Intrusion Detection Using Machine Learning and Deep Learning Techniques

Calisir, Sinan; Atay, Remzi; Pehlıvanoğlu, Meltem Kurt; Duru, N. Jeremi

doi:10.1109/ubmk.2019.8906997

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Intrusion detection is classified into two types based on the behavior -host-based intrusion detection systems (HIDS) detects by monitoring system activity and network-based intrusion detection systems (NIDS) [4]. captures the network activity.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Intrusion Detection System using CNN, ANN and DNN

Jeyavim Sherin,

Parkavi

2023

Advances in Computer Science Research

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The Network Intrusion Detection System (NIDS) is a crucial aspect of safeguarding against cyber threats in the domain of cybersecurity. The major drawback of conservative approaches like Machine Learning (ML) that involve manual feature selection is their dependency on human involvement, which can hinder their efficacy. Deep learning (DL) is one of the technologies widely used in intrusion detection systems, it increases the performance of the model and securing the networks and classify the attacks. The primary concern regarding both convergence and speed along with the uneven values of the input-hidden layer were addressed as a gap in NIDS. This research compares the Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Artificial Neural Network (ANN) are all related to the field of neural networks. The Performance is evaluated using the following metrics like Accuracy, Precision, Recall and true positive rate. For evaluating the effectiveness of the proposed model in both binary and multiclass classifications, the benchmark dataset CSE-CIC-IDS2018 is utilized. As per the experimental findings, the CNN model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.72%.

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Section: Introductionmentioning

confidence: 99%