On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples

Zhang, Ying; Liu, Qiang

doi:10.1016/j.future.2022.03.007

Cited by 38 publications

(21 citation statements)

References 44 publications

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“…In the process of the long-term monitoring of towers, it was found that the original dataset composed of fault information has the following characteristics: the number of samples generated by normal operation of towers is much larger than the number of samples where faults occur, the frequency of occurrence of various types of mechanical faults is different, and it is impossible to artificially control the number of samples generated by each type of fault, resulting in a poorly balanced dataset which is difficult to use directly in the machine learning process. Thus, the SMOTE algorithm was chosen to process the original dataset [22]. There are three steps in the process of implementing the SMOTE algorithm.…”

Section: Construction Of Tower Mechanical Failure Dataset Based On Sm...mentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

Applied Sciences

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In this paper, a classification recognition algorithm for tower mechanical faults is proposed, and a multiclass central segmentation hyperplane support vector machine (CSH-SVM) is proposed to improve the existing multiclass support vector machine for problems in which a certain sample satisfies multiple hyperplanes at the same time. The tilt angle change and wind direction data were extracted using the tilt sensors and anemometers attached to the tower, and the temperature and humidity sensors, as well as real-time rainfall and water accumulation information, were combined to construct a sample of the original dataset during the operation of the tower. The unbalanced samples were improved using the synthetic minority oversampling technique (SMOTE) algorithm to construct a balanced dataset suitable for machine learning and improve the prediction accuracy of machine learning. At the same time, the support vector machine hyperplane under the one-vs-all classification principle was additionally computed, and the new hyperplane was computed via the existing hyperplane not only to solve the classification problem of the transition area under the one-vs-all classification so that the samples located in this area no longer meet two hyperplane equations at the same time, but also to reduce the probability of incorrect classification to a certain extent. Through verification, CSH-SVM can classify 15 out of 77 misclassified samples into the correct category with slightly higher computational power than the traditional one-vs-all classification SVM, which can improve the classification prediction accuracy for unbalanced tower mechanical failure datasets and make an accurate judgment on the current state of the tower through the tower data as to when the tower may generate mechanical failure, thus reducing economic loss and personal safety threats.

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Section: Construction Of Tower Mechanical Failure Dataset Based On Sm...mentioning

confidence: 99%

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Han

Wang

et al. 2023

Applied Sciences

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“…To improve learning from imbalanced samples, Zhang et al [48] presented IoT intrusion detection based on data augmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To accurately evaluate the effectiveness of HXG-BLSTM, we conducted in-depth comparison with the most recent DL algorithms that were employed in the cyber threat detection literature. In this comparison, we used deep learning algorithms from earlier studies such as [11], [28], [10], [32], [1], [9], BWO-CONV-LSTM [27], [48], [45], [6], [18], [21], [42], and [1]. The results of the comparison for deep learning approaches are shown in Table 9.…”

Section: Hxgblstm Vs Other Algorithmsmentioning

confidence: 99%

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

Amin,

El-Taweel,

Ali

et al. 2023

mendel

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The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.

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“…Therefore, many methods have been proposed to solve the problem of insufficient training datasets. For example, data augmentation methods [4], generative adversarial networks [5], and small sample learning techniques [6], etc.…”

Section: Introductionmentioning

confidence: 99%

FedSiM: a similarity metric federal learning mechanism based on stimulus response method with Non-IID data

Wang,

Zhang

2023

Meas. Sci. Technol.

Self Cite

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FL (Federal Learning) based on parameter sharing under the assumption that the data obey IID (Independent Identical Distribution) has already achieved good results in areas such as fault diagnosis. Data collected by the decentralized devices often do not obey IID. However, when faced with the scenario of client data obeying Non-IID distribution, its diagnostic accuracy is usually weak. Based on this, we did an investigation on the mechanism causing this phenomenon and found that it was attributed to the weight shift of the network. Therefore, based on the stimulus response principle, we investigated the network similarity of federal clients under different data distributions and explain the reasons for the weight shift. Firstly, it was pointed out that there are differences in the regions where the network is activated when performing different classification tasks. Then, FedSiM (Similarity Metric Federal Learning) was proposed based on the principle that there are differences between the activated regions. Finally, experiments were designed on the Case Western Reserve University bearing failure dataset for different degrees of independent identical distribution cases. The results show that FedSiM can improve the diagnostic accuracy by 15.8 percentage points in the case of Non-IID, and a few shared FedSiM methods to further improve the accuracy were also given.

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On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples

Cited by 38 publications

References 44 publications

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

Research on Tower Mechanical Fault Classification Method Based on Multiclass Central Segmentation Hyperplane Support Vector Machine Improvement Algorithm

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

FedSiM: a similarity metric federal learning mechanism based on stimulus response method with Non-IID data

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