Clustering K-Means Using SNORT Application For Denial Of Service Attacks


  • Rifki Indra Perwira Universitas Pembangunan Nasional Veteran Yogyakarta
  • Bagus Muhammad Akbar Universitas Pembangunan Nasional Veteran Yogyakarta
  • Hari Prapcoyo Universitas Pembangunan Nasional Veteran Yogyakarta



Clustering, K-Means, Snort, DOS


Data quality and transparency are of the utmost importance for organizations. Collecting original data from the source without any indication of interruption or interception is an indicator of an attack on the server. The most common attack is Denial of Service (DoS), which is a type of pattern that will crash, shutdown, reboot, or not respond to services of a host on the network. One technique for this attack is the use of the k-means clustering method with a snort. This study aims to design a SNORT-Intrusion Detection System (IDS) application with a k-means algorithm that can categorize attacks into high, medium, and low attacks and is accurate on DoS attacks. Snort accuracy testing functions to measure the packet size detected by snort using an attack application, then the number of packets caught can be categorized using clustering techniques. From the measurement results, the increase was 73.18%. The contribution of this research is a survey and analysis of anomalous packets contained in a network. It can identify the level of types of attacks and take preventive measures from these attacks.


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