Cyberattacks on IoT devices are accelerating at an unprecedented rate, largely driven by IoT malware activities. The IoT malware attacks are typically composed of three stages: intrusion, infection, and execution. It is essential to instantaneously detect malware at the early stage of intrusion on IoT devices before massive attacks. To build an efficient and scalable instruction detection system, a multi-client single-server privacy-preserved neural network model is proposed. Further, it is important to train the neural network model on the fly to cope with the fast-evolving malware variations. For online training and detection, we propose Lynx-Net, a distributed deep learning online training model for inferring malicious activities by analyzing power side-channel signals. To protect user private data and model parameters, and reduce prediction latency, we implement a novel privacy-preserved protocol via secret sharing and packed hybrid homomorphic encryption. Through theoretical analysis and empirical experiments, we demonstrate that Lynx-Net can detect infection activities of different IoT malware with high accuracy. Our extensive experiments demonstrate not only stable training but also a 1.13 to 2.67 times speedup compared to the state-of-the-art training model and an 8 to 500 times improvement in Lynx-Net’s inference prediction latency compared to the state-of-the-art inference model.

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have utilized power side-channel in-formation to identify this intrusion behavior on IoT devices but still lack accurate models in real-time for ubiquitous botnet detection. We propose the first online intrusion detection system called DeepAuditor for multiple IoT devices via power auditing. To de-velop the real-time system, we propose a lightweight power auditing device called Power Auditor. We also design a distributed CNN classifier for online inference in a laboratory setting. In order to protect data leakage and reduce networking redundancy, we then propose a privacy-preserved inference protocol via Packed Homo-morphic Encryption and a sliding window protocol in our system. The classification accuracy and processing time are measured, and the proposed classifier outperforms a baseline classifier, especially against unseen patterns. We also demonstrate that the distributed CNN design is secure against any distributed components. Over-all, the measurements are shown to the feasibility of our real-time distributed system for intrusion detection on IoT devices.

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Thus, a new approach that monitors these ini-tial intrusions is needed. Power side-channel information can be used because it does not require any modification in programming languages or operating systems on diverse IoT devices. We propose a distributed power side-channel auditing system for online IoT intrusion detection. To meet the real-time requirement, we develop a lightweight power auditing device. We then design a distributed CNN classifier for online inference in a laboratory setting. Two distributed protocols are also proposed in order to protect data leakage and reduce networking redundancy. In this work, we demonstrate the feasibility of our real-time distributed system for intrusion detection on IoT devices.

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' private data by exploiting the smart bulb's capability of infrared emission.

Tremor is a common symptom among Parkinson's disease (PD) patients at all stages. To measure tremor, we utilized IMU sensing data from the wrists while PD patients were drawing. With 30 patients' IMU sensing data obtained from standard tremor rating scale activities, we conducted data analysis for identifying any tremor episodes and extracting tremor amplitude. In this demo, we demonstrate that our preliminary analysis and results show the potential of measuring kinetic tremors effectively using these methods.

Aggressive tennis shots with high ball speed are the key factor in winning a tennis match. Today's tennis players are increasingly focused on improving ball speed. As a result, in recent tennis tournaments, records of tennis shot speeds are broken again and again. The traditional method for calculating the tennis ball speed uses multiple high-speed cameras and computer vision technology. This method is very expensive and hard to set up. Another way to calculate the tennis ball speed is to use motion sensors, which are lower cost and easier to set up. In this paper, we propose an approach for tennis ball speed estimation based on a racket-mounted motion sensor. We divide the tennis strokes into three categories: serve, groundstroke, and volley. For a serve, a regression model is proposed to estimate the ball speed. For a groundstroke or volley, two models are proposed: a regression model and a physical model. We use the physical model to estimate the ball speed for advanced players and the regression model for beginner players. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work.