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All Journal JURNAL TEKNOLOGI LINGKUNGAN agriTECH Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal on Computing (Indo-JC) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI IJAIT (International Journal of Applied Information Technology) International Journal of Advances in Data and Information Systems eProceedings of Engineering Jurnal Rekayasa elektrika Jurnal Sistem Informasi Jurnal INFOTEL LOGIC: Jurnal Penelitian Informatika
Parman Sukarno
Universitas Telkom
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences

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Enhancing IoT Security: Optimizing PUF Responses through Pre-Processing Techniques Parman Sukarno; Fachrul Reiza Medina
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1236

Abstract

In this paper, we propose and detail the implementation of pre-processing techniques—specifically truncation and uniformization—to enhance the performance of authentication processes utilizing Physical Unclonable Functions (PUFs) within the context of the Internet of Things (IoT). Traditional authentication methods are often critiqued for their reliance on static secret storage, presenting inherent security risks. Physical Unclonable Function (PUF) technology addresses this concern by dynamically generating keys, akin to a device's "biometric" signature, thereby offering a more secure alternative. However, despite the dynamic nature of PUF-generated secret keys, vulnerabilities to specific attacks persist. Prior research has not focused on optimizing the secret key generated by PUFs, resulting in a lack of additional security layers and maintaining the susceptibility to PUF-targeted attacks at a constant level. This study introduces a PUF-based IoT device framework that optimizes PUF responses, aiming to significantly improve the security performance of the system. This enhancement is evaluated through metrics such as decidability, the confusion matrix, and randomness value, presenting a comprehensive approach to reinforcing system security. The optimization of Physical Unclonable Function (PUF) responses, through methods such as truncation or bit uniforming, plays a critical role in enhancing the security of IoT devices. Our findings indicate that bit uniforming notably improves system security, evidenced by a significant increase in the decidability value from 0.73 (unoptimized) to 1.37. This improvement is further reflected in the confusion matrix values, with False Rejection Rate (FRR), False Acceptance Rate (FAR), True Rejection Rate (TRR), and True Acceptance Rate (TAR) showing marked improvements from 18.02%, 4.93%, 95.06%, and 81.97% in the unoptimized state to 3.04%, 0.98%, 99.02%, and 96.96%, respectively, post-optimization. The proposed pre-processing techniques show its effectiveness in the PUF authentication systems, as superior results are obtained.
A Heterogeaneous Dataset–Driven Ensemble Learning Framework for Malicious URL Detection Sukarno, Parman; Ngah, Syahrulanuar
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

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Abstract

Modern cyberattacks are increasingly associated with phishing campaign, malware distribution, and website defacement, which are often delivered through malicious Uniform Resource Locator (URL) originating from diverse source. This paper examine malicious URL detection using an ensemble learning framework evaluated on large scale heterogeneous dataset composed of URL aggregated from multiple public threat intelligence source. The dataset include benign, phishing, malware, and defacement URL, thereby reflecting real world variability in attack pattern and data distribution. Three ensemble based classifier, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB), are evaluated with respect to detection accuracy and computational efficiency. In addition to classification performance, this study present a detailed analysis of training and detection time in order to identify most suitable model for practical deployment. Experimental results indicate that the DT model achieves a training time of 4.14 seconds with macro and weighted accuracies of 94.11% and 91.71%, respectively, and a per category detection time of 0.2162 seconds. The RF model attains macro and weighted accuracies of 93.64% and 90.94%, with training and detection times of 9.73 seconds and 0.2420 seconds, respectively. Although the GB model exhibits the longest training time of 45.38 seconds, it achieves the fastest per category detection time of 0.2151 seconds. Despite its comparatively lower overall accuracy of 92.48% for macro averaging and 89.42s% for weighted averaging, the rapid inference capability of GB makes it a strong candidate for real time malicious URL detection in heterogeneous cybersecurity environments.
Co-Authors Adrian Gusti Nurcahyo Aji Gautama Putrada Amir Hasanudin Fauzi Asep Mulyana AULIA ARIF WARDANA Azimi, Indra Bayu Erfianto Chaniago, Nero Dania M.P.M Dita Oktaria Doan Perdana Erwid Mushofa Jadied Fachrul Reiza Medina Hariandi Maulid Hilal Hudan Nuha Husneni Mukhtar Isa Mulia Insan Muhammad Al Makky Muhammad Danil Muis Muhammad Danil Muis Muhammad Rifki Fauzan Ngah, Syahrulanuar Novian Anggis Suwastika Nuha, Hilal H Pahlevi , Rizka Reza Prayitno, Muhammad Ramadhan Putri, Marsha Chikita Intania Rahmat Yasirandi Rendratama, Muhammad Thariq Sunu Ridha Muldina Negara Rio Guntur Utomo Shyffa Ilmallia Noer Fhadillah Sri Rezeki