The identification of device brands and models plays a pivotal role in the
realm of multimedia forensic applications. This paper presents a framework
capable of identifying devices using audio, visual content, or a fusion of
them. The fusion of visual and audio content occurs later by applying two
fundamental fusion rules: the product and the sum. The device identification
problem is tackled as a classification one by leveraging Convolutional Neural
Networks. Experimental evaluation illustrates that the proposed framework
exhibits promising classification performance when independently using audio or
visual content. Furthermore, although the fusion results don't consistently
surpass both individual modalities, they demonstrate promising potential for
enhancing classification performance. Future research could refine the fusion
process to improve classification performance in both modalities consistently.
Finally, a statistical significance test is performed for a more in-depth study
of the classification results.

提出了一个用于通过音频、视觉内容或两者的融合来识别设备的框架，使用了卷积神经网络进行设备识别问题的分类，并通过实验证明，在独立使用音频或视觉内容时，该框架表现出有希望的分类性能。此外，融合结果虽然并不始终超过单个模态，但展示了增强分类性能的潜力。未来的研究可以进一步改进融合过程，以在两种模式下实现持续提升的分类性能。最后，进行了统计显著性测试，对分类结果进行了深入研究。

利用视频中的音频和视觉内容进行相机模型识别

Camera Model Identification Using Audio and Visual Content from Videos

Identifying IoT devices is crucial for network monitoring, security
enforcement, and inventory tracking. However, most existing identification
methods rely on deep packet inspection, which raises privacy concerns and adds
computational complexity. More importantly, existing works overlook the impact
of wireless channel dynamics on the accuracy of layer-2 features, thereby
limiting their effectiveness in real-world scenarios. In this work, we define
and use the latency of specific probe-response packet exchanges, referred to as
"device latency," as the main feature for device identification. Additionally,
we reveal the critical impact of wireless channel dynamics on the accuracy of
device identification based on device latency. Specifically, this work
introduces "accumulation score" as a novel approach to capturing fine-grained
channel dynamics and their impact on device latency when training machine
learning models. We implement the proposed methods and measure the accuracy and
overhead of device identification in real-world scenarios. The results confirm
that by incorporating the accumulation score for balanced data collection and
training machine learning algorithms, we achieve an F1 score of over 97% for
device identification, even amidst wireless channel dynamics, a significant
improvement over the 75% F1 score achieved by disregarding the impact of
channel dynamics on data collection and device latency.

通过累积分数为数据收集和机器学习算法训练引入的方法，在考虑到无线信道的动态影响时，成功实现了超过 97% 的设备识别的 F1 分数，相较于忽略信道动态对数据收集和设备延迟的影响时仅实现 75% 的 F1 分数，实现了显著改进。

利用机器学习在动态无线环境中进行准确的物联网设备识别

Leveraging Machine Learning for Accurate IoT Device Identification in  Dynamic Wireless Contexts

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% in classifying individual IoT devices using the UNSW
dataset with 22 smart-home IoT devices. This signifies that the proposed
approach is invaluable to network operators in making their networks more
secure.

使用基于监督机器学习的设备指纹模型，仅利用通信流量特征（或隐含标识符）来识别网络连接的物联网设备，达到了对 22 个智能家居物联网设备进行分类的 98% 准确率，从而提高网络安全性。