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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Sinkron : Jurnal dan Penelitian Teknik Informatika REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat
Alam, Sahirul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Analyzing Image Malware with OSINTs after Steganography using Symmetric Key Algorithm Fauziyyah, Anni Karimatul; Adrian, Ronald; Alam, Sahirul
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12266

Abstract

Steganography is the practice of hiding a message or information within another file, such as an image (Singh & Singla, 2022). OSINT (Open Source Intelligence) involves using publicly available information for intelligence gathering purposes. In this research, the asymmetric key algorithm will be applied to the steganography method, using 10 images with different sizes and dimensions. Images tested for steganography are in tiff, gif, png, jpg, and bmp format. A combination of steganography and OSINT could involve analyzing and decoding images found on publicly available platforms, such as social media, to uncover hidden messages. On the other hand, steganography within OSINT can also be used to protect sensitive information from prying eyes. Overall, the combination of Symmetric Key Algorithm steganography and OSINT can be a powerful tool for both intelligence gathering and secure communication. Here in this work, malware is developed, and using that malware the victim’s machine is exploited. Later, an analysis is done via freely available OSINTs to find out which is the best OSINT that gives the best results. OSINTs have been very helpful in identifying whether the URLs and files are malicious or not. But how binding an image with the malware makes it difficult for OSINTs to identify they are malicious or not is being analyzed in this work. The analysis shows that the best OSINT is VirusTotal which has a greater number of engines that could detect the malware whereas others don’t have a variety of engines to detect the malware. Also, when it comes to malware afore binding it with an image is easier to detect whereas for an OSINT it was difficult to identify and detect the malware after binding with an image
Design and Implementation of a Machine Learning-Based Adaptive IDS on Raspberry Pi for Smart Home Network Security Adrian, Ronald; Mandasari, R. Deasy; Alam, Sahirul
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 22, No 2 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v22i2.33485

Abstract

The rapid growth of the Internet of Things (IoT) has accelerated the adoption of smart home technologies, offering convenience and automation in daily life. However, this interconnected environment increases the risk of cyber threats, making information security a pressing concern. To address this, the study presents the design and implementation of an adaptive Intrusion Detection System (IDS) based on machine learning, deployed on a Raspberry Pi platform as a low-cost, flexible, and energy-efficient solution for smart home security. Unlike traditional IDS approaches that rely on static, rule-based detection, the proposed system leverages adaptive learning algorithms to identify evolving attack patterns in real time. It integrates network traffic monitoring with carefully selected sensors and detection algorithms to improve responsiveness across various threat types from application-level exploits to network infrastructure attacks. System performance was evaluated through simulated attacks, including DDoS, brute force, and malware injection scenarios. Results show that the adaptive IDS significantly improves detection accuracy to 85%, surpassing the 65% accuracy achieved by conventional methods. The response time was also reduced from 5 seconds to just 2 seconds, demonstrating the system’s suitability for real-time threat mitigation in resource-constrained environments. The Raspberry Pi acts as the IDS host and a firewall enhancement tool, supporting custom iptables rules, whitelist-based access control, and integration with the Elastic Stack for real-time logging and visualization. The system also supports continuous learning by updating its detection models based on new traffic patterns, making it scalable and resilient to future threats. This research contributes to IoT cybersecurity by demonstrating that an adaptive, machine learning-based IDS can be effectively implemented on lightweight hardware without sacrificing performance. It offers a cost-effective and scalable solution to secure smart home networks against increasingly sophisticated cyberattacks.Keywords: Firewall, IDS, IoT, Raspberry, Smart Home
AIoT-Based Soil Moisture Monitoring System for Precision Agriculture and Energy Efficiency in Rural Smart Villages Mandasari, R Deasy; Adrian, Ronald; Kustiawan, Iwan; Alam, Sahirul; Lukman Hakim, Dadang; Wahyudin, Didin
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 6, No 3 (2025): Reka Elkomika
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v6i3.217-225

Abstract

Alamendah Village is a horticultural area where irrigation and energy use are still managed manually, leading to water inefficiency and unmonitored electricity consumption. This study developed an AIoT-based system using ESP32 microcontrollers and soil moisture sensors to support precision irrigation and energy monitoring. The system design included sensor calibration to convert ADC values into moisture percentages, threshold-based irrigation recommendations, and a placement formula to determine sensor deployment. Field testing in strawberry plots demonstrated that soil moisture often dropped below the 40% threshold, triggering timely irrigation alerts. The dashboard provided real-time data visualization and revealed peak electricity demand in the evening at the village hall. The results indicated that the system enhanced irrigation accuracy, provided a baseline for monitoring energy consumption, and increased community awareness of sustainable resource management. The project offers a replicable model for integrating smart agriculture and energy monitoring in rural areas.
Co-Authors Didin Wahyudin, Didin Fauziyyah, Anni Karimatul Iwan Kustiawan Lukman Hakim, Dadang Mandasari, R Deasy Mandasari, R. Deasy Ronald Adrian, Ronald