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All Journal Jurnal Transformatika JIEET (Journal of Information Engineering and Educational Technology) INOVTEK Polbeng - Seri Informatika Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Joined Journal (Journal of Informatics Education Transformasi Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bhinneka Julia Jurnal
Eko Supriyadi
Universitas An Nuur
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Other

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AI-BAHSI: Metode Hibrid Artificial Intelligence-Behavioral Analysis dan Hybrid Security Intelligence untuk Deteksi dan Mitigasi Ancaman Real-time pada Wireless Access Point Rheimanda Devin Emmanuel; Ani Anggraini; Agus Condro Wibowo; Kartika Imam Santoso; Eko Supriyadi
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.30

Abstract

Wireless access point (AP) security faces significant challenges with the emergence of sophisticated attacks such as SSID Confusion (CVE-2023-52424), KRACK attacks, and advanced persistent threats. This research develops a hybrid AI-BAHSI (Artificial Intelligence-Behavioral Analysis and Hybrid Security Intelligence) method that integrates deep learning, ensemble machine learning, and federated learning for real-time threat detection and mitigation on wireless access points. The proposed method combines Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for pattern recognition, Random Forest-Support Vector Machine ensemble for threat classification, and federated learning for privacy-preserving security intelligence. Evaluation was conducted on a synthetic dataset that includes 15,000 normal traffic samples and 8,500 attack samples of various types. The results show that AI-BAHSI achieves a detection accuracy of 98.7%, a precision of 97.3%, a recall of 98.1%, and an F1-score of 97.7% with a false positive rate of only 1.2%. This method successfully detected zero-day attacks with a 94.6% confidence level and was able to automatically mitigate them in an average of 0.8 seconds. The main contribution of this research is the development of an adaptive security framework that can learn from new attack patterns in real time while preserving privacy through a federated learning architecture.
Co-Authors Achmad Basuki Achmad Rizki Ramadhani Addien Anaba Adri Tiyono Agus Condro Wibowo Agus Susilo Nugroho Agus Susilo Nugroho Agus Susilo Nugroho Alfahmi, Furqon Andri Triyono Ani Anggraini Arindhya Ratna Hayuningtyas Arum, Dhika Malita Puspita Basedt, Ngabdul Deden Pratyaten Defriyantoro, Gatot Dhika Malita Dhika Malita Puspita Edi Widodo, Edi Emmanuel, Rheimanda Devin Erika Dwi Saputra Farokhah, Mirna Vaul Kartika Imam Santoso Kartika Imam Santoso Mika Agustiana Muhammad Muzammil Muhammad Muzammil muzammil, muhammad Nadia Cahaya Islami Nugroho, Agus Susilo Puput Rismawati Putra, Ngurah Made Darma Radityo, Samuel Rahmawan Bagus Trianto Rheimanda Devin Emmanuel Rheimanda Devin Emmanuel Riniasih, Wahyu Riyanto Sigit Sari, Gigih Kenanga susilo nugroho, agus Triyono, Andri Ulfa, Fatma Nurmala WAHYUDI Zahra Fatma Saniya