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.