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All Journal JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Infotekmesin Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Muhammad Nur Faiz, Muhammad Nur
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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Journal : Infotekmesin

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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.
Comparison Analysis of Cloning-Hashing Applications for Digital Evidence Security Faiz, Muhammad Nur
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1844

Abstract

The development of the Internet has resulted in an increasing variety of cyber crimes. Cybercrime is closely related to digital evidence, so cybercriminals tend to delete, hide, and format all collected data to eliminate traces of digital evidence. This digital evidence is very vital in proving at trial, so it is necessary to develop applications to secure digital evidence. This study aims to compare the results of cloning and hashing in securing digital evidence and evaluate the performance of a digital forensic application developed under the name Clon-Hash Application v1 compared to applications commonly used by investigators including Autopsy, FTK Imager, md5.exe in terms of its function, the result, CPU usage. The results of the research conducted show that the cloning process is perfectly successful, as evidenced by the hash value results which are the same as paid applications and there are even several other applications that have not been able to display the hash value. Hash values in the Clon-Hash v1 application also vary from MD5, SHA1, and SHA256 which do not exist in other applications. Applications developed are better in terms of function, results, and CPU usage.
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 Arif Wirawan Muhammad Jumari Jumari, Jumari Maharrani, Ratih Hafsarah Mukhlishah, Ulfatul Sari, Laura Supriyono, Abdul Rohman