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A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

Md. Alamin Talukder, Khondokar Fida Hasan, Md. Manowarul Islam, Md Ashraf Uddin, Arnisha Akhter, Mohammad Abu Yousuf, Fares Alharbi, Mohammad Ali Moni

arXiv:2212.04546v2cs.CRcs.LG

TL;DR

Network intrusion detection must handle evolving attacks, large datasets, and questions about accuracy and dependability. The paper proposes a hybrid ML/DL pipeline using SMOTE for balancing and XGBoost for feature selection, achieving 99.99% accuracy on KDDCUP’99 and 100% on CIC-MalMem-2022. The authors also report dependability and real-time implementation scope, while identifying emerging threats and class imbalance in new data as boundaries for future evaluation.

  • Problem

    Current intrusion-detection models must address evolving threats, large datasets, and the accuracy and dependability requirements of contemporary networks.

  • Method

    The paper develops a hybrid machine-learning approach combining data balancing with feature selection and dimensionality reduction.

  • Results

    99.99% accuracy was achieved on KDDCUP’99 and 100% on CIC-MalMem-2022.

  • Takeaways & Limitations

    The authors conclude that the hybrid pipeline can provide superior detection with model availability and support real-time implementation on internet-connected IDS devices.

  • Takeaways & Limitations

    Future evaluation will examine emerging threats, and performance may decrease when new data is tested under class imbalance.

Abstract

from arXiv · show

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.

1. Introduction

The introduction frames anomaly-based network intrusion detection as necessary for evolving threats but difficult to make accurate, dependable, and scalable. It presents a hybrid ML/DL approach using SMOTE, XGBoost, and classifier evaluation to address these challenges.

  • Detection challenges: Anomaly-based detectors can identify unknown malware by comparing incoming packets with prior model knowledge.Signature-based systems depend on predefined patterns and may miss new, unpredictable attacks.
  • Detection challenges: Machine-learning intrusion detection can improve detection and generalization but often requires substantial training data and may produce more false positives.These implementation challenges motivate updating classic ML-based IDS for changing security needs.
  • Detection challenges: Large and excessive datasets increase dimensional complexity, hindering accurate classification and reducing model performance and reliability.The introduction links high-dimensional information with poor performance and frequent false positives.
  • Proposed approach: The proposed hybrid approach combines machine learning and deep learning with feature selection, dimensionality reduction, and data balancing.SMOTE addresses class imbalance, while XGBoost selects features to lower dimensionality and computational workload.
  • Proposed approach: SMOTE balances datasets and XGBoost selects features before multiple ML and DL classifiers are evaluated.The evaluated classifiers include RF, DT, KNN, MLP, CNN, and ANN.
  • Reported performance: Above 99.9% accuracy was reported for all evaluated ML classifiers in the proposed model.The paper also reports lower Type-1 and Type-2 rates than existing models.

2. Related Works

Related work explores feature selection, resampling, hybrid models, and diverse classifiers across intrusion-detection datasets. Reported performance varies substantially by dataset, feature-selection method, classifier, and evaluation setup.

  • Existing approaches: 99.9993% and 99.992% accuracy were reported on IoT-BoT and KDDCup’99, respectively, using 16 and 19 features with JRip.The study compared reduced feature sets against the original feature set and typical IDSs.
  • Reported results: 91.50% accuracy was reported for a DNN evaluated on KDDCUP’99, NSL-KDD, and UNSW-NB15.Other reviewed KDDCUP’99 results included 98.00% with PSO and ANN, and 99.40% with correlation-based selection and DNN.
  • Feature reduction: XGBoost-based feature dropping and SMOTE-enhanced classifiers were among prior strategies for improving intrusion-detection performance.The reviewed literature also describes MapReduce, ensemble, PSO, and mutual-information feature-selection methods.
  • Reported results: 99.10% detection rate, 99.01% accuracy, and 0.01% FAR were reported for an IG-PCA ensemble on ISCX 2012.The same work reported additional accuracy, detection-rate, and FAR values on NSL-KDD and Kyoto 2006+.
  • Feature reduction: Feature-reduction studies reduced attributes from 77 to 24 and from 41 to 12, reporting 99.96% and 99.32% accuracy on CIC-IDS2017 and KDDCUP’99.The reported times were 133.66 seconds for CIC-IDS2017 and 11.22 seconds for KDDCUP’99.

3. Proposed Methodology

The proposed methodology targets data imbalance, dimensionality, detection-error assessment, and model dependability in AI-enabled intrusion detection. It combines preprocessing, XGBoost feature selection, and comparative ML/DL evaluation, selecting 20 features that meet a 99.95% accuracy threshold.

  • Problem Statement: The methodology addresses imbalanced datasets, limited error analysis, dimensionality reduction, and model dependability as three stated AI-enabled intrusion-detection problems.The authors emphasize precision, recall, F1-score, confusion-matrix analysis, computational efficiency, and dependability evaluation.
  • Data Balancing: SMOTE balances minority and majority classes by generating synthetic samples for imbalanced datasets.The approach uses oversampling to address datasets such as KDDCUP’99.
  • Proposed Architecture: The hybrid approach combines preprocessing, SMOTE balancing, XGBoost feature selection, and ML/DL model evaluation to recommend an algorithm for network-intrusion detection.The evaluated algorithms include RF, DT, KNN, MLP, CNN, and ANN.
  • Feature Selection using XGBoost: XGBoost selects optimal features to reduce dimensionality, computational costs, and potentially overfitting through embedded feature selection and regularization.The method uses feature importance from decision-tree ensembles, while regularization controls model complexity.
  • Feature Selection using XGBoost: After 20 feature selections, all evaluated machine-learning algorithms satisfy the 99.95% accuracy threshold.The first 20 features were therefore nominated as the candidate feature set for the proposed work.

4. Model Implementation and Evaluation

The study implements a hybrid intrusion-detection approach using SMOTE for data balancing, XGBoost for feature selection, and multiple ML and DL algorithms for model selection. It validates the approach through experiments on multiple datasets.

  • The hybrid approach uses SMOTE for data balancing and XGBoost for feature selection.
  • Several machine-learning and deep-learning algorithms are analyzed to select the best model for network intrusion detection.
  • The approach is validated and assessed through extensive experiments on various datasets.
  • The implementation process includes dataset description, data preparation, and model training.

4.1. Dataset Description

The evaluation uses KDDCUP’99 and CIC-MalMem-2022, which represent network intrusions and obfuscated-malware memory analysis, respectively. The datasets support binary and multilabel attack-classification settings and include documented feature distributions.

  • The study tests several ML and DL algorithms on the KDDCUP’99 and CIC-MalMem-2022 datasets.
  • The KDDCUP’99 dataset is a benchmark intrusion-detection dataset containing auditable network-connection data from a military network environment.
  • The experiment uses 10% of KDDCUP’99 to reduce experimental time and cost, with 41 input features and 23 attack subcategories.
  • KDDCUP’99 supports binary and multilabel classification, with attack categories and subcategories represented across 44 attributes.
  • CIC-MalMem-2022 is a memory-based dataset for evaluating obfuscated-malware identification algorithms and includes spyware, ransomware, and Trojan Horse malware.
  • CIC-MalMem-2022 contains 58,596 entries split evenly between 29,298 benign and 29,298 harmful memory dumps.

4.2. Data Preparation

Data preparation handles missing and invalid records, scales numerical features through standardization, and converts categorical labels into numerical values before model training.

  • Preprocessing handles missing values, scales features, and selects a specified number of features before ML and DL training.
  • Rows containing null, infinite, or duplicate entries are removed to address corrupted or improperly recorded data.
  • Standardization normalizes each attribute by subtracting its mean and dividing by its standard deviation.
  • The paper motivates scaling because differing measurement units or feature ranges can reduce ML and DL model accuracy.
  • Label encoding converts categorical features into numerical values for entry into the model-training process.
  • The encoding procedures for binary and multilabel KDDCUP’99 classification are presented in Tables 6 and 7.

4.3. Training Process

The models are implemented in Python using a notebook-based environment and evaluated with classification, error, and ROC-based performance measures.

  • Training is performed on an HP 250 G5 notebook running Windows 10 Pro 64-bit with an Intel i3-6006U CPU and 8GB RAM.
  • The implementation uses Jupyter Notebook 6.4.6 and Python 3.8.5.
  • Pandas and NumPy support data cleaning, extraction, and feature selection, while Matplotlib and Seaborn support visualization.
  • Performance is evaluated using accuracy, precision, recall, F1-score, RMSE, and ROC curves.

5. Result Analysis

The evaluation uses 10-fold cross-validation and compares feature configurations and ML/DL algorithms across binary and multilabel intrusion-detection tasks. Random Forest generally performs best, while the proposed feature-processing approach achieves very high accuracy and low error rates on both datasets.

  • Evaluation Setup: The analysis compares all, selected, and proposed feature sets using multiple performance metrics, including accuracy, precision, recall, F1-score, AUC, ROC, MAE, MSE, and RMSE.The proposed feature set is reported to outperform the other two feature configurations.
  • Evaluation Setup: 10-fold cross-validation uses 80% of each dataset for training and 20% for testing, rotating the held-out fold across experiments.The data are divided into 10 folds, with a different fold tested each time.
  • KDDCUP’99 Binary Classification: 99.99%, 99.98%, 99.98%, and 99.95% accuracy are reported for RF, DT, KNN, and MLP, respectively, in KDDCUP’99 binary classification.The reported increases and error values vary across all-feature and selected-feature configurations.
  • KDDCUP’99 Binary Classification: RF achieves 99.99% accuracy and a 1.18% RMSE error rate, while ANN achieves 99.93% accuracy and a 2.68% RMSE error rate in one comparison.The confusion matrix also reports higher TP and TN rates with very low FP and FN rates for RF using 20 features.
  • Algorithm Comparison: RF outperforms the other ML and DL algorithms for both binary and multilabel network-intrusion classification.This conclusion follows the comparative analysis across the tested algorithms.
  • CIC-MalMem-2022: 100% AUC is reported for RF, DT, KNN, and MLP on CIC-MalMem-2022, while RF, DT, MLP, and ANN achieve 100% accuracy with 0% RMSE error.KNN records 99.97% accuracy and a 1.85% RMSE error rate in the same comparison.
  • Discussion: The proposed model is reported to outperform existing work on both binary and multilabel classification and to provide higher detection rates for malware memory attacks.The framework is also described as compatible with contemporary NIDS network topologies and dynamic systems.

6. Conclusion

The study develops a dependable hybrid intrusion-detection approach combining preprocessing, feature selection, and machine/deep-learning evaluation. The selected RF model achieved 99.99% accuracy on KDDCUP’99 and 100% on CIC-MalMem-2022, while SMOTE addressed class imbalance and reduced Type-1 and Type-2 errors.

  • 99.99% accuracy on KDDCUP’99 and 100% on CIC-MalMem-2022 were achieved by RF using the chosen features.RF had the highest accuracy among the evaluated machine-learning and deep-learning algorithms.
  • Performance comparisons with previous research indicate dependability and robustness over other models.The proposed approach is presented as suitable for real-time implementation on internet-connected IDS devices.
  • The proposed hybrid model integrates data balancing, feature scaling, feature selection, and multiple machine-learning and deep-learning algorithms.SMOTE balances datasets, while XGBoost extracts dominant features for selection.
  • SMOTE reduces the negative effects of class imbalance and Type-1 and Type-2 errors in intrusion classification.The paper links imbalance to models predicting some classes more accurately than others.
  • XGBoost supports feature selection by identifying feature importance and reducing dimensionality to improve model performance.Its described mechanisms include regularization, stochastic gradient boosting, and feature-importance estimation.
  • Future work will examine emerging threats, newer datasets, and ensemble feature-selection methods using feature unions and intersections.The stated goal is to identify important features for use in the deep neural network.
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