Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

Lin, Haifeng; Xue, Qilin; Bai, Ding

doi:10.1016/j.dcan.2022.09.021

Cited by 40 publications

(16 citation statements)

References 21 publications

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“…In addition, for leaves with irregular and complicated shapes, e.g., those of Fatsia japonica and conifer needles, it is not convenient to choose suitable geometric prisms enclosing the leaf blade. Nevertheless, with the advancement of computer hardware ( Lin et al., 2022b ) with superior graphics processing capabilities and artificial intelligence algorithms that can support forest survey applications, we believe that accurate tree feature recognition and plant phenotyping research ( Li et al., 2022 ) at fine scales will become easier and more accurate in the future, which will propel the development of precision forestry ( Holopainen et al., 2014 ) that incorporates interpretations of the volume-based GF vol of trees.…”

Section: Discussionmentioning

confidence: 99%

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

Zhu

Fan

et al. 2023

Front. Plant Sci.

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The gap fraction (GF) of vegetative canopies is an important property related to the contained bulk of reproductive elements and woody facets within the tree crown volume. This work was developed from the perspectives of porous media theory and computer graphics techniques, considering the vegetative elements in the canopy as a solid matrix and treating the gaps between them as pores to guide volume-based GFvol calculations. Woody components and individual leaves were extracted from terrestrial laser scanning data. The concept of equivalent leaf thickness describing the degrees of leaf curling and drooping was proposed to construct hexagonal prisms properly enclosing the scanned points of each leaf, and cylinder models were adopted to fit each branch segment, enabling the calculation of the equivalent leaf and branch volumes within the crown. Finally, the volume-based GFvol of the tree crown following the definition of the void fraction in porous media theory was calculated as one minus the ratio of the total plant leaf and branch volume to the canopy volume. This approach was tested on five tree species and a forest plot with variable canopy architecture, yielding an estimated maximum volume-based GFvol of 0.985 for a small crepe myrtle and a minimal volume-based GFvol of 0.953 for a sakura tree. The 3D morphology of each compositional element in the tree canopy was geometrically defined and the canopy was considered a porous structure to conduct GFvol calculations based on multidisciplinary theory.

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Section: Discussionmentioning

confidence: 99%

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

Zhu

Fan

et al. 2023

Front. Plant Sci.

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“…This method can reduce redundant information in the dataset, improve the performance of IDS, and reduce its processing time. Lin et al [27] designed a multi feature extraction ELM (MFE-ELM) algorithm for cloud computing, added a multi feature extraction process to cloud servers, and used the MFE-ELM algorithm deployed on cloud nodes to detect and discover network intrusion on cloud nodes. The proposed algorithm can effectively detect and recognize most network data packets with good model performance.…”

Section: Related Workmentioning

confidence: 99%

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Chen,

Guo,

Zhang

et al. 2023

Symmetry

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Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system.

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“…They evaluated their model on NSL-KDD, UNSW-NB15, and CIC-IDS 2017. To prevent intrusion in a cloud-based IoT environment, Lin et al [41] developed an IDS using multi-feature extraction Extreme Learning Machine (MELM) to detect attacks on the NSL-KDD dataset. Yousefnezhad et al [42] increased the detection rate and reduced the false alarm rate by proposing an ensemble classification model using Dempster-Shafer technique (DM) to detect assaults in the network traffic.…”

Section: Comparative Analysismentioning

confidence: 99%

Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

Bhavani Mallampati,

Hari

2023

Computers, Materials &Amp; Continua

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Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are utilized. The Adaptive Synthetic oversampling technique generates synthetic samples for minority classes. In the proposed fusion-based feature importance, we use different approaches from the filter, wrapper, and embedded methods like mutual information, random forest importance, permutation importance, Shapley Additive exPlanations (SHAP)-based feature importance, and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its rank. Then by simple plurality voting, the most optimal features are retrieved. Then the optimal features are fed to various models like Extra Tree (ET), Logistic Regression (LR), Support vector Machine (SVM), Decision Tree (DT), and Extreme Gradient Boosting Machine (XGBM). Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the performance. The experiments were carried out on the original imbalanced data and balanced data. The outcomes demonstrate that the balanced data scenario knocked out the imbalanced data. Finally, the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%, 99.68%, and 92.4%, with 9, 7 and 8 features by training time of 1.5, 4.5 and 5.5 s on Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD), Canadian Institute for Cybersecurity (CIC-IDS 2017), and UNSW-NB15, datasets respectively. In addition, the suggested technique has been examined and contrasted with the state of art methods on three datasets.

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Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

Cited by 40 publications

References 21 publications

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

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