Comparative Approachfor Intrusion Detection using CNN

Dublin Core

Title

Comparative Approachfor Intrusion Detection using CNN

Subject

ntrusion Detection,CNN,MLAlgorithms,Network Security,Performance Evaluation

Description

n the realm of computer network security, the role of intrusion detection is crucial for safeguarding systems against various threats. However, with the advancement of intrusion techniques, traditional detection methods have demonstrated constraints in recognizing novel attacks. This study tackles the urgent challenge of enhancing intrusion detection by employing Convolutional Neural Networks (CNN) algorithms, contrasting them with different machine learning methodologies like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The primary aim is to assess and compare the effectiveness of these algorithms utilizing an extensive dataset acquired from Kaggle, comprising 25,192 data entries and 42 attributes. Through the assessment of metrics such as accuracy, precision, recall, and F1-score, the findings reveal a nuanced profile of the strengths and weaknesses of each approach. Remarkably, CNN demonstrated remarkable accuracy, prompting further inquiry into its performance

Creator

Muhamad Aziz, Wakhid A

Source

https://ijicom.respati.ac.id/index.php/ijicom/article/view/58/48

Date

December 2023

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Collection

Citation

Muhamad Aziz, Wakhid A, “Comparative Approachfor Intrusion Detection using CNN,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8388.