# XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems

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## Abstract

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## 1. Introduction

- We built an XGBoost model to improve the detection of imbalanced distribution attack types using two benchmark IIoT IDS datasets, the X-IIoTID and TON_IoT. The raw datasets were preprocessed in several steps, including normalizing features, encoding labels, and splitting the training and testing data.
- We evaluated the proposed method with imbalanced multiclass classification by measuring two performance metrics, which were the confusion matrix and the learning curve.
- We compared the proposed method’s results with other related methods. The experimental results showed that our model enhanced the performance of attack detection for imbalanced multiclass classification in IIoT-based IDS datasets and outperformed other previous models on the same datasets.

## 2. Materials and Methods

#### 2.1. ML-Based IoT/IIoT IDS Context

#### 2.2. IIoT Datasets and the Imbalanced Multiclass Problem

#### 2.3. The Proposed Method

- The data processing. We performed feature normalization, label encoding, and splitting of the training and testing datasets from the original TON_IoT and X-IIoTID. In the feature normalization, we used a min–max scale for input features following the formula:$${X}_{i}^{N}=\frac{{X}_{i}^{N}-min\left({X}_{i}^{N}\right)}{max\left({X}_{i}^{N}\right)-min\left({X}_{i}^{N}\right)}$$In the label encoding, we processed the multiclass output values in order to convert non-numerical type data to numerical data for the ML model to learn. We encoded target labels (Y) with a value between 0 and ($n\_classes-1$), where $n\_classes$ is the number of different values in Y. We used the LabelEconder function of the Sklearn library in Python language to process this task. The ratio between the training data and testing data determined the number of sampling data separately used for the training and testing processes of the proposed method. In splitting the training and testing data, we divided the data with a ratio of 70:30, respectively.
- XGBoost-based IIoT IDS. XGBoost is one representative of the sequence model XGBoost. We chose the XGBoost model because of its advantages, including learning from its mistakes, fine-tuning extensive hyperparameters, scaling imbalanced data, and processing null values. The sequential ensemble method is known as boosting, which attempts to correct the mistakes of the previous models in their sequences. XGBoost is a kind of boosting algorithm that has been proven to boost weak learners in both classification and regression problems. The trees in XGBoost can create a new tree by considering the previous prediction value for the given input data of the tree and then maximize the gain in prediction. We present the main concept of XGBoost-based IIoT IDS datasets in Algorithm 1.In Algorithm 1, the training process is iterative to add a new tree, which can fix prior tree mistakes and residuals. After that, this process combines the previous trees to generate the final prediction. The prediction value is shown in Equation (2).$$Y{p}_{T}\left(X\right)=Y{p}_{T-1}\left(X\right)+\alpha \ast {f}_{T}(X,{w}_{T})$$Based on the most significant gain loss, the model selects a leaf node; meanwhile, the model continuously measures the node loss during the training process. The model adds a tree each time by learning a new function ${f}_{t}(X,{w}_{t})$ to fit the residual of the last prediction. After training, the T tree is obtained, which contains the corresponding leaf node with the corresponding score. Finally, by adding the related scores of each tree, the predicted value is calculated. In order to avoid over-fitting issues, XGBoost needs to find the optimal solution to balance the decline of complexity and the object function. Hence, XGBoost takes the Taylor expansion of the loss function up to the second order and adds a regularization term. Therefore, the XGBoost model prediction is shown in Equation (3).$$Y{p}_{t}=\sum _{t=1}^{T}{f}_{t}\left({X}_{t}\right)$$The training objective function of XGBoost includes two parts, which are the training error and regularization as shown in Equation (4).$$X{p}_{t}=\sum _{i=1}^{n}L({Y}_{i},Y{p}_{i})+\sum _{t=1}^{T}re\left({f}_{t}\right)$$Taylor expansion on training object: in the above step t-th object function, the previous $t-1$ prediction function y head can be considered as a variable t-th weak learner, and ${f}_{t}\left(X\right)$ is the delta change; so, XGBoost uses a second order Taylor expression to approximate the step t-th object function:$$X{p}_{t}=\sum _{i=1}^{N}\left(L({Y}_{i},Y{p}_{t-1}+{g}_{i}{f}_{t}\left({X}_{i}\right)+\frac{1}{2}{h}_{i}{f}_{t}^{2}\left({X}_{i}\right)\right)+\sum _{i=1}^{t}re\left({f}_{i}\right)$$$${g}_{i}=\partial {Yp}_{t-1}L(Yi,Y{p}_{t-1})$$$${h}_{i}={\partial}^{2}Y{p}_{t-1}L(Yi,Y{p}_{t-1})$$In the equation above, at the current t-th step, the $t-1$ step prediction y head and all before t regularization are known values; so, they are constant values in the t-th step object function. We remove the constant terms (because they do not impact the object function optimization), and we obtain:$$X{p}_{t}=\sum _{i=1}^{n}\left({g}_{i}{f}_{t}\left({X}_{i}\right)+\frac{1}{2}{h}_{i}{f}_{t}^{2}\left({X}_{i}\right)\right)+re\left({f}_{t}\right)$$The tree mapping function definition is as follows:$${I}_{j}=\left\{i\right|q\left({X}_{i}\right)=j\}$$$${f}_{t}\left(X\right)={w}_{q}\left(X\right)$$Next, we rewrite the object function with regularization:$$X\left(t\right)=\sum _{i=1}^{n}\left({g}_{i}{f}_{t}\left({X}_{i}\right)+\frac{1}{2}{h}_{i}{f}_{t}^{2}\left({X}_{i}\right)\right)+\gamma T+\frac{1}{2}\lambda \sum _{j=1}^{T}{w}_{j}^{2}$$$$=\sum _{j=1}^{T}\left(\left(\sum _{i\in {I}_{j}}{g}_{i}\right){w}_{j}+\frac{1}{2}(\sum _{i\in {I}_{j}}{h}_{i}+\lambda ){w}_{j}^{2}\right)+\gamma T$$The optimized object function is as follows: Now, the t-th step object function is a function of ${w}_{i}$, ${g}_{i}$, and ${h}_{i}$, whose values are known, because they related to the loss function and step $t-1$ prediction values. So, we can use the following equation to obtain the best ${w}_{i}$ to minimize the object function:$${\partial}_{wi}Xt=0$$The optimal w is:$${w}_{j}=-\frac{{\sum}_{i\in {I}_{j}}gi}{{\sum}_{i\in {I}_{j}}{h}_{i}+\lambda}$$Furthermore, the corresponding minimal object value is:$$Xt=-\frac{1}{2}\sum _{j=1}^{T}\frac{{\left({\sum}_{i\in {I}_{j}}{g}_{i}\right)}^{2}}{{\sum}_{i\in {I}_{j}}{h}_{i}+\lambda}+\gamma T$$The slitting criteria for the weak learner is as follows: Firstly, we obtain the t-th step object function. Next, we build the t-th tree. This tree should be constructed to reduce the object function value as much as possible. To build this tree, we only allow a node split and search for the best split, which causes the greatest reduction. Hence, in each split, we measure the objective function value reduced by the tree object function value (After Node Split)-(Before Node Split).$$G=\frac{1}{2}\left(\frac{{\left({\sum}_{i\in {I}_{L}}{g}_{i}\right)}^{2}}{{\sum}_{i\in {I}_{L}}{h}_{i}+\lambda}+\frac{{\left({\sum}_{i\in {I}_{R}}{g}_{i}\right)}^{2}}{{\sum}_{i\in {I}_{R}}{h}_{i}+\lambda}-\frac{{\left({\sum}_{i\in I}{g}_{i}\right)}^{2}}{{\sum}_{i\in I}{h}_{i}+\lambda}\right)-\gamma $$Gain (G) is how many object’s function values are reduced in the split. ${I}_{L}$ is a left splitting child leaf; ${I}_{R}$ is a right splitting leaf; and I is the parent leaf.For simplicity, each leaf can calculate its Similarity Score ($SS$):$$SS=\frac{{\left({\sum}_{i\in I}{g}_{i}\right)}^{2}}{{\sum}_{i\in I}{h}_{i}+\lambda}$$The splitting gain can be expressed as:$$Left\left(SS\right)+Right\left(SS\right)-Parent\left(SS\right)$$Based on the chosen loss function, ${g}_{i}$ and ${h}_{i}$ are 1-order and 2-order derivatives to calculate tree node similarity and tree leaf output ${w}_{i}$. For classification, the loss function is a custom log loss function (as shown in Equation (19).$$L={Y}_{i}log\left({p}_{i}\right)+(1-{Y}_{i})log(1-{p}_{i})$$
- Classification evaluation metrics. Cortés-Leal et al. [42] used a performance metric to mitigate IIoT attacks through measuring the impact of the energy consumption during transmission in IoT and WSN environments. However, we considered other performance metrics, since they suited the ML approach in this work. In particular, we used a confusion matrix (CM) and learning curves to evaluate the performance of our proposed model. A CM contains true positive (TP), true negative (TN), false positive (FP), and false negative (FN). Based on these values of CM, we calculated other performance evaluation metrics, including precision (P), recall (R), and the F1 score.The precision (P) is used to measure the accuracy of the model for classifying a sample as positive.$$P=\frac{TP}{TP+FP}$$The recall (R) is used to measure the ability of the model to detect positive samples.$$R=\frac{TP}{TP+FN}$$The F1 score is the harmonic mean of P and R, which is calculated following Equation (22).$$F1=2\times \frac{P\times R}{P+R}$$The learning curve performance represents the efficiency of the model during training time with instances. The cross-validation score will represent the evaluation performance of the learning curve.

Algorithm 1: XGBoost-based IDS model. |

## 3. Results and Discussion

#### 3.1. Imbalanced Multiclass Classification

#### 3.2. Performance Learning Curve

#### 3.3. Performance Comparison

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Abbreviations

IoT | Internet of Things |

IIoT | Industrial Internet of Things |

IDS | Intrusion Detection System |

ML | Machine Learning |

NIDS | Network Intrusion Detection System |

XGBoost | eXtremely Gradient Boosting |

M2M | Machine-to-Machine |

M2P | Machine-to-People |

H2M | Human-to-Machine |

M2H | Machine-to-Human |

MitM | Man-in-the-Middle |

DL | Deep Learning |

RNN | Recurrent Neural Network |

LSTM | Long Short-Term Memory |

GRU | Gated Recurrent Unit |

U2R | User to Root |

LR | Logistics Regression |

LDA | Linear Discriminant Analysis |

SVM | Support Vector Machine |

NB | Naïve Bayes |

kNN | k-Nearest Neighbors |

GBM | Gradient Boosting Machines |

RF | Random Forest |

NN | Neural Network |

AP2PFL-MLP | Asynchronous Peer-to-Peer Federated Learning-Multilayer Perceptron |

TP2SF | Trustworthy Privacy-Preserving Secured Framework |

WSN | Wireless Sensor Network |

DR | Detection Rate |

FAR | False Alarm Rate |

DDoS | Distributed Denial of Service |

CM | Confusion Matrix |

TP | true positive |

TN | true negative |

FP | false positive |

FN | false negative |

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**Figure 5.**Confusion matrix results of the proposed method on the TON_IoT dataset. (

**a**) IoT Fridge; (

**b**) IoT Garage Door; (

**c**) IoT GPS Tracker; (

**d**) IoT Modbus; (

**e**) IoT Motion Light; (

**f**) IoT Thermostat; and (

**g**) IoT Weather.

**Figure 6.**Learning curve results of the proposed method on the X-IIoTID dataset: (

**a**) class 1 output; (

**b**) class 2 output.

**Figure 7.**Learning curve results of the proposed method on the TON_IoT dataset. (

**a**) IoT Fridge; (

**b**) IoT Garage Door; (

**c**) IoT GPS Tracker; (

**d**) IoT Modbus; (

**e**) IoT Motion Light; (

**f**) IoT Thermostat; and (

**g**) IoT Weather.

Dataset | Features | Limitation |
---|---|---|

N-BaIoT [32] | An IoT environment simulation was set up to collect normal status and botnet attacks. This simulation included some IoT devices such as access points, wifi, wired connection, and a router. A small-scale network-based Wireshark for network traffic collecting aimed to reduce many packets in the high-bandwidth network. | There were no telemetry data from IoT sensors and data traces of operating systems. |

UNSW-NB15 [33] | The dataset was created in a network traffic simulation using an IXIA traffic generator and was saved in four CSV files. This dataset contained a normal vector and nine attack vectors. Each vector had 47 features and the class target feature. | The dataset did not contain security events against operating systems and IoT networks. |

Bot-IoT [34] | Large-scale raw packets from different virtual machines were collected. This dataset contained malware events and various botnet attacks with various data features. | The dataset did not contain hacking vectors against traces of operating systems and IoT systems. |

Device | Target Value | Number of Samples |
---|---|---|

IoT_Fridge | normal | 35,000 |

ddos | 5000 | |

injection | 5000 | |

backdoor | 5000 | |

password | 5000 | |

ransomware | 2902 | |

xss | 2042 | |

IoT_Garage_Door | normal | 35,000 |

ddos | 5000 | |

password | 5000 | |

backdoor | 5000 | |

injection | 5000 | |

ransomware | 2902 | |

xss | 1156 | |

scanning | 529 | |

IoT_GPS_Tracker | normal | 35,000 |

password | 5000 | |

backdoor | 5000 | |

injection | 5000 | |

ddos | 5000 | |

ransomware | 2833 | |

xss | 577 | |

scanning | 550 | |

IoT_Modbus | normal | 35,000 |

injection | 5000 | |

backdoor | 5000 | |

password | 5000 | |

xss | 577 | |

scanning | 529 | |

IoT_Motion_Light | normal | 35,000 |

ddos | 5000 | |

password | 5000 | |

injection | 5000 | |

backdoor | 5000 | |

ransomware | 2264 | |

scanning | 1775 | |

xss | 449 | |

IoT_Thermostat | normal | 35,000 |

password | 5000 | |

injection | 5000 | |

backdoor | 5000 | |

ransomware | 2264 | |

xss | 449 | |

scanning | 61 | |

IoT_Weather | normal | 35,000 |

password | 5000 | |

backdoor | 5000 | |

ddos | 5000 | |

injection | 5000 | |

ransomware | 2865 | |

xss | 866 | |

scanning | 529 |

Output | Target Value | Number of Samples |
---|---|---|

Class 1 | Normal | 421,417 |

RDOS | 141,261 | |

Scanning_vulnerability | 52,852 | |

Generic_scanning | 50,277 | |

BruteForce | 47,241 | |

MQTT_cloud_broker_subscription | 23,524 | |

Discovering_resources | 23,148 | |

Exfiltration | 22,134 | |

insider_malcious | 17,447 | |

Modbus_register_reading | 5953 | |

False_data_injection | 5094 | |

C&C | 2863 | |

Dictionary | 2572 | |

TCP Relay | 2119 | |

fuzzing | 1313 | |

Reverse_shell | 1016 | |

crypto-ransomware | 154 | |

MitM | 117 | |

Class 2 | Normal | 421,417 |

RDOS | 141,261 | |

Reconnaissance | 127,590 | |

Weaponization | 67,260 | |

Lateral_movement | 31,596 | |

Exfiltration | 22,134 | |

Tampering | 5094 | |

C&C | 2863 | |

Exploitation | 1133 | |

crypto-ransomware | 154 |

Dataset | Output class/Device | Precision | Recall | F1 |
---|---|---|---|---|

X-IIoTID | Class 1 | 1.0 | 0.9975 | 0.999 |

Class 2 | 1.0 | 0.998 | 0.999 | |

TON_IoT | IoT_Fridge | 0.9995 | 0.999 | 0.9993 |

IoT_Garage_Door | 0.9995 | 1.0 | 0.9995 | |

IoT_GPS_Tracker | 1.0 | 1.0 | 1.0 | |

IoT_Modbus | 1.0 | 1.0 | 1.0 | |

IoT_Motion_Light | 0.999 | 0.9953 | 0.9984 | |

IoT_Thermostat | 0.995 | 0.9957 | 0.996 | |

IoT_Weather | 0.995 | 1.0 | 0.9975 |

**Table 5.**Comparison of multiclass classification by the F1 score of the proposed method with other methods on the X-IIoTID dataset.

Attack Type | DPM–DDM [27] | DT [28] | Proposed Method (XGBoost) |
---|---|---|---|

crypto-ransomware | 100 | 99.86 | 100 |

Exploitation | 64.66 | 98.52 | 100 |

C&C | 99.27 | 89.66 | 100 |

Tampering | 99.72 | 99.47 | 100 |

Exfiltration | 99.98 | 89.76 | 100 |

Lateral_movement | 91.48 | 98.52 | 100 |

Weaponization | 99.76 | 99.97 | 100 |

Reconnaissance | 93 | 99.22 | 99.8 |

RDOS | 99.94 | 99.99 | 100 |

Normal | - | - | 100 |

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## Share and Cite

**MDPI and ACS Style**

Le, T.-T.-H.; Oktian, Y.E.; Kim, H.
XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems. *Sustainability* **2022**, *14*, 8707.
https://doi.org/10.3390/su14148707

**AMA Style**

Le T-T-H, Oktian YE, Kim H.
XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems. *Sustainability*. 2022; 14(14):8707.
https://doi.org/10.3390/su14148707

**Chicago/Turabian Style**

Le, Thi-Thu-Huong, Yustus Eko Oktian, and Howon Kim.
2022. "XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems" *Sustainability* 14, no. 14: 8707.
https://doi.org/10.3390/su14148707