Ensemble based Effective Intrusion Detection System for Cloud Environment over UNSW-NB15 Dataset

Amin, Uzma; Ahanger, Aamir S; Masoodi, Faheem; Bamhdi, Alwi M.

doi:10.52458/978-93-91842-08-6-46

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…In [29], the authors suggested an intrusion detection system based on machine learning models to detect attacks in IoT networks. They evaluated their model based on data captured by IoT sensors using Node MCU ESP8266, DHT 11 sensor, and wireless router.…”

Section: Ensemble Machine Learning Classificationmentioning

confidence: 99%

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

2023

IJIES

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The internet of things (IoT) has gained great importance due to its applicability in various daily life applications and its flexible and scalable framework. The wide and spreading use of IoT in the last few years has attracted intruders, who were able to take advantage of the vulnerabilities of any IoT framework due to the absence of robust security protocols. This discourages current and probable investors. Out-of-date intrusion detection models are mainly developed to support information technology systems using built-in, predefined patterns or highly imbalanced datasets. Over the past decade, deep learning models have outperformed traditional machine learning models in attack detection tasks. The biggest challenge in detecting zero-day attacks is determining the best deep-learning classifier. Numerous research initiatives have combined ensemble learning to improve performance, avoid overfitting, and minimize errors. In this work, to address this research gap, we propose a new enhanced meta-learning ensemble deep learning model based on stacking that combines the baseline deep learning models using two tiers of meta-classifiers. Then, conducting several experiments on two recent huge-size different IoT benchmark datasets to evaluate the performance of the proposed model. Different baseline deep learning classifiers are trained for each dataset, and their performance is compared to the proposed ensemble model. The findings show that the proposed ensemble model significantly enhances the classification accuracy of baseline deep-learning models.

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Section: Ensemble Machine Learning Classificationmentioning

confidence: 99%

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

2023

IJIES

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“…The rules are processed with the occurrence of events; otherwise, they are classified as intrusion. 11 Due to the exponential advancement of technology and the increasing number of hacking tools, the capacity for adaptation is regarded as a critical attribute. In order to effectively address a greater number of network intrusions, it is necessary to implement either a semi-automatic or automated detection phase.…”

Section: Introductionmentioning

confidence: 99%

“…The knowledge base contains predefined rules for rule‐based anomaly identification. The rules are processed with the occurrence of events; otherwise, they are classified as intrusion 11 . Due to the exponential advancement of technology and the increasing number of hacking tools, the capacity for adaptation is regarded as a critical attribute.…”

Section: Introductionmentioning

confidence: 99%

Hybrid one‐dimensional residual autoencoder and ensemble of gradient boosting for cloud IDS

Chauhan,

Dwivedi

2024

Concurrency and Computation

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SummaryThe distributed and decentralized architecture of cloud computing is important for a number of industries, including business, government, entertainment, education, and information technology. It facilitates a wide aspect of information technology, where the computing model is vulnerable to attacks or intrusion. For detecting malicious activities, a novel intrusion detection system (IDS) is required to be developed. In this paper, an enhanced synthetic minority oversampling technique (SMOTE) with a hybrid one‐dimensional residual auto encoder and the Ensemble of Gradient Boosting (1D‐RAE‐EGB) models are proposed for cloud intrusion detection. The proposed IDS resolves the class imbalance problem through enhanced SMOTE. In addition, noise reduction is accomplished with ID‐RAE, which minimizes the data dimension. Finally, the soft voting‐based ensemble classification model is used for attack detection. The ensemble gradient boosting model comprises categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The ensemble model is fine‐tuned by reducing the number of parameters under fitting conditions since it is not compulsory to re‐adjust the weight values in a backpropagation process. The proposed IDS is implemented in Python using the NSL‐KDD dataset. The accuracy, precision, recall, f1‐score, false positive rate (FPR), false negative rate (FNR), specificity, and Kappa measure obtained for the proposed IDS are 99.98%, 99.3%, 98.5%, 99.95%, 0.723, 0.46, 99.98%, and 99.97%, respectively.

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An Effective Intrusion Detection System in Cloud Computing Environment

Dixit

Hussain

2023

Lecture Notes in Networks and Systems

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Ensemble based Effective Intrusion Detection System for Cloud Environment over UNSW-NB15 Dataset

Cited by 5 publications

References 20 publications

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

Hybrid one‐dimensional residual autoencoder and ensemble of gradient boosting for cloud IDS

An Effective Intrusion Detection System in Cloud Computing Environment

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