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All Journal Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Informasi Galuh Journal of Mechatronics and Artificial Intelligence
Arif Wirawan Muhammad
Fakultas Informatika, Universitas Telkom Purwokerto
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Social Sciences

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Journal : Infotekmesin

 1 
Pengembangan Perangkat Lunak Untuk Deteksi DDoS Berbasis Neural Network Arif Wirawan Muhammad; Muhammad Nur Faiz; Ummi Athiyah
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1544

Abstract

System security issues are a vital factor that needs to be considered in the operation of systems and networks, which will later be used for disaster mitigation and preventing attacks on the network. Distributed Denial of Services (DDoS) is a form of attack carried out by individuals or groups to damage data through servers or malware in the form of flooding packets, therefore it can paralyze the network system used. Network security is a factor that must be maintained and considered in an information system. DDoS can take the form of Ping of Death, flood, Remote control attack, User Data Protocol (UDP) flood, and Smurf Attack. This study aims to develop software to detect DDoS attacks based on network traffic logs. The software has been tested and run according to the neural network algorithm. This software was developed with an interface that makes it easier for users to detect the source IP whether the IP is carrying out a DDoS attack or normal.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Sari, Laura; Faiz, Muhammad Nur; Muhammad, Arif Wirawan
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Faiz, Muhammad Nur; Muhammad, Arif Wirawan; Sari, Laura
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Achmad Zaki Yamani Ahmad Azhari Alameka, Faza Ali Diko Putra, Muhammad Alika, Shintia Dwi Amalia, Hasna Shafa Arsita Pinandita Atika Ratna Dewi Azhari, Ahmad Bita Parga Zen Busthomi, Iqbal Cik Feresa Mohd Foozy Davito Rasendriya Rizqullah Putra Dwi Januarita Ardianing Kusuma Feresa Mohd Foozy, Cik Firda Millennianita Foozy, Cik Feresa Mohd Imam Riadi Kamaruddin Malik bin Mohammed Laura Sari Maharrani, Ratih Hafsarah Malik, Kamaruddin Muhammad Afrizal Amrustian Muhammad Nur Faiz Muhammad Nur Faiz, Muhammad Nur Muhammad Quthb Habiburrahman Nofrizaldi Oktavia Jazilatus Sa’adah Oman Somantri Pangestu, Happy Gery Ratri Ayunita Kinasih Risa Eri Susanti Rusydi Umar Santi Purwaningrum Sarah Astiti S.Kom., M.MT Sari, Laura Sinaga, Rifaldo Yohannes Sukma Aji Sunardi Sunardi Supriyono, Abdul Rohman Tuti Rohayati Ulya, Fadilla Zundina Ummi Athiyah Wahyu Adi Prabowo Widi Widayat Yudha Saintika Yunita Kisworini, Rianti