Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection
Abstract
:1. Introduction
- We propose an optimized Intrusion Detection for IoMT Networks with tree-based machine learning on a balanced CICIDS2017 dataset. These algorithms include Decision Tree, Random Forest, XGBoost, and CatBoost.
- We apply filter-based feature selection methods to determine the relative importance of features. These methods are Mutual Information and the XGBoost method named (MI-XGBoost).
- We apply the mathematical set (intersection theory) concept to find the most common features between the chosen one after applying MI-XGBoost. This approach aims to extract the minimal set of the most relevant features.
- We evaluate the performance of our proposed model in terms of accuracy, F1 score, recall, precision, TPR, and FPR. Then, we use k = tenfold cross-validation to validate our model.
- We compare our model with recent related works.
2. Related Works
3. Research Methodology
4. Overview of the Proposed Model
4.1. Dataset Description
4.2. The Flow of the Proposed Model
- Data cleaning and pre-processing: This is an important step for ensuring the accuracy and reliability of the proposed model. The CICIDS2017 dataset is often incomplete or imbalanced, which could affect the performance of the proposed IDS; thus, the dataset must be pre-processed. To do so, the initial step to pre-process the data in our model involves removing null values and removing duplicate records. Additionally, to ensure that all features contribute equally to the model’s training, we apply Max-Min to standardize the feature range. Finally, to deal with an imbalanced dataset, the class weight is used.
- Feature selection: Feature selection is a critical component of our proposed model. This step aims to reduce the computational complexity of the model and enhances its performance by focusing on the relevant features. Thus, we apply MI and XGBoost for feature selection. Then, we reduce the number of selected features by using a mathematical set theory intersection to create a unified subset of relevant features.
- Tree-based machine learning classification: We use well-known tree classifiers such as DT, RF, XGBoost, and CatBoost to classify network data into benign and malicious. These classifiers are chosen for our model based on their robustness in handling complicated and non-linear patterns in network traffic, as well as their efficiency in processing large amounts of data. By exploiting different aspects of the data, each classifier enhances the overall detection capability of the intrusion detection system (IDS) and increases the precision and dependability of our intrusion detection.
4.2.1. Data Cleaning and Pre-Processing
- Data Cleaning:After combining the eight files into a single dataset, various methods were employed on the CICIDS2017 dataset, including data cleansing and the elimination of null values and duplicates.
- Data Pre-processing:As real-world data are often inadequate and mismatched, pre-processing the data is essential. Pre-processing the dataset is crucial for improving its quality by eliminating noise, addressing missing values, standardizing or normalizing features, and readying the data for analysis or model training. Many techniques have been proposed to pre-process data, including scaling. Scaling is a pre-processing technique that involves applying certain scalers to the numerical characteristics of a dataset to optimize it for machine learning algorithms [38]. The failure to utilize scaling for pre-processing the dataset may result in unsatisfactory model performance, skewed predictions, and erroneous interpretations of the model’s behavior. Scaling is a crucial pre-processing step in many machine learning workflows, as it guarantees dependable and precise model training and predictions. Thus, we first divided the dataset into 30% as a testing set and 70% as a training set, then Max-Min was used in the pre-processing step to scale the features of the dataset to a specified range, usually from 0 to 1 [39]. This technique standardizes the range of features to guarantee they are all on the same scale. This avoids larger-scale features from overpowering smaller-scale traits in model training. Additionally, to improve the performance of the model, class weight was used to address class imbalance issues in datasets where one class is significantly more common than the others. This involves assigning higher significance to data from the minority class and lower significance to samples from the majority class during model training. Eventually, Max-Min scaling standardizes the features, whereas class weight considers dataset imbalance. Collectively, they can improve the resilience and adaptability of the machine learning model. The main objective of using balanced data for model training is to increase the classification accuracy of the systems, thus boosting performance [40].
4.2.2. Feature Selection
- Mutual InformationOne of the most useful metrics for assessing the interdependence of variables is mutual information (MI). MI generates a non-negative result, and the two observed variables are statistically independent if the Mutual Information is zero. If it is greater than zero, it indicates a stronger relationship between the two variables [43]. Thus, we used it in our model to determine the most relevant features. Figure 4 presents the top 30 selected features by applying MI.
- XGBoostThe fundamental concept of the XGBoost method is sequentially training a set of tree models, progressively improving their accuracy by including a penalty parameter to manage the model complexity [44]. The XGBoost technique computes a score for each feature of the used dataset. The measure is used to evaluate the importance of each feature. So, we can determine the importance of inputs in the learning and classification processes. Figure 5 demonstrates the top 30 features selected by applying XGBoost to the CICIDS2017 dataset.
4.2.3. Tree-Based ML Classifiers
- Decision TreeThe Decision Tree classifier operates using the divide-and-conquer strategy. This classifier is categorized as non-parametric supervised learning [46]. By employing Decision Tree, it can distinguish between pure and impure subsets of data [47]. The procedure is terminated after the pure subsets have been acquired, or it is repeated.
- Random ForestThe Random Forest classifier was defined by Breiman in 2001. Fernández-Delgado et al. in [48] emphasized that Random Forest is a remarkably efficient classification method for several practical issues. It is comprised of a forest of Decision Trees. This classifier builds Decision Trees by randomly selecting data and making predictions for each tree.
- XGBoostXGBoost is a newer tree classifier that can scale to large-scale data [49]. This technique has gained wide acceptance in various fields, such as cyber security, due to its exceptional effectiveness and high performance [50]. XGBoost enhances accuracy by combining multiple Decision Trees and reducing the processing time [47].
- CatBoostThe CatBoost algorithm is a potent machine learning technique that produces exceptional results in various applications. Even though CatBoost is primarily intended to manage category features, it can also handle continuous or numerical attributes. The CatBoost model is a unique feature integrated into the gradient-boosting Decision Tree technique [32].Overall, Decision Tree classifiers offer a straightforward and comprehensible approach to intrusion detection, allowing analysts to comprehend the decision-making process and identify important factors that affect classification. They provide valuable insights for detecting and dealing with security issues in networks.
5. Environment Tools
6. Evaluation Metrics and Results
6.1. Evaluation Metrics
- Accuracy: This metric measures the model’s capacity to accurately classify benign instances as displayed in Equation (3):
- Recall: Also known as the detection rate or sensitivity, this metric measures the model’s ability to recognize attacks as displayed in Equation (4):
- Precision: This metric refers to the model’s capacity to produce accurate predictions, specifically, the number of correctly detected positive predictions (attacks), as displayed in Equation (5):
- F1-measure: This metric effectively addresses the trade-off between recall and precision by balancing them over all instances. This is demonstrated in Equation (6):
- False positive rate (FPR): This metric measures the percentage of assault cases classified as normal by the model. This is demonstrated in Equation (7):
- True positive rate (TPR): This metric represents the probability that a true positive will be correctly identified as positive. This is demonstrated in Equation (8):
- Receiver operating characteristics area under the curve (ROC AUC): This metric works as an indicator of the efficiency of a test across different threshold configurations. A higher level indicates a test that is more useful, with values ranging from 0.0% to 100%. A high ROC value indicates a highly successful classification model. In addition, the evaluation of a model includes the consideration of specificity (TPR) and sensitivity (also known as the true negative rate or TNR), where sensitivity is comparable to recall. This is demonstrated in Equation (9):
6.2. Experiment Results
7. A Comparison of the Proposed Model with Other State-of-the-Art Methods
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Day Activity | Traffic Type |
---|---|
Monday | Benign |
Tuesday | Benign, FTP-Patator, SSH-Patator |
Wednesday | Benign, DoS GoldenEye, DoS Hulk, Dos Slowhttptest, Dos Slowlories, Heartbleed |
Thursday (Morning) | Benign, Web Attack XSS, Web Attack Brute Force, Web Attack Sql Injection |
Thursday (Afternoon) | Benign, Infilration |
Friday (Morning) | Benign, Botnet. |
Friday (Afternoon) | Benign, PortScan |
Friday (Afternoon) | Benign, DDos |
Model | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
DT | 0.9987 | 0.9948 | 0.9980 | 0.9964 |
RF | 0.9989 | 0.9954 | 0.9981 | 0.9967 |
XGBoost | 0.9987 | 0.9957 | 0.9970 | 0.9963 |
CatBoost | 0.9969 | 0.9829 | 0.9990 | 0.9909 |
Model | Accuracy % | ROC | DR % | FAR |
---|---|---|---|---|
DT | 98.12 | 0.9505 | 98.74 | 0.049 |
RF | 98.14 | 0.960 | 98.55 | 0.039 |
XGBoost | 98.63 | 0.992 | 98.77 | 0.0205 |
CatBoost | 98.79 | 0.9794 | 98.70 | 0.007 |
Study | Year | Dataset | Balanced | FS Methods | Num of FS | ML-Classifier | Accuracy | IoMT | Cross-Validation |
---|---|---|---|---|---|---|---|---|---|
[26] | 2022 | CICIDS2017 | × | (CFS–FPA) | 30 | SVM, RF, NB and KNN | 99.7 | × | ✓ |
[52] | 2022 | CICIDS2017 NSL-KDD | × | RFE (best FST) | 8 | DT, NB, and KNN | 99.98 | × | ✓ |
[51] | 2024 | CICIDS2017 | ✓ | ANOVA | 10 | SVC, DT, RF, GNB, AdaBoost, XGBoost and LR | 97.23 | × | × |
[53] | 2024 | CICIDS2017 | - | - | - | RF and DT | 98.12 | × | ✓ |
Our work | 2024 | CICIDS2017 | ✓ | MI-XGboost | 15 | DT, RF, XGboost, and Catboost | 98.79 | ✓ | ✓ |
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Balhareth, G.; Ilyas, M. Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection. Sensors 2024, 24, 5712. https://doi.org/10.3390/s24175712
Balhareth G, Ilyas M. Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection. Sensors. 2024; 24(17):5712. https://doi.org/10.3390/s24175712
Chicago/Turabian StyleBalhareth, Ghaida, and Mohammad Ilyas. 2024. "Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection" Sensors 24, no. 17: 5712. https://doi.org/10.3390/s24175712