced synthetic minority oversampling technique (SMOTE) and lightweight hybrid CAE (convolutional auto encoder)-ELM (extreme learning machine) model. Initially, the normalization of the original data is performed to avoid the influence of the maximum and minimum values. Secondly, an enhanced SMOTE is employed for solving the issues of the less detection rate of minority-attacks because of less training data. Here, the war strategy optimization (WSO) is incorporated with the SMOTE to design a novel WSO-based SMOTE technique for balancing the dataset. The three major steps utilized in WSO-based SMOTE are WSO based clustering, filtering and over-sampling. Then, the Information gain and fisher score based features extraction is employed for the reduction of computational complexity prior to the intrusion detection. Finally, the lightweight hybrid CAE-ELM model is executed for attack detection. CAEs are one of the most commonly used learning models because of their ability to construct a higher-level feature representation from the input data. Another model used in intrusion detection is the ELM, which provides an acceptable discrimination performance as well as a fast speed of learning. The performance of the proposed NIDS model is tested on the two benchmark datasets and achieved better accuracies of 99.21% and 99.15% on the UNSW-NB15 and CSE-CIC-IDS2018 datasets.

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Section: Comparative Analysis With State Of the Art Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

2023

IJIES

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“…In [24], the authors proposed a hybrid intrusion detection system for online network intrusion detection. e researchers integrated improved particle swarm optimization and regularized extreme learning machine (IPSO-IRELM).…”

Section: Hybrid Intrusion Detection Systemmentioning

confidence: 99%

A Systematic Review on Hybrid Intrusion Detection System

Maseno

Wang

Xing

2022

Security and Communication Networks

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As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.

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“…Support vector machine (SVM), back propagation neural network (BP) and extreme learning machine (ELM) have been widely used in the detection of meat and adulteration [ 16 , 24 , 25 , 26 , 27 ]. The uses of new algorithms to optimise parameters and their combination with practical problems have become an important research direction for machine learning in recent years [ 28 , 29 , 30 ]. Quantum particle swarm optimisation (QPSO) was used to improve SVM in evaluating meat freshness [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

Fan

Zhu

et al. 2022

Foods

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The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.

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An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine

Cited by 15 publications

References 37 publications

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

A Systematic Review on Hybrid Intrusion Detection System

Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm

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