An IoT Device Identification Method based on Semi-supervised Learning

2020 ◽  
Author(s):  
Linna Fan ◽  
Shize Zhang ◽  
Yichao Wu ◽  
Zhiliang Wang ◽  
Chenxin Duan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Device Identification ◽  
Identification Method

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 Supervised Learning-Based Fast, Stealthy, and Active NAT Device Identification Using Port Response Patterns

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1444
Author(s):  
Seungwoon Lee ◽  
Si Jung Kim ◽  
Jungtae Lee ◽  
Byeong-hee Roh
Keyword(s):  
Supervised Learning ◽  
Response Patterns ◽  
Learning Approaches ◽  
Security Issue ◽  
Ip Address ◽  
Device Identification ◽  
Address Translation ◽  
Proposed Model ◽  
Network Operations ◽  
Network Administrators

Although network address translation (NAT) provides various advantages, it may cause potential threats to network operations. For network administrators to operate networks effectively and securely, it may be necessary to verify whether an assigned IP address is using NAT or not. In this paper, we propose a supervised learning-based active NAT device (NATD) identification using port response patterns. The proposed model utilizes the asymmetric port response patterns between NATD and non-NATD. In addition, to reduce the time and to solve the security issue that supervised learning approaches exhibit, we propose a fast and stealthy NATD identification method. The proposed method can perform the identification remotely, unlike conventional methods that should operate in the same network as the targets. The experimental results demonstrate that the proposed method is effective, exhibiting a F1 score of over 90%. With the efficient features of the proposed methods, we recommend some practical use cases that can contribute to managing networks securely and effectively.

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 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%.

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