Article Per Year (5 Year)
p-Index From 2020 - 2025
1.756
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnas Nasional Teknologi dan Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO Jurnal Ilmu Komputer Dan Informatika Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Yaya Sudarya Triana
Universitas Mercu Buana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
User behavior analysis for insider attack detection using a combination of memory prediction model and recursive feature elimination algorithm Triana, Yaya Sudarya; Osman, Mohd Azam; Stiawan, Deris; Budiarto, Rahmat
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1793-1804

Abstract

Existing defense tools against the insider attacks are rare, not in real time fashion and suffer from low detection accuracy as the attacks become more sophisticated. Thus, a detection tool with online learning ability and better accuracy is required urgently. This study proposes an insider attack detection model by leveraging entity behavior analysis technique based on a memory prediction model combined with the recursive feature elimination (RFE) feature selection algorithm. The memory-prediction model provides ability to perform online learning, while the RFE algorithm is deployed to reduce data dimensionality. Dataset for the experiment was created from a real network with 150 active users, and mixed with attacks data from publicly available dataset. The dataset is simulated on a testbed network environment consisting of a server configured to run 4 virtual servers and other two computers as traffic generator and detection tool. The experimental results show 94.01% of detection accuracy, 95.64% of precision, 99.28% of sensitivity, and 96.08% of F1-score. The proposed model is able to perform on-the-fly learning to address evolving nature of the attacks. Combining memory prediction models with the RFE for user behavior analysis is a promising approach, and achieving high accuracy is definitely a positive outcome.
Co-Authors Adji Pratomo Anggito Susilo Anindyka Elras Saputri Anjar Rochana Budi Susatyo Deris Stiawan Dwi Prasetyo Firmansyah, Aditya Fitri Anggri Maharani Fitri Herawati Fryda Farizha Adrianti Helvywidya Utari Hendri Gunawan Huda, Darul Fitahul Jumaryadi, Yuwan Kaburuan, Emil Robert Krisna Pangestu L. Muhamad Safiih Lilla Chairunnisa Mohd Azam Osman Muhammad Fermi Pasha Osman, Mohd Azam Palinggi, Owen Baan Permana, Muhamad Bagas Priyono, Kus Andi Rahmad, Khozaeni Bin Rahmat Budiarto Rahmat Budiarto Raziv Herman Diputra Retnowardhani, Astari Siti Maesaroh Sumantri, Bagas Syahidin, Muhammad Farhan