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All Journal International Journal of Engineering Continuity International Journal of Science Education and Cultural Studies Jurnal Komputer dan Elektro Sains
Budi Wibowo
Department of Informatics Engineering, Institut Teknologi Budi Utomo, Jakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Other

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An Energy-Efficient ESP32 IoT System for Real-Time Detection of WiFi Deatuhentication Attacks Faizal Riza; Dannie Febrianto Hendrakusuma; Budi Wibowo
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.433

Abstract

WiFi deauthentication attacks pose a serious threat to users on public WiFi networks by forcibly disconnecting them from access points, often as a prelude to man-in-the-middle exploits. To counter this threat, we developed an energy-efficient ESP32-based IoT system that monitors WiFi traffic in real time to identify deauthentication attack patterns. The device captures deauthentication frames in monitor mode and immediately notifies users through on-device audible/visual alarms (buzzer, LED/OLED) and digital channels (MQTT dashboard and Telegram bot). Experimental evaluation under moderate and high attack scenarios demonstrated robust performance: detection accuracy remained above 97% even under heavy attack traffic (97.8% at peak intensity). Furthermore, the system’s duty-cycled design limited average power consumption to ~79 mA (~30% lower than continuous monitoring) and achieved a rapid notification latency of ~270 ms, confirming real-time responsiveness. By combining physical indicators with online alerts, the system effectively warns users and improves public digital security literacy by making cyber threats immediately visible and understandable. Overall, these results establish the proposed system as a low-power, real-time attack detection solution that enhances WiFi network security and user awareness.
Deep Learning in Wazuh Intrusion Detection System to Identify Advanced Persistent Threat (APT) Attacks Budi Wibowo; Aji Nurrohman; Luqman Hafiz
International Journal of Science Education and Cultural Studies Vol. 4 No. 1 (2025): IJSECS
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijsecs.v4i1.311

Abstract

Advanced Persistent Threats (APTs) pose a significant challenge in modern cybersecurity by leveraging persistent and sophisticated methods to compromise organizations. These threats employ advanced techniques such as encrypted communication, polymorphic malware, and log tampering, to evade detection, exfiltrate sensitive data, and disrupt critical infrastructure. Such characteristics often render conventional security measures ineffective in mitigating or preventing such attacks. This study adopted an experimental approach to assess the application of Wazuh, an advanced open-source security platform, in countering APT attacks. By simulating attack scenarios and analyzing real-time logs from diverse sources, Wazuh demonstrated strong intrusion detection capabilities, identifying attack patterns such as brute force attempts and unauthorized directory access. The findings underscore Wazuh’s effectiveness in enhancing organizational resilience by enabling rapid detection and response to suspicious activities. This research highlights how integrated log analysis can address the stealthy nature of APTs. Future studies should explore the integration of machine learning with platforms like Wazuh to further enhance automated and predictive threat detection capabilities, thereby strengthening defenses against evolving strategies of APTs.
Development of an IoT-Based Smart Waste Bin with Automated Operation and Capacity Monitoring Pria Intiadi; Gunawan Sihaloho; Al Fauzan Dito Prasetyo; Budi Wibowo
International Journal of Science Education and Cultural Studies Vol. 4 No. 2 (2025): ijsecs
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijsecs.v4i2.379

Abstract

In many public facilities, waste bins are still monitored through routine manual checks, which often results in delayed collection when the bin reaches its capacity. This situation is commonly found in campus park areas, where waste overflow reduces cleanliness and user comfort. This research aims to design and evaluate an IoT-based smart waste bin system that integrates automated lid operation and real-time capacity monitoring to improve waste management efficiency in public spaces. The system uses an ESP32 microcontroller together with an ultrasonic sensor to estimate the waste level and a Passive Infrared (PIR) sensor to detect human presence near the bin. An OLED display is included to show local system status, while remote monitoring and notifications are handled through the Blynk Console platform. The methodology involves system design, algorithm development, and simulation-based testing using the Wokwi platform. During operation, the bin lid opens when motion is detected and closes automatically after a short period. The waste level is observed continuously, and a notification is sent when the predefined capacity threshold is reached. Simulation results demonstrate an average accuracy of 98.8% for capacity detection with an absolute error of 1.2%. The system successfully performed automated lid operations, real-time status display on OLED, RGB LED status indication, and timely notifications via Blynk Console. These findings indicate that the proposed IoT-based smart waste bin can significantly enhance waste management operations in public areas by enabling proactive collection scheduling and reducing overflow incidents, thereby contributing to improved environmental hygiene and operational efficiency.
Risk Analysis of Bruteforce Attacks on Webserver with Telegram Notifications Budi Wibowo; Luqman Hafiz
Jurnal Komputer dan Elektro Sains Vol. 3 No. 1 (2025): komets
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/komets.v3i1.305

Abstract

In today's digital era, server security is a top priority for many organizations. Intrusion Detection Systems (IDS) such as Fail2ban, have proven effective in protecting servers from threats by monitoring logs and blocking suspicious IP addresses. This paper discusses the implementation of Fail2ban integrated with Telegram notifications, how it works, testing, and results showing improvements in detecting and responding to attacks. Server ssh brute force attacks pose considerable risks to web servers and have potentially severe consequences. Implementing strong preventive measures, continuous monitoring, and leveraging Telegram notifications for real-time alerts significantly improved the organization’s security posture. These combined efforts ensure robust and responsive detection of brute force attacks. Fail2ban was able to quickly discover the IP address from which the attacker performed the brute force attack and took preventive action by blocking the attacker's Ip for 3 failed login attempts within a specified time limit of 3600 s.
Co-Authors Aji Nurrohman Al Fauzan Dito Prasetyo Dannie Febrianto Hendrakusuma Faizal Riza Gunawan Sihaloho Luqman Hafiz Pria Intiadi