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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of ICT Research and Applications Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Jurnal Respirologi Indonesia JINAV: Journal of Information and Visualization Devotion: Journal of Research and Community Service Makara Journal of Technology International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Dodi Sudiana
Department Of Electrical Engineering, Faculty Of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics

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Design of Realtime Web Application Firewall on Deep Learning-Based to Improve Web Application Security Rofif Zainul Muttaqin; Dodi Sudiana
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8346

Abstract

Web applications are widely used nowadays, but comprises several vulnerabilities that are often used by attacker to exploit the system. There is web application firewall (WAF) that could mitigate these problem. WAF generally works based on pre-established rules. However, the weakness of this system is the evolving nature of attacks, and configuring rules on WAF requires in-depth knowledge related to existing applications. Artificial intelligence technology, both machine learning (ML) and deep learning (DL), shows good potential in recognizing types of attacks. In this research, a Real-time DL-based WAF was built to enhance security in web applications. Various ML and DL models were tested to perform the task of web attack detection, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Based on the test results, the CNN-LSTM model achieved the highest performance, namely an accuracy of 98.61%, precision of 99%, recall of 98.08%, and f1-score of 98.54%. From the testing results with a web vulnerability scanner, the performance of the DL-based WAF is not inferior to ModSecurity WAF, which is used as a comparison. From the analysis results, it can be concluded that the implementation of DL-based WAF can improve the security of web applications.
Co-Authors Agus Santoso Tamsir Ali, Shadiq Anna Rozaliyani Arba’i Yusuf Dadang Gunawan Dyatmika, Haris S. Haris Suka Dyatmika Harry Sudibyo Imamul Muttakin Iman Firmansyah Indah Kusumawati Susanti Isbat, Faiz Khairul Marendra Eko Budiono Marendra Eko Budiono, Marendra Eko Marpuah Jamilah, Erni Mia Rizkinia Mukarto Siswoyo Nur Afny Catur Andryani Nursahidin, Nursahidin Prastowo, Stefanus Lugas Rachmat Maulana Rachmat Maulana Rahmat Arief Rahmat Arief, Rahmat Rani Sauriasari, Rani Rofif Zainul Muttaqin Shadiq Ali Wahyu Widada Warsito P. Taruno Zaenal Arifin