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All Journal International Journal of Informatics and Computation
M. Aziz
Universitas Respati Yogyakarta, Indonesia
Computer Science & IT

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Comparative Approach for Intrusion Detection using CNN M. Aziz; Ahmad W
International Journal of Informatics and Computation Vol. 5 No. 2 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i2.58

Abstract

In 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.
Co-Authors Ahmad W