Enhancing Intrusion Detection Systems with Adaptive Learning Techniques

Naufal Zahir Rizqullah, Julius Alekhine, Dwika Lovitasari Yonia, Raden Mokhamad Racel Purnomo, Ary Mazharuddin Shiddiqi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The importance of adaptive and deep learning in cybersecurity aims to advocate for various techniques to combat the constantly evolving cyber threats effectively. Those techniques utilize diverse attack types and network traffic data to mirror the real-world security environment accurately. This study proposes an intrusion detection technique using adaptive random forest (ARF) to enhance its performance. The ARF uses an ensemble learning method designed to handle data streams or changing data over time, thereby allowing the model to adapt and evolve. The experiment results indicate an increase in the accuracy of the ARF compared to its native Random Forest (RF) model, with accuracy rates of 97% and 99%, respectively. This improvement underscores ARF's effectiveness in enhancing the performance of intrusion detection systems.

Original languageEnglish
Title of host publicationInternational Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350350524
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024 - Virtual, Online, Indonesia
Duration: 22 Feb 202423 Feb 2024

Publication series

NameInternational Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

Conference

Conference2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
Country/TerritoryIndonesia
CityVirtual, Online
Period22/02/2423/02/24

Keywords

  • Cybersecurity
  • adaptive randomforest
  • intrusion detection

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