Design of Host based Intrusion Detection System using Fuzzy Inference Rule

Om, Hari; Gupta, Alok

doi:10.5120/10666-5442

Cited by 4 publications

(1 citation statement)

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“…In the paper [24] author, offers a thorough analysis of current studies on the application of deep learning methods to intrusion detection. The authors talk about numerous deep learning models that have been used in intrusion detection, such as autoencoder, convolutional neural network, and recurrent neural network.…”

Section: Research Articlementioning

confidence: 99%

A Machine Learning-based Approach for Intrusion Detection and Prevention in Computer Networks

Dubey¹

2020

TURCOMAT

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The potential of cyberattacks and network penetration has increased due to modern enterprises' increasing reliance on computer networks. Such attacks are detected and prevented by intrusion detection and prevention systems (IDPS), although conventional rule-based solutions have difficulties identifying unidentified attacks. Due to its capacity to learn from data and spot patterns of assault that conventional methods could miss, machine learning (ML) techniques have been gaining prominence in IDPS. This article provides a thorough analysis of the several ML methods utilized in IDPS, including supervised, unsupervised, and hybrid techniques. Also, a hybrid ML-based IDPS that combines the advantages of several methodologies for better performance is proposed. Furthermore, covered are the difficulties with ML-based IDPS and potential solutions. It is demonstrated how ML-based IDPS may be applied in real-world situations, emphasizing the advantages of applying ML to intrusion detection and prevention. In conclusion, this study offers insights into the most recent methods for ML-based IDPS and their potential to enhance network security.

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Section: Research Articlementioning

confidence: 99%