BadUSB attacks exploit the implicit trust that operating systems place in USB Human Interface Devices (HIDs), enabling a malicious peripheral to inject automated keystrokes and execute commands as if they originated from a legitimate user. This study develops a low-cost USB passthrough system that operates independently of host-side security software, using a Raspberry Pi 4 as an intermediary security layer between a keyboard and a host computer. The prototype combines VID/PID-based device identification, whitelist and blacklist controls, real-time keystroke-timing analysis, CAPTCHA-based human verification, input forwarding, and security-event logging. A Raspberry Pi Pico configured as a malicious HID was used in a preliminary, attack-driven functional evaluation comprising seven black-box test cases covering device enrolment, normal keyboard passthrough, resilience, malicious-keystroke detection, human verification, logging, and administrative functions. Of 18 predefined functional outcomes, 16 passed, one partially passed, and one failed. These outcomes demonstrate the functional feasibility of the prototype under the tested configuration but do not represent detection accuracy or general classification performance. The limited evaluation did not support the calculation of a confusion matrix, precision, recall, F1-score, or false-positive rate. The preliminary latency comparison indicated an additional average delay of 1.15 ms; however, the available experiment did not provide sufficient statistical evidence for broader performance interpretation. Broader repeated testing involving multiple users, devices, operating systems, attack patterns, and statistically rigorous latency measurements is required before operational deployment.
Muhammad Alif Nukman bin Nor Azman, N. M. Salleh, Siti Rahayu Selamat et al.· International journal of res...· 0 citations
Water quality monitoring is essential in aquaculture to ensure healthy fish growth and sustainable farming practices. In Malaysia, fish farmers commonly rely on manual methods to monitor key water quality parameters, including temperature and turbidity. However, these methods are labour-intensive, time-consuming, and prone to human error, resulting in delayed detection of water quality deterioration and inefficient data management. This study presents an Internet of Things (IoT)-based real-time water quality monitoring system to automate the monitoring process and improve aquaculture management. The system integrates an ESP32 microcontroller, a turbidity sensor, and a DS18B20 temperature sensor to continuously acquire water quality data. The collected data are transmitted wirelessly to the Blynk cloud platform for real-time monitoring, automatically recorded in Google Sheets, and visualized through Google Looker Studio to support historical data analysis. The developed prototype was evaluated through functionality testing to verify sensor connectivity, wireless communication, cloud synchronization, automated notifications, and data logging. The results demonstrate that the system successfully performs continuous data acquisition, real-time monitoring, cloud-based visualization, automated notifications, and historical data storage. The proposed system provides a practical and cost-effective solution for remote water quality monitoring in small-scale fish farming, supporting timely decision-making and more sustainable aquaculture management.
Mohamad Afif Md Gharif, N. M. Salleh, Haniza Nahar et al.· International journal of res...· 0 citations
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