Light Auditor: Power Measurement Can Tell Private Data Leakage Through IoT Covert Channels

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users\u27 private data by exploiting the smart bulb\u27s capability of infrared emission. In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process the data and transform them into two-dimensional images through Continous Wavelet Transformation (CWT). Next, we design a two-dimensional convolutional neural network (2D-CNN) model to identify the CWT images generated by malicious behavior. Our experiment results show that the proposed design is efficient in identifying infrared-based anomalies: 1) With much fewer parameters than transfer-learning classifiers, it achieves an accuracy of 88% in identifying the attacks, including unseen patterns. The results are similarly accurate as the sophisticated transfer-learning CNNs, such as AlexNet and GoogLeNet; 2) We validate that our system can classify the CWT images in real time

Power Profiling Smart Home Devices

In recent years, the growing market for smart home devices has raised concerns about user privacy and security. Previous works have utilized power auditing measures to infer activity of IoT devices to mitigate security and privacy threats. In this thesis, we explore the potential of extracting information from the power consumption traces of smart home devices. We present a framework that collects smart home devices’ power traces with current sensors and preprocesses them for effective inference. We collect an extensive dataset of duration \u3e 2h from 6 devices including smart speakers, smart camera and smart display. We perform different classification tasks including device identification and action classification and present accuracy and confusion matrices for each tasks. Our analysis reveals that from devices’ running power traces, we can accurately identify the type of smart device being used with 93% accuracy and subsequently infer user behavior with on average 92% accuracy

Development of fully dry-connection for prefabricated hollow section CA-RPC pier

To facilitate accelerated bridge construction, a dry-connection was proposed for the prefabricated hollow section coarse aggregate reactive powder concrete (CA-RPC) pier. An enlarged part was designed at the bottom of the pier to tolerate 24 high-strength prestressed reinforcement (HSPR), which were anchored in the cap. By apply the prestress in the HSPR, the prefabricated pier could be fixed on the cap and transfer axial load, shear force and bending moment. To explore the performance of the novel dry-connection, a full-size specimen underwent a pseudo-static test, and specimen delivered satisfactory stiffness, strength and ductility, which was comparable to the in-site casting pier. The failure mode of the specimen and the strain development of the HSPR verify the reliability of the dry-connection. An analytical model was proposed to describe the performance of dry-connected pier. Finally, the design suggestion was derived to guarantee the ductility of the dry-connected hollow CA-RPC pier