Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Alin, François; Chemchem, Amine; Nolot, Florent; Flauzac, Olivier; Krajecki, Michaël

doi:10.1007/978-3-030-45778-5_2

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…A bi-level hierarchical classification methodology using ML has been proposed in [13] to identify the different types of secondary tasks drivers are engaged in using their driving behavior parameters. The authors in [14] indicated that splitting the classification task into sub-classification tasks can improve the accuracy rate on some non-BGP benchmark cyberattack datasets. Currently, ML-based self-maintenance solutions for SICNs are not available.…”

Section: A Employing ML Methodsmentioning

confidence: 99%

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

2021

IEEE Commun. Mag.

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Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world. However, an indispensable part for CNs is network management, where responsive and autonomous maintenance is much needed. With the technological advancement in telecommunications networks, a classical satellite-dependent CN is envisioned to be transformed into a satellite-integrated CN (SICN), which will embrace significant autonomy, intelligence, and scalability in network management. This article discusses the machine-learning (ML) based hierarchical approach to enabling autonomous selfmaintenance for SICNs. The approach is split into the anomaly identification and anomaly mitigation phases, where the related ML methods, data collection means, deployment options, and mitigation schemes are presented. With the case study, we discuss a typical scenario using satellite and fixed connections as backhaul options and show the effectiveness and performance improvements of the proposed approach with recurrent neural network and ensemble methods.

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Section: A Employing ML Methodsmentioning

confidence: 99%

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

2021

IEEE Commun. Mag.

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“…Through training, this model effectively differentiated complex attacks and accurately analyzed unknown web-based attacks.F. Alin et al [36] proposed a hierarchical deep learning approach for intrusion detection. Henry et al [37] The author proposes a technique that combines CNN and GRU, in which different CNN-GRU combination sequences are proposed to optimize network parameters.…”

Section: Intrusion Detection Modelmentioning

confidence: 99%

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Jiang,

Fu,

Wang

2024

IEEE Access

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In today's network environments vulnerable to cyber threats like hackers and viruses, intrusion detection technology is considered the most effective means of detection and defense. Deep neural networks are commonly used in intrusion detection technology. However, improving the model's ability to extract feature information and reducing computational space while retaining local feature information are critical challenges that need to be addressed.To tackle these issues, this paper proposes a model named BBO-CFAT, which combines the Biogeography-Based Optimization algorithm (BBO) for feature selection and an improved Transformer model for preserving context information and reducing computational space. Specifically, the BBO-CFAT model employs a roulette selection method to control the operations of migration and mutation operators. It utilizes feature information entropy to weight updates of adaptive variables in these operators, thereby enhancing the credibility of feature selection. Furthermore, the Transformer framework is hierarchically designed to facilitate the acquisition of context information. Additionally, depthwise separable convolutions are employed to reduce computational space, thereby improving computational efficiency and training speed.Experimental evaluations using the CIC-IDS2017 and NSL-KDD datasets demonstrate promising accuracies for BBO-CFAT on both datasets, achieving 99.1% and 97.5% accuracy, respectively, surpassing the performance of comparative experiments.Overall, the BBO-CFAT model provides a comprehensive solution to the challenges of intrusion detection, effectively balancing feature preservation, computational efficiency, and training accuracy.

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“…In the CICIDS2017 dataset, there are 15 classes including the benign class and among the recent machine-learning based solutions evaluated on this dataset, the number of classes has at least eight different values. In [16,21,22,28], all 15 classes are used as the classifier outputs.…”

Section: Classifier Outputmentioning

confidence: 99%

Performance analysis and feature selection for network-based intrusion detection with deep learning

2021

Turk J Elec Eng & Comp Sci

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An Intrusion Detection System is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are assessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a certain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set of size 9; while the average time required to classify a test sample is halved compared to the complete set.

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Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Cited by 8 publications

References 23 publications

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Performance analysis and feature selection for network-based intrusion detection with deep learning

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