WiFi-based IoT Devices Profiling Attack based on Eavesdropping of Encrypted WiFi Traffic

Alyami, Mnassar; Alharbi, Ibrahim; Zou, Cliff C.; Solihin, Yan; Ackerman, Karl

doi:10.1109/ccnc49033.2022.9700674

Cited by 14 publications

(12 citation statements)

References 17 publications

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“…Even though the RF is a sufficient model in regression [30] and multi-classification applications [39], it is not commonly used for image classifications because images have a large number of pixels, resulting in high-dimensional feature spaces. In addition, image processing is computationally expensive and time-consuming during training.…”

Section: Discussionmentioning

confidence: 99%

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Alkhowaiter

Kholidy

Alyami

et al. 2023

Sensors

Self Cite

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Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding led us to develop a hypothesis that most classical machine learning models, such as random forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and, at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on the CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems.

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Section: Discussionmentioning

confidence: 99%

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Alkhowaiter

Kholidy

Alyami

et al. 2023

Sensors

Self Cite

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“…Additionally, its effectiveness and performance were assessed. Alyami et al 37 showed that fingerprinting devices of IoT systems are possible without joining the WiFi network of IoT devices. And also they discussed potential defenses to mitigate the attack.…”

Section: Background Conceptsmentioning

confidence: 99%

“…But still, data will be eavesdropped in its encrypted form. We can modify the WiFi network to secure our network 37 Search wireless signal attack: If the WiFi network is not properly encrypted, then the devices searching them might be attacked by hackers.…”

Section: Background Conceptsmentioning

confidence: 99%

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities

Patel,

Kakkar

et al. 2023

Security and Privacy

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With the advancement of innovative technologies, the Internet of Things (IoT) has become quite popular in various applications such as smart homes, smart cities, agriculture, healthcare, and so forth. However, the heterogeneity of IoT protocols can pose security challenges to wireless networks. Thus, we have presented a systematic review of IoT technologies such as ZigBee, Radio Frequency Identification (RFID), Bluetooth, WiFi, Ethernet, AR, embedded subscriber identification modules (E‐SIM), and smart sensors, along with their communication protocols, which are part of the IoT‐layered architecture. We have bifurcated the IoT‐layered architecture into the perception, network, and application layers. Furthermore, we have presented IoT security attacks associated with the perception, network, and application layers of the IoT‐layered architecture. Moreover, we have discussed several IoT security solutions, open issues, and research challenges dedicated to the security of IoT networks. IoT security solutions can provide secure and efficient data communication over wireless networks.

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“…Analysis of network traffic can reveal private information, for example, Alyami et al [5] studied a privacy attack in which the profiling is performed using IoT devices' network traffic monitored from out-of-network. With regards to voice conversation, the analysis can be divided into active (i.e., probing) attack and passive (i.e., eavesdropping).…”

Section: B Traffic Analysismentioning

confidence: 99%

Side-Channel VoIP Profiling Attack against Customer Service Automated Phone System

Laurens

Christianto

Caulkins

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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In many VoIP systems, Voice Activity Detection (VAD) is often used on VoIP traffic to suppress packets of silence in order to reduce the bandwidth consumption of phone calls. Unfortunately, although VoIP traffic is fully encrypted and secured, traffic analysis of this suppression can reveal identifying information about calls made to customer service automated phone systems. Because different customer service phone systems have distinct, but fixed (pre-recorded) automated voice messages sent to customers, VAD silence suppression used in VoIP will enable an eavesdropper to profile and identify these automated voice messages. In this paper, we will use a popular enterprise VoIP system (Cisco CallManager), running the default Session Initiation Protocol (SIP) protocol, to demonstrate that an attacker can reliably use the silence suppression to profile calls to such VoIP systems. Our real-world experiments demonstrate that this side-channel profiling attack can be used to accurately identify not only what customer service phone number a customer calls, but also what following options are subsequently chosen by the caller in the phone conversation.

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WiFi-based IoT Devices Profiling Attack based on Eavesdropping of Encrypted WiFi Traffic

Cited by 14 publications

References 17 publications

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities

Side-Channel VoIP Profiling Attack against Customer Service Automated Phone System

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