Communication Traffic Characteristics Reveal an IoT Devices Identity
CoRR(2024)
摘要
Internet of Things (IoT) is one of the technological advancements of the
twenty-first century which can improve living standards. However, it also
imposes new types of security challenges, including device authentication,
traffic types classification, and malicious traffic identification, in the
network domain. Traditionally, internet protocol (IP) and media access control
(MAC) addresses are utilized for identifying network-connected devices in a
network, whilst these addressing schemes are prone to be compromised, including
spoofing attacks and MAC randomization. Therefore, device identification using
only explicit identifiers is a challenging task. Accurate device identification
plays a key role in securing a network. In this paper, a supervised machine
learning-based device fingerprinting (DFP) model has been proposed for
identifying network-connected IoT devices using only communication traffic
characteristics (or implicit identifiers). A single transmission control
protocol/internet protocol (TCP/IP) packet header features have been utilized
for generating unique fingerprints, with the fingerprints represented as a
vector of 22 features. Experimental results have shown that the proposed DFP
method achieves over 98
dataset with 22 smart-home IoT devices. This signifies that the proposed
approach is invaluable to network operators in making their networks more
secure.
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