scholarly journals Probe Request Based Device Identification Attack and Defense

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4620
Author(s):  
Xiaolin Gu ◽  
Wenjia Wu ◽  
Xiaodan Gu ◽  
Zhen Ling ◽  
Ming Yang ◽  
...  
Keyword(s):  
Defense Mechanism ◽  
Relevant Information ◽  
Security Risks ◽  
Unique Identifier ◽  
Device Identification ◽  
Novel Device ◽  
Identification Method ◽  
Attack And Defense ◽  
Probe Request ◽  
Open Nature

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

scholarly journals On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1515
Author(s):  
Dayu Shi ◽  
Xun Zhang ◽  
Lina Shi ◽  
Andrei Vladimirescu ◽  
Wojciech Mazurczyk ◽  
...  
Keyword(s):  
Theoretical Perspective ◽  
Visible Light Communication ◽  
Device Identification ◽  
Light Emitting ◽  
White Leds ◽  
Novel Device ◽  
Identification Method ◽  
New Radio ◽  
5G Network ◽  
Radio Signals

In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.

A Robust IoT Device Identification Method with Unknown Traffic Detection

2021 ◽  
pp. 190-202
Author(s):  
Xiao Hu ◽  
Hong Li ◽  
Zhiqiang Shi ◽  
Nan Yu ◽  
Hongsong Zhu ◽  
...  
Keyword(s):  
Device Identification ◽  
Identification Method ◽  
Traffic Detection

scholarly journals Clock Skew Based Computer Identification on Different Types of Area Networks

2019 ◽  
Vol 3 (1) ◽  
pp. 25
Author(s):  
Nola Verli Herlian ◽  
Komang Oka Saputra ◽  
I Gst A. Komang Diafari Djuni Hartawan
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Linear Regression Method ◽  
Clock Skew ◽  
Device Identification ◽  
Identification Method ◽  
Measurement Results ◽  
Different Types ◽  
True Time ◽  
Computer Identification

The increase of client devices along with the growth of internet access currently affects to security threats at the user's identity. Identifiers that commonly used today, such as SSID, IP address, MAC address, cookies, and session IDs have a weakness, which is easy to duplicate. Computer identification based on clock skew is an identification method that is not easily duplicated because it is based on the hardware characteristics of the device. Clock skew is the deviation of the clock to the true time which causes each clock to run at a slightly different speed. This study aims to determine the effect of network types to the clock skew stability as a reliable device identification method. This research was conducted on five client computers which running windows and linux operating systems. The measurement was conducted based on three different types of area networks, i.e., LAN, MAN, and WAN. The skew estimation was done using two linear methods i.e., linear programming and linear regression. The measurement results show that the most stable clock skew is found on the LAN measurement because it meets the threshold tolerance limit i.e., ±1 ppm. Skew estimation using linear programming method has better accuracy than linear regression method.

scholarly journals A Delay-Based Machine Learning Model for DMA Attack Mitigation

Cryptography ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 18
Author(s):  
Yutian Gui ◽  
Chaitanya Bhure ◽  
Marcus Hughes ◽  
Fareena Saqib
Keyword(s):  
Machine Learning ◽  
Direct Memory Access ◽  
Main Memory ◽  
Memory Access ◽  
Unique Identifier ◽  
Device Identification ◽  
Processing Power ◽  
Host Machine ◽  
Minimal Modification ◽  
Time Required

Direct Memory Access (DMA) is a state-of-the-art technique to optimize the speed of memory access and to efficiently use processing power during data transfers between the main system and a peripheral device. However, this advanced feature opens security vulnerabilities of access compromise and to manipulate the main memory of the victim host machine. The paper outlines a lightweight process that creates resilience against DMA attacks minimal modification to the configuration of the DMA protocol. The proposed scheme performs device identification of the trusted PCIe devices that have DMA capabilities and constructs a database of profiling time to authenticate the trusted devices before they can access the system. The results show that the proposed scheme generates a unique identifier for trusted devices and authenticates the devices. Furthermore, a machine learning–based real-time authentication scheme is proposed that enables runtime authentication and share the results of the time required for training and respective accuracy.

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