JURNAL INFOTEL

Enhancing IoT Security: Optimizing PUF Responses through Pre-Processing Techniques

2025-06-02T05:49:25+08:00

In this paper, we propose and detail the implementation of pre-processing techniquesâ€”specifically truncation and uniformizationâ€”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.

Autism Face Detection System using Single Shot Detector and ResNet50

2025-06-21T03:59:39+08:00

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.

English Analysis of Optical Communications System HFC and FTTH Using Optisystem Software (a Case Study at PT. Link)

2025-06-23T07:46:48+08:00

This research present the Analysis of the application of Optisystem software technology for FTTH and HFC shows that both have the capability for the WDM systems, to support a maximum transmission distance of 20 kilometers and can serve up to 32 subscribers. Key factors such as signal strength, Q factor, and bit error rate (BER) were observed and analyzed discreetly. It was found that FTTH has an average Q factor of 13.49 and HFC has an average Q factor of 7.475. The difference is about 44.59%, which indicates that FTTH has an advantage in terms of signal quality. However, based on the simulation results as well as the field measurements, Since the BER value does not exceed the maximum limit of 10^-9 and the Q-factor value exceeds the minimum limit of 6, it can be stated that both technologies are reliable for efficient and high-quality communication services.