EAVESDROPPING-DRIVEN PROFILING ATTACKS ON ENCRYPTED WIFI NETWORKS: UNVEILING VULNERABILITIES IN IOT DEVICE SECURITY
Date
2024-08-02
Authors
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Publisher
University of Central Florida
Abstract
This dissertation investigates the privacy implications of WiFi communication in
Internet-of-Things (IoT) environments, focusing on the threat posed by out-of-network observers.
Recent research has shown that in-network observers can glean information about IoT devices,
user identities, and activities. However, the potential for information inference by out-of-network
observers, who do not have WiFi network access, has not been thoroughly examined. The first
study provides a detailed summary dataset, utilizing Random Forest for data summary classifica-
tion. This study highlights the significant privacy threat to WiFi networks and IoT applications
from out-of-network observers.
Building on this investigation, the second study extends the research by utilizing a new set of
time series monitored WiFi data frames and advanced machine learning algorithms, specifically
xGboost, for Time Series classification. This extension achieved high accuracy of up to 94% in
identifying IoT devices and their working status, demonstrating faster IoT device profiling while
maintaining classification accuracy. Furthermore, the study underscores the ease with which out-
side intruders can harm IoT devices without joining a WiFi network, launching attacks quickly and
leaving no detectable footprints.
Additionally, the dissertation presents a comprehensive survey of recent advancements in machine-
learning-driven encrypted traffic analysis and classification. Given the challenges posed by encryp-
tion for traditional packet and traffic inspection, understanding and classifying encrypted traffic are
crucial. The survey provides insights into utilizing machine learning for encrypted network traffic
analysis and classification, reviewing state-of-the-art techniques and methodologies. This survey
serves as a valuable resource for network administrators, cybersecurity professionals, and policy
enforcement entities, offering insights into current practices and future directions in encrypted
traffic analysis and classification.
Description
Keywords
IoT Device Fingerprinting, Encrypted Traffic Analysis, WiFi Eavesdropping, Summary-Data Analysis, Time-Series Analysis, Machine Learning, Wireless Network Security, Privacy Issues, Network Traffic Classification, IoT Privacy, Packet Length Analysis, XGBoost, SMOTE, Random Forest, Access Point Monitoring