Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

Hossain, Md. Alamgir; Islam, Md. Saiful

doi:10.1016/j.array.2023.100306

Cited by 41 publications

(16 citation statements)

References 33 publications

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“…The research presented in [23] has discussed the development of an IDS using ensemble-based machine learning techniques, with a focus on the RF ensemble method. The research has addressed the limitations of traditional IDSs and has presented a novel approach using various ensemble strategies, including RF, Adaboost, Gradient Boosting, and Gradient XGBoost.…”

Section: Related Workmentioning

confidence: 99%

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A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Chelloug

2024

CMC

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Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on new attacks. This has led to the utilization of deep learning techniques, which have showcased significant potential for processing large datasets and therefore improving detection accuracy. For that reason, this paper focuses on the role of stacking deep learning models, including convolution neural network (CNN) and deep neural network (DNN) for improving the intrusion detection rate of the NSL-KDD dataset. Each base model is trained on the NSL-KDD dataset to extract significant features. Once the base models have been trained, the stacking process proceeds to the second stage, where a simple meta-model has been trained on the predictions generated from the proposed base models. The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate. Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection. The performance of the ensemble of base models, combined with the meta-model, exceeds the performance of individual models. Our stacking model has attained an accuracy of 99% and an average F1-score of 93% for the multi-classification scenario. Besides, the training time of the proposed ensemble model is lower than the training time of benchmark techniques, demonstrating its efficiency and robustness.

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Section: Related Workmentioning

confidence: 99%

“…The research has addressed the limitations of traditional IDSs and has presented a novel approach using various ensemble strategies, including RF, Adaboost, Gradient Boosting, and Gradient XGBoost. The system described in [23] was tested on multiple public datasets and consistently demonstrated accuracy exceeding 99%.…”

Section: Related Workmentioning

confidence: 99%

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Chelloug

2024

CMC

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“…Dalam upaya untuk melindungi sistem pembelajaran elektronik dari ancaman intrusi siber, penggunaan teknologi kecerdasan buatan, khususnya machine learning, telah menjadi fokus perhatian yang signifikan (Hossain, 2023). Teknik machine learning telah terbukti sebagai alat yang efektif dalam mendeteksi pola dan perilaku yang mencurigakan pada data jaringan, yang merupakan ciri khas dari serangan siber (Kanimozhi, 2020).…”

Section: Latar Belakangunclassified

Deteksi Intrusi Siber pada Sistem Pembelajaran Elektronik berbasis Machine Learning

Amirah,

Sanmorino

2023

J. Ilm. Inform. Glob.

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This study aims to develop a mechanism for detecting machine learning-based cyber intrusions in electronic learning systems. In today's digital era, e-learning systems have become an integral part of education and training, providing global accessibility and more interactive learning efficiency. However, security and privacy challenges are becoming critical issues due to the increasingly real threat of cyber intrusion. Attackers try to take advantage of vulnerabilities and weaknesses in e-learning systems to steal sensitive data or disrupt operations. To overcome this problem, this study focuses on the use of artificial intelligence technologies, especially machine learning, to proactively detect and respond to intrusive threats. Through e-learning security analysis, identification of weaknesses, and potential loopholes for cyber-attacks, the most suitable machine learning algorithms are selected to detect patterns and signs of intrusion attacks on network data. The evaluation results show that several machine learning algorithms, such as SVM and Decision Tree, have good performance in recognizing cyber intrusions with high accuracy, precision, recall, F1-score, and ROC-AUC. By implementing machine learning-based intrusion detection technology, it is expected that electronic learning systems can be more proactive in identifying and responding to intrusion threats before significant damage occurs. This research has significant benefits in increasing security and privacy in the use of electronic learning systems. In addition, this study is expected to be a reference for further research in the world of cyber security and the application of artificial intelligence technology in supporting digital security.

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“…As a result, the confidentiality and integrity of our sensitive data has become vulnerable to series forms of intrusions, leaks and potential threat of unauthorized access by malicious individuals [2]. Moreover, the advancement in today's technologies and the hackers' determination to continuously enhance these penetrations and cyber-attacks have given rise to novel and unexpected intrusions which are increasingly sophisticated [3,4]. Therefore, enhancing network security and defending it from such complex intrusions and hacking attempts has been a growing trend among network researchers in recent years [5][6][7], making intrusion detection an important research field that requires in-depth investigation.…”

Section: Introductionmentioning

confidence: 99%

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

Shebl,

Elsedimy,

Ismail

et al. 2024

Preprint

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Network security has become imperative in the context of our interconnected networks and everyday communications. Recently, many deep learning models have been proposed to tackle the problem of predicting intrusions and malicious activities in interconnected systems. However, they solely focus on binary classification and lack reporting on individual class performance in case of multi-class classification. Therefore, the need for an efficient and accurate network intrusion detection system has reached a pivotal point. In this paper, we propose a novel intelligent detection system based on convolutional neural network, namely DCNN. The proposed model can be utilized to analyze and detect attacks and intrusions in intelligent network systems. The DCNN model is applied against two benchmark datasets and compared with state-of-the-art models. Experimental results show that the proposed model improved resilience to intrusions and malicious activities for binary as well as multi-class classification. Furthermore, our DCNN model outperforms similar intrusion detection systems in terms of positive predicted value, true positive rate, F1 measure, and accuracy. The scores obtained for binary and multi-class classifications on the CICIoT2023 dataset are 99.50% and 99.25%, respectively. Additionally, for the CICIDS-2017 dataset, DCNN attains a score of 99.96% for both binary and multi-class classifications.

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Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

Cited by 41 publications

References 33 publications

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Deteksi Intrusi Siber pada Sistem Pembelajaran Elektronik berbasis Machine Learning

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

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