With recent advances in communication technologies and Internet of Things (IoT) infrastructures, home automation (HA) systems have emerged as a new promising paradigm that provides convenient smart-home services to users. However, there exist various security risks during the deployment and application of HA systems, which pose severe security threats to users. On the one hand, traditional IoT security threats (e.g., device intrusion, protocol vulnerabilities, and so on) are inherent to HA systems. On the other hand, as the core of HA systems, the Trigger-Action Programming (TAP) model organizes cloud platforms, local hubs, and smart devices through user-customized rules, but the complex interactions involved bring new challenges to the security of HA systems. These two kinds of security issues have attracted widespread attention from both academia and industry, and explorations on both attack and defense have been made. However, there is not yet a survey that provides a summary of the overall HA systems’ security research. In this paper, we conduct a comprehensive survey of the state-of-the-art literature on HA system security from aspects of attack and defense. We first give a brief introduction to the HA system architecture and present a general workflow of HA systems. Then, we review and classify the relevant attacks based on the HA architecture, with an explicit analysis of vulnerabilities exploited by these attacks. We further elaborate on the security requirements of HA systems and provide detailed descriptions and comparisons of existing defenses methods. Finally, we conclude with a thorough discussion of open issues for future research.

In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues. In this paper, we strive to address these shortcomings from three aspects: (1) We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts forgery evidence. (2) We devise three functional modules to handle the multi-stream and multi-scale features in a collaborative learning scheme. (3) Confronted with the challenge of obtaining forgery annotations, we propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations. Empirically, our method demonstrates significantly improved robustness and generalizability, outperforming previous methods on six benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge preview dataset from 0.797 to 0.835 and video-level AUC on CelebDF_v1 dataset from 0.811 to 0.847. Our implementation is available at https://github.com/sccsok/Locate-and-Verify.

More and more people turn to online voice communication with loudspeaker-equipped devices due to its convenience. To prevent speech leakage, soundproof rooms are often adopted. This paper presents mmPhone, a novel acoustic eavesdropping system that recovers loudspeaker speech protected by soundproof environments. The key idea is that properties of piezoelectric films in mmWave band can change with sound pressure due to the piezoelectric effect. If the property changes are acquired by an adversary (i.e., characterizing the piezoelectric effect with mmWaves), speech leakage can happen. More importantly, the piezoelectric film can work without a power supply. Base on this, we proposed a methodology using mmWaves to sense the film and decoding the speech from mmWaves, which turns the film into a passive "microphone". To recover intelligible speech, we further develop an enhancement scheme based on a denoising neural network, multi-channel augmentation, and speech synthesis, to compensate for the propagation and penetration loss of mmWaves. We perform extensive experiments to evaluate mmPhone and conduct digit recognition with over 93% accuracy. The results indicate mmPhone can recover high-quality and intelligible speech from a distance over 5m and is resilient to incident angles of sound waves (within 55 degrees) and different types of loudspeakers.

Background: Chronic liver disease (CLD) is an important cause of morbidity and mortality and can lead to hepatic fibrosis. This study was conducted to evaluate the diagnostic value of real-time shear wave elastography (SWE) in the assessment of hepatic fibrosis.Methods: A systematic search of databases was performed for publications on SWE during the period between 2010 and 2017. The identified studies were analyzed using Meta-disc 1.4 software to integrate and analyze the data.Results: Eleven studies comprising 1560 patients were included for analysis. The pooled sensitivity, specificity and diagnostic odds ratio were 0.85 (95% CI: 0.82–0.87), 0.79 (95% CI: 0.76–0.82) and 30.81 (95% CI: 16.55–57.34), respectively for patients with a Metavir-score of ≥ F2; 0.87 (95% CI: 0.84–0.91), 0.84 (95% CI: 0.82–0.87), 41.45 (95% CI:18.25–94.45), respectively for patients with ≥ F3; 0.88(95% CI: 0.83–0.91), 0.91 (95% CI: 0.89–0.92), 67.18 (95% CI:30.02–150.31), respectively for patients with ≥ F4. The areas under the receiver operating characteristic curve of the three groups were 0.9147, 0.9223 and 0.9520, respectively.Conclusions: Our work demonstrates that SWE is highly accurate for detecting and staging hepatic fibrosis.

Federated Learning (FL) is a promising distributed learning approach that enables multiple clients to collaboratively train a shared global model. However, recent studies show that FL is vulnerable to various poisoning attacks, which can degrade the performance of global models or introduce backdoors into them. In this paper, we first conduct a comprehensive study on prior FL attacks and detection methods. The results show that all existing detection methods are only effective against limited and specific attacks. Most detection methods suffer from high false positives, which lead to significant performance degradation, especially in not independent and identically distributed (non-IID) settings. To address these issues, we propose FLTracer, the first FL attack provenance framework to accurately detect various attacks and trace the attack time, objective, type, and poisoned location of updates. Different from existing methodologies that rely solely on cross-client anomaly detection, we propose a Kalman filter-based cross-round detection to identify adversaries by seeking the behavior changes before and after the attack. Thus, this makes it resilient to data heterogeneity and is effective even in non-IID settings. To further improve the accuracy of our detection method, we employ four novel features and capture their anomalies with the joint decisions. Extensive evaluations show that FLTracer achieves an average true positive rate of over 96.88% at an average false positive rate of less than 2.67%, significantly outperforming SOTA detection methods.