Robust Detection of Network Intrusion using Tree-based Convolutional Neural Networks

Mishra, Sanket; Dwivedula, Rohit; Kshirsagar, V. N.; Hota, Chittaranjan

doi:10.1145/3430984.3431036

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…The authors at [12] have depict the usage of Binary Grey Wolf Optimization for detection of optimal features from the data. They have proposed a CNN approach named TreeNets.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Overview of Deep Learning Techniques for Network Intrusion Detection Systems

Stevanoski,

Risteski,

Porjazoski

2023

JEEIT

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The rapid advances in the new digital world are producing vast amounts of data. This gives more opportunities in business management, but it can also help in implementing new security techniques. Intrusion detection systems (IDS) are enforcing processes for analyzing network data. This study is reviewing the main Deep Learning approaches for intrusion detection in IT network traffic. In the beginning, the study gives an overview of the various IDS types and their usability in the IT network. Then it presents some of the most used Deep Learning techniques proposed by the research community in recent years. By analyzing various papers on the subject, current achievements, and limitations in developing IDS are detected and presented. The study ends by providing the future reach of the newly proposed Deep Learning techniques in monitoring and detecting malicious activities in network traffic.

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“…The authors at [12] have depict the usage of Binary Grey Wolf Optimization for detection of optimal features from the data. They have proposed a CNN approach named TreeNets.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…The best results are achieved in applications where the output is predicted by analyzing the previous values of the data. In [12] the authors are suggesting a hybrid DL model for IDS by using RNNs. The method is based on LSTM type of RNN and is proposed improved long-time memory tree model.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Overview of Deep Learning Techniques for Network Intrusion Detection Systems

Stevanoski,

Risteski,

Porjazoski

2023

JEEIT

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“…This subsection discusses the intrusion detection schemes such as [162]- [189], which apply CNN in intrusion detection. For example, in [190] network traffic features by using CNN, and then, the required data for detecting intrusions is achieved by supervised learning.…”

Section: F Cnn-based Schemesmentioning

confidence: 99%

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

et al. 2021

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Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.INDEX TERMS Intrusion detection, auto-encoder, recurrent neural network, Boltzmann machine, CNN.

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