Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset

Hammad, Mohamed; Elmedany, Wael; Ismail, Yasser

doi:10.1109/3ict51146.2020.9312002

Cited by 22 publications

(5 citation statements)

References 17 publications

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“…These measures were calculated using the appropriate equations, which were based on sources [28][29][30][31]. (6) where TP is the true positive, TN is the true negative, FP is the false positive, and FN is the false negative.…”

Section: Resultsmentioning

confidence: 99%

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Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

A. Kadheem Hammood,

T. Sadiq

2023

ijci

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The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset.

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Section: Resultsmentioning

confidence: 99%

“…In [6], four algorithms-naive Bayes (NB), RF, J48, and zero-were utilized to categorize cyberattacks on the UNSW-NB15 dataset. Two groups were created using the UNSW-NB15 dataset using K-means and expectation maximization clustering techniques, depending on whether the objective attack is used or regular network traffic only.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

A. Kadheem Hammood,

T. Sadiq

2023

ijci

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“…In the labyrinth of cyber threats, this research stands as a guiding beacon, illuminating a path towards a more secure and resilient digital future. [10]…”

Section: Discussionmentioning

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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“…Filter based on feature selection XGBoost [9] is used for detecting network attacks by using machine learning classification techniques with 91% accuracy. Feature selection approach [10] uses correlation techniques as naive bays (NB), Random Forest (RF), J48, and ZeroR. Study of various IDS techniques [11] for identifying attacks using classification algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

Bhuvaneshwari¹,

Venkatachalam²,

Prabu³

2022

Computers, Materials &Amp; Continua

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With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate.

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Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset

Cited by 22 publications

References 17 publications

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

GuardianAI: Smart Intrusion Detection for Modern Threats

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

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