A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance

Ayubkhan, Sheikh Abdul Hameed; Yap, Wun‐She; Morris, Ezra; Rawthar, Mumtaj Begam Kasim

doi:10.1007/s12652-022-04449-w

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…Similarly, authors proposed classification techniques that were tested using the CIC-IDS2017 dataset. The proposed ensemble classifier technique of [ 47 ] achieved 86.5% accuracy, while model presented in [ 66 ] achieved 99.86% accuracy. Other techniques that were proposed in [ 25 , 34 , 48 , 49 ], received, 97.72%, 99.89%, 99.95%, and 98.62%accuracy, while our proposed scheme is 99.98% accurate during the classification.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Farooqi,

Akhtar,

Rahman

et al. 2023

Sensors

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In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

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Section: Discussionmentioning

confidence: 99%

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Farooqi,

Akhtar,

Rahman

et al. 2023

Sensors

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“…Ayubkhan et al 22 Developed LightGBM-DAE classifier for noise removal and feature learning enhancement.…”

Section: Problem Statementmentioning

confidence: 99%

“…Several existing approaches are considered for comparison, including EPK-DNN, 16 IMOPSO, 17 AIDS-HML, 19 PSO+KNN, 20 PSO+ANN, 20 FSACM, 21 LightGBM-DAE, 22 HTF-DS, 24 DHMLM, 25 SGM, 26 and HDL 27 using NADS datasets.…”

Section: Evaluation Based On Nads Datasetsmentioning

confidence: 99%

“…Nonetheless, the classification of a limited number of attack categories or unclear hints in the dataset resulted in slightly lower detection rates, accuracy, and scalability. An ADS that combined a Denoising Auto Encoder (DAE) and Light Gradient‐ Boosting Machine (LightGBM) (LightGBM‐DAE) classifier was developed by Ayubkhan et al 22 The noise and exploitations in the network circulation were removed by the DAE, potentially improving the feature learning capacity for both binary and multi arrangement. Subsequently, the LightGBM classifier was employed to classify the samples, utilizing attribute histogram bins with more gradients to rush the training process and enhance the detective ability of the approach.…”

Section: Reviewmentioning

confidence: 99%

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NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Seth,

Sharma,

Ratmele

2024

Concurrency and Computation

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SummaryDetecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs), as they are susceptible to various attacks that compromise nodes and yield inaccurate outcomes. Conventional attack detection approaches face challenges like high false positives, vulnerability to complex attacks, and limited adaptability to changing attacks due to predefined patterns. Additionally, the computational strain on resource‐constrained nodes hampers network efficiency, demanding innovative and resilient security solutions. Therefore, this research work introduces Novel Hybrid Approaches for Privacy‐preserved Multiple Attacks Detection (NHAPMAD) framework in both network attack detection system and host attack detection system. This topology bolsters the network's defense against various types of attacks, thereby ensuring the preservation of sensitive data privacy. Federated learning has been incorporated into the NHAPMAD framework to protect user privacy in WSNs. The introduced framework provides better anomaly detection and reduces false alarms. Additionally, the introduced model protects sensitive data, ensuring the security and integrity of the entire WSN, promoting a stable and dependable operating environment. The introduced method exhibits superior performance across various metrics like overall accuracy (99.6%), F1‐score (99.59%), recall (99.47%), precision (99.63%), detection rate (99.5%), processing time (0.0009 s), etc. compared to traditional approaches, marking a significant advancement in the realm of WSN security.

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“…In this section, we examine various autoencoder architectures discussed in the literature pertaining to the task of network traffic anomaly detection. We select a set of frequently encountered autoencoder models in the NIDS domain [5], [6], [8], [9]. The notations used in explaining the autoencoderbased techniques are summarized in Table I.…”

Section: Review Of Different Autoencoder Modelsmentioning

confidence: 99%

Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS

Hore,

Nguyen,

et al. 2023

2023 IEEE Conference on Dependable and Secure Computing (DSC)

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Anomaly detection is critical for network security. Unsupervised learning models trained on benign network traffic data aim to detect anomalies without relying on attack data sets. Autoencoder-based models have emerged as a promising approach for detecting anomalies in network intrusion data. While autoencoder models have predominantly been utilized in flow-based approaches, which are suitable for offline analysis, there is a notable gap in research concerning unsupervised learning, particularly autoencoder-based techniques, for packetbased network intrusion detection. Packet-based network intrusion detection systems (NIDS) enable real-time detection at a granular level, making this area of investigation crucial. In this work, we compare autoencoder models for anomaly detection in packet-based NIDS. A methodological framework is presented for implementing an autoencoder-based network intrusion detection mechanism with packet data. A novel reconstruction error metric is proposed for autoencoders, which is evaluated at different threshold levels to compare the detection accuracies of network traffic anomalies. The effectiveness of autoencoder models is demonstrated on various network attacks and adversarial samples obtained from publicly available network intrusion data sets. The analysis highlights the strengths and limitations of different autoencoders for network traffic anomaly detection. The insights obtained from the empirical evaluation offer valuable guidance to researchers and practitioners aiming to develop an autoencoderbased network intrusion detection mechanism.

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A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance

Cited by 8 publications

References 64 publications

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection

Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS

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