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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JUITA : Jurnal Informatika WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Tekno Kompak Jurnal Pengabdian Literasi Digital Indonesia
Dandy Pramana Hostiadi
Institut Teknologi dan Bisnis STIKOM Bali
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Journal : Lontar Komputer: Jurnal Ilmiah Teknologi Informasi

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The Optimization of the ARP Poisoning Attack Detection Model Using a Similar Approach Based on NetFlow Analysis Yohanes Priyo Atmojo; Dandy Pramana Hostiadi; I Made Darma Susila; Made Liandana; Gede Angga Pradipta; Putu Desiana Wulaning Ayu
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 2 (2023): Vol. 14, No. 2 August 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i02.p05

Abstract

Information security and threats are a concern in the cyber era. Attacks can be malicious activities. One of them is known as ARP poisoning attack activity, which attacks by falsifying a computer's identity through illegal access to retrieve confidential information in a target computer. Besides, it has also caused service deadlocks in the network. Previous studies have been introduced for the ARP Attack Detection model using rule-based and mining-based. Still, they cannot show optimal detection performance and obtain high false positive results. This paper proposed a detection model for ARP poisoning attacks using a similarity measurement approach adopting cosine similarity. The goal is to obtain measurements of host activities similar to ARP poisoning attacks. The experiment results showed that the model got an accuracy of 97.25%, recall of 96.43%, and precision of 81% with a similarity threshold value of 0.488. Comparison results with previous studies showed higher detection accuracy than previous studies and some classification methods. It shows that the model can improve intrusion detection performance and facilitate network administrators to analyze ARP poisoning attacks in computer networks.
Co-Authors Abdul Wahab Dadang Hermawan Gede Angga Pradipta Gede Angga Pradipta I Made Darma Susila Ida Bagus Suradarma Luh Putu Wiwien Widhyastuti Made Liandana, Made Muhamad Rihamzah Muhammad Riza Hilmi Ni Luh Putri Srinadi, Ni Luh Ni Nyoman Utami Januhari, Ni Nyoman Putu Desiana Wulaning Ayu Putu Desiana Wulaning Ayu Putu Sumardika Eka Putra Roy Rudolf Huizen Yohanes Priyo Atmojo