With the recent spread of Internet of Things (IoT) devices, the security issues for hardware devices have increased. When an IoT device runs an application program, the power consumption of the running application is combined with the power consumption of the device hardware itself, resulting in a complex power waveform. To detect anomalous application behaviors using the power waveforms, it is necessary to subtract the steady-state power waveform due to the device hardware from the measured power waveforms and extract only the application power waveform. In this paper, we propose a method for detecting anomalous IoT behaviors using generated power waveforms by introducing hyper-parameter tuning. The proposed method detects anomalous behaviors by generating a highly accurate steady-state power waveform and an application power waveform by adjusting the waveform period through hyper-parameter tuning, even if the measured power waveform includes large noises. Experimental evaluation demonstrates that we successfully detect anomalous behaviors from an AES encryption circuit containing a hardware Trojan on an FPGA device, while the existing state-of-the-art method cannot.

In this study, we present an autonomous driving sys-tem that utilizes a binarized autoencoder implemented on a Field Programmable Gate Array (FPGA). The binarized autoencoder compresses the image into optimal features in this system. The recurrent neural network then determines the following control based on the feature values extracted from the autoencoder and the rotation speed of the motor. We reduced the model size by binarizing the autoencoder because of the limited on-chip memory of the FPGA. We implemented the system on an Ultra96-V2, a board with a programmable logic and processing system. The robot employing our implemented system exhibits robust control by recognizing the entire road marking and road edge line as a feature and drives autonomously along the specified route.

In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize operation duration time and consumed energy extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar duration time and consumed energy but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shape-based distance (SBD) measure. The proposed method firstly obtains the entire power waveform of the target IoT device and extract several application power waveforms. After that, we give the invariances to them and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects the anomalous application behaviors, while the existing method fails to detect them.

This paper presents an autonomous driving system utilizing FPGA-based image processing. We develop a robot that our system is implemented on Ultra96-V2, a board with programmable logic and processing system. We use ROS, a middleware framework for developing robots, to manage the system such as controlling hardware devices, localization and determination of the direction to go. We implement a neural network to detect road markings on the road on a programmable logic on the board. The robot with our system implemented drives autonomously along the specified route on a miniature road, recognizing edge line and road markings.