Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

Authorisation inconsistency in IoT third‐party integration

Abstract Today's IoT platforms provide rich functionalities by integrating with popular third‐party services. Due to the complexity, it is critical to understand whether the IoT platforms have properly managed the authorisation in the cross‐cloud IoT environments. In this study, the authors report the first systematic study on authorisation management of IoT third‐party integration by: (1) presenting two attacks that leak control permissions of the IoT device in the integration of third‐party services; (2) conducting a measurement study over 19 real‐world IoT platforms and three major third‐party services. Results show that eight of the platforms are vulnerable to the threat. To educate IoT developers, the authors provide in‐depth discussion about existing design principles and propose secure design principles for IoT cross‐cloud control frameworks

RNA-Seq Profiling of Intact and Enucleated Oocyte SCNT Embryos Reveals the Role of Pig Oocyte Nucleus in Somatic Reprogramming - Fig 5

<p>Scatter plot of DEGs in (a) A vs. C; (b) B vs. D; (c) A vs. B; (d) C vs. D and GO functional classification of DEGs in (e) A vs. C and (f) B vs. D. Note: For A vs. C, A is the control group. Red points indicate genes up-regulated in C relative to A, green points represent genes down-regulated in C relative to A, and blue points represent genes that showed no differences or fold change below 2.</p

Summary of mapping results of four samples (mapping to reference genome).

<p>Summary of mapping results of four samples (mapping to reference genome).</p

The cell number of blastocyst generated from parthenogenesis,enucleated oocyte NT and intact oocyte NT.

<p>The cell number of blastocyst generated from parthenogenesis,enucleated oocyte NT and intact oocyte NT.</p

Top 10 unique genes in gene expression profiles of four samples.

<p>Top 10 unique genes in gene expression profiles of four samples.</p

Cluster image of DEG levels of transcription factor activity genes expressed in the nucleus.

<p>Each column represents one sample, and each row represents one gene. Red indicates up-regulation and green indicates down-regulation.</p

Cluster image of DEG levels of four samples.

<p>Each column represents an experimental sample, and each row represents a gene. Differences in expression are shown in different colors. Red indicates up-regulation and green represents down-regulation.</p

Primer sequences used in real-time RT-PCR.

<p>Primer sequences used in real-time RT-PCR.</p