Publications

2026

1. IMUWhisper: Keyword Inference from Ambient Speech Through Zero-Permission Inertial Sensors

   Caiyue Chen, Qi Li, and Keyang Yu

   In 2026 35th International Conference on Computer Communications and Networks (ICCCN), 2026
2. Inferring Device-Level Activity from VPN-Encrypted Smart-Home Traffic

   Ye Zhou, Anany Pratap Gupta, Keyang Yu, and 1 more author

   In 2026 35th International Conference on Computer Communications and Networks (ICCCN), 2026
3. SolarSys: Scalable Peer-to-Peer Energy Trading for Residential Virtual Power Plants

   Anany Pratap Gupta, Xiangyu Li, Keyang Yu, and 1 more author

   In Proceedings of the 13th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, , 2026

   Abs

   Distributed solar energy resources (DSERs) in residential power systems are rapidly increasing due to declining photovoltaic module prices. This increase has led to the development of virtual power plants (VPPs) that coordinate local generation to support reliable and efficient grid operation. However, many existing VPPs rely on centralized control, where a central operator aggregates household-level information and determines trading outcomes, preventing individual users from independently and concurrently trading surplus energy. As a result, researchers have explored decentralized coordination, where individual households trade surplus solar energy directly with others to improve utilization and fairness within residential networks. However, such approaches perform well only in small deployments and become inefficient at scale as the number of participating households increases. To address these limitations, we design SolarSys, a hierarchical coordination system for peer-to-peer energy trading in distributed solar and storage communities. The system is organized hierarchically to support decision making at both the household and cluster levels. Each household node measures its generation, demand, and battery state, and makes local energy management decisions using a policy trained with Multi-Agent Proximal Policy Optimization to improve local solar usage and maintain cluster-level energy balance. Across clusters, SolarSys leverages a mean-field multi-agent reinforcement learning approach that updates high-level decisions using aggregated cluster conditions. In addition, we deploy SolarSys on a Raspberry Pi and find that the learned policies can run efficiently on low-power edge hardware. We find that SolarSys reduces the energy drawn from the grid by 27.48 ± 0.42%, increases daytime solar utilization to 82.76 ± 5.11%, and improves fairness to 0.773 (Jain’s Index). The results show that SolarSys enables efficient, fair, and scalable peer-to-peer energy trading in large-scale virtual power plant deployments.

4. Fingerprinting Voice Commands of VPN-protected Smart Speakers

   Xiaoguang Guo, Keyang Yu, Qi Li, and 1 more author

   ACM Trans. Sen. Netw., Apr 2026

   Abs

   Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of 0.68 in a closed-world setting and infer voice command categories with an MCC of 0.84 in an open-world setting by eavesdropping VPN-encrypted network traffic data. This presents a significant risk to user privacy and security, as it suggests that external on-path attackers could still potentially intercept and decipher users’ voice commands despite the VPN encryption. We then further examine the sensitivity of voice speaker commands to VoiceAttack. We find that 134 voice speaker commands are highly vulnerable to VoiceAttack. We also present a defense approach—VoiceDefense, which could inject inject appropriate traffic “noise” into voice speaker traffic. And our evaluation results show that VoiceDefense could effectively mitigate VoiceAttack on Amazon Echo and Google Home.

5. SocialStego: A Steganography Tool for the Modern Era of Social Media

   Branden Palacio, and Keyang Yu

   In Proceedings of the Workshop on Attack Provenance, Reasoning, and Investigation for Security in the Monitored Environment (PRISM), Feb 2026

   Co-located with the Network and Distributed System Security Symposium (NDSS)

2025

1. Enabling Automatic Solar PV Array Identification using Big Satellite Imagery

   Qi Li, Keyang Yu, Carson Snow, and 1 more author

   ACM J. Comput. Sustain. Soc., May 2025

   Abs

   Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed solar PV arrays without any extra cost. In essence, SolarDetector+ first leverages multiple data augmentation techniques, including Cycle-Consistent Adversarial Networks, Latent Diffusion Models, and Generative Adversarial networks, to build a large rooftop satellite imagery dataset (RSID). Then, SolarDetector+ employs Mask R-convolutional neural networks algorithm to accurately identify rooftop solar PV arrays and learn the detailed installation information for each solar PV array simultaneously. We find that pre-trained SolarDetector+ yields an average Matthews correlation coefficient of 0.862 to detect solar PV arrays over RSID, which is ∼50% better than the most recent open source detection system—SolarFinder.

2. Enabling Automatic Solar PV Array Identification using Big Satellite Imagery

   Qi Li, Keyang Yu, Carson Snow, and 1 more author

   ACM J. Comput. Sustain. Soc., May 2025

   Abs

   Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed solar PV arrays without any extra cost. In essence, SolarDetector+ first leverages multiple data augmentation techniques, including Cycle-Consistent Adversarial Networks, Latent Diffusion Models, and Generative Adversarial networks, to build a large rooftop satellite imagery dataset (RSID). Then, SolarDetector+ employs Mask R-convolutional neural networks algorithm to accurately identify rooftop solar PV arrays and learn the detailed installation information for each solar PV array simultaneously. We find that pre-trained SolarDetector+ yields an average Matthews correlation coefficient of 0.862 to detect solar PV arrays over RSID, which is ∼50% better than the most recent open source detection system—SolarFinder.

3. Home Shield IoT Traffic Analyzer: A Comprehensive Analysis of Data Sharing Practices

   Dhananjai Bajpai, and Keyang Yu

   In Proceedings of the Workshop on Innovation in Metadata Privacy: Analysis and Construction Techniques (IMPACT), Feb 2025

   Co-located with the Network and Distributed System Security Symposium (NDSS)

2024

1. Safeguarding User-Centric Privacy in Smart Homes

   Keyang Yu, Qi Li, Dong Chen, and 1 more author

   ACM Trans. Internet Technol., Nov 2024

   Abs

   Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

2. VoiceAttack: Fingerprinting Voice Command on VPN-protected Smart Home Speakers

   Xiaoguang Guo, Keyang Yu, Qi Li, and 1 more author

   In Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Hangzhou, China, Nov 2024

   Abs

   Recently, there are growing security and privacy concerns regarding smart voice speakers, such as Amazon Alexa and Google Home. Extensive prior research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning (ML) and deep learning (DL)-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of 0.68 in a closed-world setting and infer voice command categories with an MCC of 0.84 in an open-world setting by eavesdropping VPN-encrypted network traffic rates. This presents a significant risk to user privacy and security, as it suggests that external on-path attackers could still potentially intercept and decipher users’ voice commands despite the VPN encryption. We then further examine the sensitivity of voice speaker commands to VoiceAttack. We find that 134 voice speaker commands are highly vulnerable to VoiceAttack and 3 commands are less sensitive. We also present a proof-of-concept defense approach—VoiceDefense, which could effectively mitigate TA attack accuracy on Amazon Echo and Google Home by  50%.

3. PACAS: A Privacy-Aware Smart Camera System

   Keyang Yu, and Dong Chen

   In 2024 IEEE Cloud Summit, Nov 2024

   Abs Bib PDF Website

   Smart cameras have been increasingly deployed in smart homes for remote monitoring and enhancing home security. However, extensive recent research has uncovered potential user privacy threats associated with popular commercial camera systems. Some manufacture design of these commercial camera systems usually requires smart camera users to relinquish their control of camera recorded data. For instance, these cameras often upload camera recordings to their cloud servers to enable advanced data analysis for camera app services. To facilitate enhanced camera services, the data may be further shared with on-path vendors, third parties of manufacturers, and cloud providers, potentially allowing them to access video footage or image captures without users’ awareness or meaningful consent. To address this problem, we design a new smart camera system-PACAS that enables people to regain the control of their data while still retaining access to regular camera services. We evaluate PACAS using multiple camera video footage traces and on multiple real-world camera prototypes. We show that PACAS can achieve the performance of 0.5 second living streaming latency at a frame rate of 30 fps and a resolution of 240x320 on average. PACAS offers compelling evidence that smart camera systems can deliver on-device modern capabilities comparable to those provided by current commercial systems, all while upholding user privacy.

   @inproceedings{10630953,
     author = {Yu, Keyang and Chen, Dong},
     booktitle = {2024 IEEE Cloud Summit},
     title = {PACAS: A Privacy-Aware Smart Camera System},
     year = {2024},
     volume = {},
     number = {},
     pages = {170-177},
     keywords = {Cloud computing;Privacy;Smart cameras;Prototypes;Smart homes;Streaming media;Recording;Internet of Things;User Privacy;Tiny ML},
     doi = {10.1109/Cloud-Summit61220.2024.00035},
   }

4. EWSN

   I Still See You: Why Existing IoT Traffic Reshaping Fails

   Su Wang, Keyang Yu, Qi Li, and 1 more author

   Nov 2024

   Abs Bib PDF Code Website

   The Internet traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and device manufacturers, and often shared with their third parties to maintain and enhance user services. Unfortunately, on-path adversaries could infer and fingerprint users’ sensitive privacy information such as occupancy and user activities by analyzing these network traffic traces. While there’s a growing body of literature on defending against this side-channel attack-malicious IoT traffic analytics (TA), there’s currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework-IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. In particular, we also design a novel image-based attack capable of inferring sensitive user information, even when users employ the most robust preventative measures in their smart homes. Researchers could leverage our new image-based attack to systematize and understand the existing literature on IoT traffic analysis attacks and preventing studies. Our results show that current defending approaches are not sufficient to protect IoT device user privacy. IoT devices are significantly vulnerable to our new image-based user privacy inference attacks, posing a grave threat to IoT device user privacy. We also highlight potential future improvements to enhance the defending approaches. ITEMTK’s flexibility allows other researchers for easy expansion by integrating new TA attack models and prevention methods to benchmark their future work.

   @misc{wang2024iyouexistingiot,
     title = {I Still See You: Why Existing IoT Traffic Reshaping Fails},
     author = {Wang, Su and Yu, Keyang and Li, Qi and Chen, Dong},
     year = {2024},
     eprint = {2406.10358},
     archiveprefix = {arXiv},
     primaryclass = {cs.CR},
     url = {https://arxiv.org/abs/2406.10358},
   }

2023

1. ICCCN

   PAROS: The Missing “Puzzle” in Smart Home Router Operating Systems

   Keyang Yu, and Dong Chen

   In 2023 32nd International Conference on Computer Communications and Networks (ICCCN), Nov 2023

   Abs Bib PDF Code Website

   The Internet of Things (IoT) devices have been increasingly deployed in smart homes for automation. Unfortunately, extensive recent research shows that external on-path adversaries can infer and fingerprint user sensitive in-home activities by analyzing IoT network traffic rates alone. Most recent traffic padding-based defending approaches cannot sufficiently protect user privacy with reasonable traffic overhead. In addition, these approaches typically assume the installation of additional hub hardware in smart homes to host their traffic padding-based defending approaches. To address these problems, we design a new open-source traffic reshaping system—privacy as a router operating system service (PAROS) that enables smart home users to significantly reduce private information leaked through IoT network traffic rates. PAROS does not assume the installation of any additional hardware device. We evaluate PAROS on open-source router Operating System (OS)—OpenWrt enabled virtual machine and also two real best-selling home routers. We find that PAROS can effectively prevent a wide range of state-of-the-art adversarial machine learning-based user in-home activity inference attacks, with near-zero system overhead increasing.

   @inproceedings{10230103,
     author = {Yu, Keyang and Chen, Dong},
     booktitle = {2023 32nd International Conference on Computer Communications and Networks (ICCCN)},
     title = {PAROS: The Missing “Puzzle” in Smart Home Router Operating Systems},
     year = {2023},
     volume = {},
     number = {},
     pages = {1-10},
     keywords = {Privacy;Automation;Operating systems;Smart homes;Telecommunication traffic;Fingerprint recognition;Hardware;Internet of Things;User Privacy;Machine Learning;Router OS;Data Analytics},
     doi = {10.1109/ICCCN58024.2023.10230103},
   }

2022

1. CNS

   TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference

   Qi Li, Keyang Yu, Dong Chen, and 2 more authors

   In 2022 IEEE Conference on Communications and Network Security (CNS), Nov 2022

   Abs Bib PDF Code Website

   People have been increasingly deploying the Internet of Things (IoT) devices to monitor and control their environments. Unfortunately, extensive recent research has shown that IoT devices are vulnerable to multiple adversarial attacks, which analyze their network traffic to reveal a wide range of sensitive private information about user in-home activities. Thus, smart home users recently have a keen interest in employing virtual private networks (VPN) to obscure their privacy information in their IoT network traffic. Our key insight is that VPN-encrypted IoT network traffic data is not anonymous, since this aggregate traffic data can still be disaggregated into individual IoT device traffic data. And this individual IoT device traffic may have an identifiable traffic signature that already embeds detailed user sensitive information. To explore the severity and extent of this privacy threat, we design a new factorial hidden Markov model (FHMM)-based smart home network traffic disaggregator-TrafficSpy that can accurately disaggregate VPN-encrypted whole-house IoT network traffic data into individual IoT device network traffic data. We evaluate TrafficSpy using VPN network traffic data from three smart homes. We find that TrafficSpy can disaggregate VPN traffic data into individual IoT device data accurately. We also show that the disaggregated traffic traces can be further attacked by smart and adaptive adversaries and thus reveal user sensitive information. TrafficSpy represents a serious privacy threat, but also a potentially useful tool that provides important contextual information for smart home monitoring and automation.

   @inproceedings{9947251,
     author = {Li, Qi and Yu, Keyang and Chen, Dong and Sha, Mo and Cheng, Long},
     booktitle = {2022 IEEE Conference on Communications and Network Security (CNS)},
     title = {TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference},
     year = {2022},
     volume = {},
     number = {},
     pages = {145-153},
     keywords = {Privacy;Data privacy;Hidden Markov models;Telecommunication traffic;Smart homes;Network security;Virtual private networks;Disaggregation;IoT Privacy;Smart Homes;Machine Learning;Deep Learning},
     doi = {10.1109/CNS56114.2022.9947251},
   }

2021

1. IGSC

   SolarDiagnostics: Automatic damage detection on rooftop solar photovoltaic arrays

   Qi Li, Keyang Yu, and Dong Chen

   Sustainable Computing: Informatics and Systems, Nov 2021

   Abs Bib PDF Code Website

   Homeowners are increasingly deploying rooftop solar photovoltaic (PV) arrays due to the rapid decline in solar module prices. However, homeowners may have to spend up to ∼$375 to diagnose their damaged rooftop solar PV system. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, recent approaches that leverage machine learning techniques have the limitation of distinguishing solar PV array damages from other solar degradation (e.g., shading, dust, snow). To address this problem, we design a new system—SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. In essence, SolarDiagnostics first leverages an K-Means algorithm to isolate rooftop objects to extract solar panel residing contours. Then, SolarDiagnostics employs a convolutional neural networks to accurately identify and characterize the damage on each solar panel residing contour. We evaluate SolarDiagnostics by building a lower cost prototype and using 60,000 damaged solar PV array images generated by deep convolutional generative adversarial networks. We find that SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews correlation coefficient (MCC) of 1.0. In addition, pre-trained SolarDiagnostics yields an MCC of 0.95, which is significantly better than other re-trained machine learning-based approaches and yields as the similar MCC as of re-trained SolarDiagnostics. We make the source code and datasets that we use to build and evaluate SolarDiagnostics publicly-available.

   @article{LI2021100595,
     title = {SolarDiagnostics: Automatic damage detection on rooftop solar photovoltaic arrays},
     journal = {Sustainable Computing: Informatics and Systems},
     volume = {32},
     pages = {100595},
     year = {2021},
     issn = {2210-5379},
     doi = {https://doi.org/10.1016/j.suscom.2021.100595},
     url = {https://www.sciencedirect.com/science/article/pii/S2210537921000834},
     author = {Li, Qi and Yu, Keyang and Chen, Dong},
     keywords = {Solar energy, Deep learning, Machine learning, Anomaly detection},
   }

2. IPSN

   PrivacyGuard: Enhancing Smart Home User Privacy

   Keyang Yu, Qi Li, Dong Chen, and 2 more authors

   In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located with CPS-IoT Week 2021), Nashville, TN, USA, Nov 2021

   Abs Bib PDF Video Code Website

   

   The Internet of Things (IoT) devices have been increasingly deployed in smart homes and smart buildings to monitor and control their environments. The Internet traffic data produced by these IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Unfortunately, extensive recent research has shown that on-path adversaries can infer and fingerprint users’ sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic traces. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable traffic overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design.To address this problem, we design a new low-cost, open-source user "tunable" defense system—PrivacyGuard that enables users to significantly reduce the private information leaked through IoT device network traffic data, while still permitting sophisticated data analytics or control that is necessary in smart home management. In essence, our approach employs intelligent deep convolutional generative adversarial networks (DCGANs)-based IoT device traffic signature learning, long short-term memory (LSTM)-based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from 5 smart homes. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art machine learning-based and deep learning-based occupancy and other 9 user in-home activity detection attacks. We release the source code and datasets of PrivacyGuard to IoT research community.

   @inproceedings{10.1145/3412382.3458257,
     author = {Yu, Keyang and Li, Qi and Chen, Dong and Rahman, Mohammad and Wang, Shiqiang},
     title = {PrivacyGuard: Enhancing Smart Home User Privacy},
     year = {2021},
     isbn = {9781450380980},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3412382.3458257},
     doi = {10.1145/3412382.3458257},
     booktitle = {Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located with CPS-IoT Week 2021)},
     pages = {62–76},
     numpages = {15},
     keywords = {Smart Home, Machine Learning, IoT privacy, Deep Learning},
     location = {Nashville, TN, USA},
     series = {IPSN '21},
   }

2020

1. Automatic Damage Detection on Rooftop Solar Photovoltaic Arrays

   Qi Li, Keyang Yu, and Dong Chen

   In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Virtual Event, Japan, Nov 2020

   Abs Bib PDF Website

   Homeowners may spend up to  375 to diagnose their damaged rooftop solar PV systems. Thus, recently, there is a rising interest to inspect potential damage on solar PV arrays automatically and passively. Unfortunately, current approaches may not reliably distinguish solar PV array damage from other degradation (e.g., shading, dust, snow). To address this issue, we design a new system—SolarDiagnostics that can automatically detect and profile damages on rooftop solar PV arrays using their rooftop images with a lower cost. We evaluate SolarDiagnostics by building a lower cost ( 35) prototype and using 60,000 damaged solar PV array images. We find that pre-trained SolarDiagnostics is able to detect damaged solar PV arrays with a Matthews Correlation Coefficient of 0.95.

   @inproceedings{10.1145/3408308.3431130,
     author = {Li, Qi and Yu, Keyang and Chen, Dong},
     title = {Automatic Damage Detection on Rooftop Solar Photovoltaic Arrays},
     year = {2020},
     isbn = {9781450380614},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3408308.3431130},
     doi = {10.1145/3408308.3431130},
     booktitle = {Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
     pages = {332–333},
     numpages = {2},
     keywords = {Solar Energy, Machine Learning, Image Processing, Deep Learning},
     location = {Virtual Event, Japan},
     series = {BuildSys '20},
   }

2. SmartAttack: Open-source Attack Models for Enabling Security Research in Smart Homes

   Keyang Yu, and Dong Chen

   In 2020 11th International Green and Sustainable Computing Workshops (IGSC), Nov 2020

   Abs Bib PDF Code Website

   The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home and smart building owners are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. The recent intensive research has shown that network traffic traces of IoT devices have significant cybersecurity and privacy issues. These security and privacy defending techniques have enabled sophisticated approaches to ensure security and preserve user privacy. However, due to the fact that different approaches are evaluated using their own datasets, their own developed security and privacy attack models, and their own evaluating metrics, it is being significantly difficult to make a fair and comprehensive comparisons among different IoT security strengthening and user privacy preserving research to better understand IoT security issues and end-user benefits. To address this problem, we present a deep learning-based adversarial attack model framework-SmartAttack, which enables a set of sophisticated adversarial attack models that can be leveraged by researchers and industrial users from IoT security community to better evaluate their work. In essence, we leverage the most widely used unsupervised machine learning and deep learning models to design and implement these attack models. SmartAttack also provides user options to select the detailed configuration for each attack model, such as kernel, dataset splitting, cross-validation states, and evaluating metrics. We also evaluate the performance of SmartAttack using two different datasets. In addition, we made the source codes and the related datasets of SmartAttack publicly-available on our research website such that researchers can use our SmartAttack to benchmark their security strengthening and privacy-preserving approaches.

   @inproceedings{9290797,
     author = {Yu, Keyang and Chen, Dong},
     booktitle = {2020 11th International Green and Sustainable Computing Workshops (IGSC)},
     title = {SmartAttack: Open-source Attack Models for Enabling Security Research in Smart Homes},
     year = {2020},
     volume = {},
     number = {},
     pages = {1-8},
     keywords = {Security;Smart homes;Measurement;Data models;Data privacy;Training;Privacy;Deep Learning;IoT security;Adversarial Machine Learning;Attack Models;User Privacy},
     doi = {10.1109/IGSC51522.2020.9290797},
   }