# HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network

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## Abstract

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## 1. Introduction

#### Our Contributions

- The use of a hybrid network intrusion dataset, i.e., merging of BoT-IoT and UNSW NB-15 dataset.
- The performance of low-rank optimised SVM by determining the hyperplane efficiently for predicting classifiable labels on reproduced observations.
- The weight optimization method through a low rank matrix factorization process on deep learning classifiers (CNN and CNN-MLP) for improvising multilabel classification.
- The use of Bayesian optimization in conjunction with low-rank factorization SVM, CNN and CNN-MLP models, enables efficient hyperparameter tuning and model optimization.

## 2. Related Work

## 3. Methodology

#### 3.1. Dataset Details

#### 3.2. Data Pre-Processing and Mixing

#### 3.3. Discretization

#### 3.4. Normalization

#### 3.5. Low Rank Factorization

#### 3.6. LR-SVM

#### 3.7. LR-CNN-MLP

#### 3.8. Bayesian Optimization

Algorithm 1. Optimal selection of hyperparameter using Gaussian-based Bayesian parameter optimization for multi-label classification. |

Input: Attack dataset in CSV format (dataset)Output: Probability estimates for each label in the multi-label classification problemStep 1: Load the dataset and split it into training ${X}_{train}$ and testing sets ${X}_{test}$.Step 2: Perform data pre-processing on ${X}_{train}$.Step 3: Define the rank r, no. of classes n, no. of labels k, iters, learning rate lr, epochs and optimizer as hyperparameters.Step 4: Initialize CNN-MLP model and train it on ${X}_{train}$ and target labels Y (n * k).spm ← model (${P}_{score}$(conv(${X}_{train}$)))/${P}_{hyp}$(conv(${X}_{train}$)) Step 4.1: Apply the parameters to objective function$hy{p}^{*}$ = arg $mi{n}_{hyp\in k}$ f(k) Step 4.2: Update surrogate probabilistic model for new parameters.spm ← model (${P}_{score}$(conv(${X}_{train}$)))/${P}_{hyp}$(conv(${X}_{train}$)) Step 5: Obtain weights ‘W’ of last fully connected layer.Step 6: Perform low-rank matrix factorization on ‘W’.W = ${M}_{train}$${N}_{train}$ T where ${M}_{train}$ = h*r ${N}_{train}$ = r*k Step 7: Initialize r with random values.Step 8: Define a function ‘$updat{e}_{r}$$({M}_{train}$, ${N}_{train}$, lr)’ at last fully connected layer.Step 8.1: Obtain the low-rank matrix until convergence:${M}_{train}$ = ${M}_{train}$ * (${X}_{train}$ * ${N}_{train}$)/(${M}_{train}$ * ${N}_{train}.T$ * ${N}_{train}$) ${N}_{train}$ = ${N}_{train}$ * (${X}_{train}.T$ * ${M}_{train}$)/(${N}_{train}$ * ${M}_{train}.T$ * ${M}_{train}$) Step 8.2: Compute the residualsE=W-${M}_{train}$${N}_{train}$ T Step 8.3: Compute the gradient of ‘${M}_{train}$’ ‘${N}_{train}$’ w.r.tgrad ${M}_{train}$ =−2E −${N}_{train}$ grad ${N}_{train}$= −2E T ${M}_{train}$ Step 8.4: Update ${M}_{train}$ and ${N}_{train}$ using learning rate as${M}_{train}$ = ${M}_{train}$−(lr* grad ${M}_{train}$) ${N}_{train}$ = ${N}_{train}$−(lr* grad ${N}_{train}$) Step 8.5: Update the weights as last fully connected layers.W = ${M}_{train}$${N}_{train}$ T Step 9: Replace the weights of last fully connected layer in CNN-MLP with updated weights${M}_{train}$${N}_{train}$. Step 10: Retrain the CNN-MLP model on ${M}_{train}$ and target labels Y using fully connected layer.Step 11: Repeat Steps 5–9 for ’iters’ iterationsStep 12: Probability estimates for each label in the multi-label classification problem as the average of the probability estimates obtained from the ensemble of models. |

## 4. Results and Discussion

#### 4.1. Parameter Tuning for Proposed SVM-Based Attack-Type Label Classification

#### 4.2. Parameter Tuning for Proposed CNN and CNN-MLP Based Attack-Type Label Classification

#### 4.3. Parameter Tuning for Guassian Based Bayesian Optimization Algorithm

#### 4.4. Limitation

## 5. Comparative Analysis

## 6. Conclusions

## 7. Future Scope

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 9.**Confusion matrix corresponding to the Bayesian optimization of the proposed model of LR-CNN-MLP.

Ref. No./Year | Technique | Data-Cleaning | Discretization | Normalization | Advantages | Limitations |
---|---|---|---|---|---|---|

[24]/ 2015 | Inexact Augmented Lagrange Multiplier | X | X | X | For complex data LRSR with spatial clustering for better performance. | LRR and LRSR not shown much improvement for simple data. Occupying single subspace. |

[25]/ 2016 | Inexact Augmented Lagrange Multiplier | ✓ | X | X | Instead of occupying single subspace, occupies multi subspace. Removal of Outliers and Noise with no computational cost. | LRS coefficient matrix is missing. |

[26]/ 2016 | Linearized alternating direction method (LADM) | X | X | ✓ | LRS coeffient matrix is obtained using LADM. | No systematical way to estimate parameters Lambda 1 and Lambda 2. |

[27]/ 2017 | Alternating direction method (ADM) | ✓ | X | ✓ | Fixed Lambda = 2, noise free data with independent subspaces. | Aim to obtain an effective data representation matrix is still needed. |

[28]/ 2019 | Wilcoxon Signed Rank | X | X | X | Fast and Flexible model with non-linear behavior and representation of data matrix. | Low detection performance. |

[29]/ 2019 | SAW, TOP SIS, MCM | X | X | X | The collected samples’ low rankness in low-dimensional space is used to create an instructive graph that captures local information. | Capturing global information is still an issue. |

[30]/ 2019 | Low-Rank Representation | X | X | X | Both local and global info of the original samples can be well captured. | Insufficient creation of dictionary |

[31]/ 2019 | LRaSMD | X | X | ✓ | Proper dictionary is created using LR and SM. | Single distribution can be used to simulate both anomalies and noise, which separates weak anomalies and noise. |

[32]/ 2020 | Manhattan Distance LSMD-MoG | X | X | ✓ | Single distribution is replaced by MoG. Not only stable but also effective for hyperspectral AD. | Lambda and beta set to 0.1. |

[33]/ 2020 | LELRP-AD | X | X | X | Low rank property of DM is enhanced. | The rank value r and cardinality c taken to be specific. |

[34]/ 2021 | Manhattan Distance LSMD-MoG | X | X | ✓ | Finds all anomalies and shapes them clearly. | WSL can not be used without LRR. |

Epochs | Learning Rate | Accuracy |
---|---|---|

Epochs = 150 | lr = 0.010 | 85.35% |

lr = 0.015 | 81.34% | |

lr = 0.020 | 82.21% | |

lr = 0.025 | 82.56% | |

Epochs = 200 | lr = 0.010 | 85.45% |

lr = 0.015 | 87.26% | |

lr = 0.020 | 85.65% | |

lr = 0.025 | 86.69% | |

Epochs = 250 | lr = 0.010 | 83.20% |

lr = 0.015 | 86.24% | |

lr = 0.020 | 86.54% | |

lr = 0.025 | 86.44% |

Learning Rate | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|

lr1 = 0.0010 | 0.888 | 0.920 | 0.904 | 92.45 |

lr2 = 0.0015 | 0.902 | 0.916 | 0.901 | 91.44 |

lr3 = 0.0020 | 0.891 | 0.926 | 0.909 | 94.26 |

lr4 = 0.0025 | 0.889 | 0.924 | 0.907 | 93.21 |

Learning Rate | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|

lr1 = 0.0010 | 0.943 | 0.975 | 0.959 | 96.12 |

lr2 = 0.0015 | 0.956 | 0.97 | 0.963 | 96.84 |

lr3 = 0.0020 | 0.944 | 0.979 | 0.961 | 98.17 |

lr4 = 0.0025 | 0.942 | 0.978 | 0.960 | 97.91 |

Model | Parameter | Accuracy |
---|---|---|

LR-SVM | c = 1.0, $\gamma $ = 0.1, lr = 0.0020, epochs = 150, and optimizer = SGD | 87.26% |

LR-CNN | $\alpha $ = 1.0, $\beta $ = 0.5, lr = 0.001, epochs = 100, layers = 2, and optimizer = Adam | 96.36% |

$\alpha $ = 1.0, $\beta $ = 0.5, lr = 0.002, epochs = 200, layers = 2, and optimizer = SGD | 97.65% | |

LR-CNN-MLP | $\alpha $ = 1.0, $\beta $ = 0.5, lr = 0.001, epochs = 100, layers = 2, and optimizer = Adam | 98.89% |

$\alpha $ = 1.0, $\beta $ = 0.5, lr = 0.0015, epochs = 125, layers = 2, and optimizer = SGD | 99.20% | |

$\alpha $ = 1.0, $\beta $ = 0.5, lr = 0.0020, epochs = 125, layers = 2, and optimizer = RMSProp | 98.54% |

Ref. No./Year | Dataset | DL Classifier | Parameters | Findings | Limitations |
---|---|---|---|---|---|

[41] 2019 | UNSW-NB15 | CNN, MLP | Accuracy and F1-Score | Good performance in terms of RMSE. | Not easily customizable. |

[42] 2020 | UNSW-NB15 | CNN | Accuracy | Analysis of min-max formulation with DL. | Specific attack types. |

[43] 2020 | Bot-IoT | CNN | Accuracy, Detection Rate, FAR | Results is binary and multiclass classification | No results for multilabel classification |

[44] 2021 | UNSW-NB15 | BCNN MCNN | Accuracy, Precision, Recall, F-measure | Skip connection methodology into CNN | Not performed well on the specific dataset. |

[45] 2021 | Bot-IoT | MLP | Precision and F1-Score | Help to monitor traffic flow in connected host | Only worked in binary classification |

[46] 2022 | Bot-IoT | CNN | Accuracy, Precision and Training Time | CNN achieved best results as compared to LR and DT | Computational and memory utilization can be improved by using optimized generalized techniques. |

Proposed Methodology | Hybrid dataset | LR-SVM | Accuracy | SVM has not been able to achieve results in multilabel classification of attack classes corresponding to one label | Due to high sparsity and high dimensionality of data in the integrated dataset |

Proposed Methodology | Hybrid dataset | LR-CNN-MLP | Accuracy, Precision, Recall and F1-Score | Hybrid CNN-MLP achieved results in multilabel classification of attack classes corresponding to one label |

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## Share and Cite

**MDPI and ACS Style**

Sharma, A.; Rani, S.; Sah, D.K.; Khan, Z.; Boulila, W.
HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network. *Sensors* **2023**, *23*, 8333.
https://doi.org/10.3390/s23198333

**AMA Style**

Sharma A, Rani S, Sah DK, Khan Z, Boulila W.
HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network. *Sensors*. 2023; 23(19):8333.
https://doi.org/10.3390/s23198333

**Chicago/Turabian Style**

Sharma, Ankita, Shalli Rani, Dipak Kumar Sah, Zahid Khan, and Wadii Boulila.
2023. "HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network" *Sensors* 23, no. 19: 8333.
https://doi.org/10.3390/s23198333