2019/07/26 by Rahmadi Trimananda, Trimananda, Rahmadi, Janus Varmarken +5 · 8 citations
Computer Science · #Advanced Malware Detection Techniques #Airfield traffic pattern #Computer network #Computer science #Cryptography and Security (cs.CR) #Data Structures and Algorithms (cs.DS) #Encryption #FOS: Computer and information sciences #Home automation #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Mobile device #Network Security and Intrusion Detection #Network packet #Networking and Internet Architecture (cs.NI) #Operating system #Ranging #Real-time computing #Robustness (evolution) #Telecommunications #cs.CR #cs.DS #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.1907.11797
published in arXiv (Cornell University) (Cornell University) · This is the technical report for the paper titled Packet-Level Signatures for Smart Home Devices published at the Network and Distributed System Security (NDSS) Symposium 2020
openalex publication_date 2019/07/26 · arxiv created 2020/02/10 · arxiv updated 2020/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by homeautomation systems.