Network Based Intrusion Detection Using the UNSW-NB15 Dataset

al., Mefta Souhail et.

doi:10.12785/ijcds/080505

Cited by 54 publications

(12 citation statements)

References 22 publications

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“…Table 9 shows the detection accuracy of different intrusion detection algorithms on the UNSW-NB15 dataset. The detection accuracy of the proposed HC-DTTWSVM algorithm is compared with the decision tree algorithm implemented by the C5.0 algorithm [56], the Naïve Bayes algorithm [56], the SVM algorithm [30] and the Deep SARSA algorithm [49]. It can be seen that the proposed HC-DTTWSVM algorithm can achieve the highest accuracy for Analysis, Dos, and Generic samples.…”

Section: Results On the Unsw-nb15 Datasetmentioning

confidence: 99%

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

et al. 2023

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Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years. The development of effective and efficient intelligent network intrusion detection methods using advanced machine learning algorithms is of great importance for defending against various network intrusions in complex network environments. In this study, a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, named HC-DTTWSVM, is proposed, which can effectively detect different categories of network intrusion. First, the hierarchical clustering algorithm is applied to construct the decision tree for network traffic data, where the bottom-up merging approach is used to maximize the separation of the upper nodes of the decision tree, which reduces the error accumulation in the construction of the decision tree. Then, twin support vector machines are embedded in the constructed decision tree to implement the network intrusion detection model, which can effectively detect the network intrusion category in a top-down manner. The detection performance of the proposed HC-DTTWSVM method is evaluated on NSL-KDD and UNSW-NB15 intrusion detection benchmark datasets. Experimental results show that HC-DTTWSVM can effectively detect different categories of network intrusion and achieves comparable detection performance compared to some of the recently proposed network intrusion detection methods.INDEX TERMS Network intrusion detection, twin support vector machine, hierarchical clustering, decision tree.

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Section: Results On the Unsw-nb15 Datasetmentioning

confidence: 99%

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

et al. 2023

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“…Subsequently, [6] the XGBoost classifier was implemented to enhance system accuracy, particularly in managing imbalanced datasets. The ensemble learning technique of XGBoost demonstrated superior capabilities in discerning between backdoor and normal intrusions.…”

Section: Xgboost Classifiermentioning

confidence: 99%

GuardianAI: Smart Intrusion Detection for Modern Threats

SAIRAJKUMAR

2024

Preprint

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Finding and stopping breaches is now essential in the rapidly evolving world of cybersecurity. This work involves a range of machine learning algorithms for intrusion detection systems (IDS) as well as a detailed examination and use of sophisticated feature engineering techniques. The study enhances intrusion detection's accuracy and efficacy to counteract the ever-changing nature of cyber threats. We handle missing values, encode categorical characteristics, and prepare and balance datasets as part of our strategy. By examining them, one can have a thorough understanding of the benefits and drawbacks of several machine learning models, including Decision Trees, Random Forests, Naive Bayes, and XGBoost, in the context of intrusion detection. Furthermore, we provide a novel approach that combines Recursive Feature Elimination (RFE) with Random Forests to show how well it maintains high classification accuracy while reducing dimensionality. We test the models on a publically available dataset, and we meticulously evaluate the models based on key performance metrics like accuracy, precision, recall, and training time. A comparison analysis of the models makes it clear if they are suitable for real-world application. Our findings indicate that the XGBoost classifier outperforms other models when paired with a well-selected feature set. This demonstrates how well the model detects common intrusions as well as backdoors. In addition, the Random Forest Classifier with RFE modifications exhibits potential as a feature selection technique, offering a balance between accuracy and computational economy. The work adds to the body of knowledge on intrusion detection and provides cybersecurity experts and system developers with valuable information. The strategy being offered offers a reliable and adaptable means of fending off the always changing cyberthreats, thereby bolstering the ongoing effort to fortify digital systems against malicious activity. This work highlights the importance of combining state-of-the-art feature engineering approaches with intricate machine learning algorithms to produce robust and effective cybersecurity defenses, laying a solid foundation for future advancements in intrusion detection.

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“…The recursive elimination method with random forest method was adopted for features selection where many of the classical ML methods were applied on the UNSW-NB15 dataset [35]. The two-stage classification was performed where 74% accuracy was achieved at the first stage with SVM and then its output was given to multiple classifiers and achieved the 86% classification accuracy with 12% improvement.…”

Section: Related Workmentioning

confidence: 99%

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

Alabrah

2023

Applied Sciences

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Network security problems arise these days due to many challenges in cyberspace. The malicious attacks on installed wide networks are rapidly spreading due to their vulnerability. Therefore, the user and system information are at high risk due to network attacks. To protect networks against these attacks, Network Intrusion Detection and Prevention Systems (NIDPS) are installed on them. These NIDPS can detect malicious attacks by monitoring abnormal behavior and patterns in network traffic. These systems were mainly developed using Artificial Intelligence (AI) algorithms. These intelligent NIDPS are also able to detect the attack type while detecting network attacks. Previous studies have proposed many NIDPS for network security. However, many challenges exist so far such as limited available data for training AI algorithms, class imbalance problems, and automated selection of the most important features. These problems need to be solved first, which will lead to the precise detection of network attacks. Therefore, the proposed framework used the highly imbalanced UNSW-NB15 dataset for binary and multiclass classification of network attacks. In this framework, firstly dataset normalization is applied using standard deviation and the mean of feature columns; secondly, an Improved Salp Swarm Algorithm (ISSA) is applied for automated feature selection separately on binary and multiclass subsets. Thirdly, after applying feature selection, the SMOTE–Tomek class balancing method is applied where at least four different ML classifiers are used for binary and multiclass classification. The achieved results outperformed as compared to previous studies and improved the overall performance of NIDPS.

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Network Based Intrusion Detection Using the UNSW-NB15 Dataset

Cited by 54 publications

References 22 publications

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

GuardianAI: Smart Intrusion Detection for Modern Threats

An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method

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