DETEKSI SERANGAN FLOODING PADA JARINGAN SMART HOME IPv6 MENGGUNAKAN METODE DECISION TREE

SAPUTRA, DHANI and Stiawan, Deris and Hermansyah, Adi (2025) DETEKSI SERANGAN FLOODING PADA JARINGAN SMART HOME IPv6 MENGGUNAKAN METODE DECISION TREE. Undergraduate thesis, Sriwijaya University.

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Abstract

The ICMPv6 "Packet TOO BIG" flooding attack presents a significant threat to IPv6 network security, especially when leveraging vulnerabilities in the EUI-64-based automatic address configuration and the Privacy Extension feature. In this type of attack, a single fixed source is used to send a high volume of malicious ICMPv6 packets to multiple destination addresses that share a similar address structure, exploiting both the deterministic nature of the EUI-64 format and weaknesses in packet handling. This can result in excessive network load and reduced performance. To address this, the study employs a simulated dataset to detect such attacks using the Decision Tree algorithm. The model is trained on labeled traffic data consisting of two categories: Benign and Flood. Evaluation results demonstrate an overall accuracy of 92.38%, with the Benign class achieving a precision of 100% and recall of 84.74%, while the Flood class attains a precision of 86.77% and recall of 100%. The F1-Score reflects balanced detection performance, with 91.70% for the Benign class and 92.91% for the Flood class.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ICMPv6 Flooding, IPv6, Packet TOO BIG, Decision Tree, EUI-64 Vulnerability, Privacy Extension, Machine Learning
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Dhani Saputra
Date Deposited:	28 Aug 2025 07:26
Last Modified:	28 Aug 2025 07:26
URI:	http://repository.unsri.ac.id/id/eprint/183431

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