Implementation of the Decision Tree Method for Detecting Attacks in Networks

Nissi Sari Margaretha Angelina Sitanggang; Octara Pribadi; Herman

doi:10.59934/jaiea.v5i1.1272

Authors

Nissi Sari Margaretha Angelina Sitanggang STMIK TIME
Octara Pribadi STMIK TIME
Herman STMIK TIME

DOI:

https://doi.org/10.59934/jaiea.v5i1.1272

Keywords:

Accuracy, Network Security, CICIDS 2017, Decision Tree, DDoS

Abstract

The rapid development of internet technology has led to an increase in cybercrime, including attacks on computer networks such as Distributed Denial of Service (DDoS). These attacks can cause serious disruption to systems and steal important data. Therefore, early detection of attacks is very important to maintain network security. One effective method for detecting anomalies in networks is the Decision Tree algorithm, due to its ability to classify data quickly and easily. This study aims to develop a Decision Tree-based attack detection model using the CICIDS 2017 dataset. The results of the study show that this algorithm is capable of classifying attack data with a high degree of accuracy and is reliable in handling large-scale data. Thus, the resulting model can help improve the security and performance of computer networks

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